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This reflection article presents insights on conducting fieldwork during and after COVID-19 from a diverse collection of political scientists-from department heads to graduate students based at public and private universities in the United States and abroad Many of them contributed to a newly published volume, Stories from the Field: A Guide to Navigating Fieldwork in Political Science (Krause and Szekely 2020) As in the book, these contributors draw on their years of experience in the field to identify the unique ethical and logistical challenges posed by COVID-19 and offer suggestions for how to adjust and continue research in the face of the pandemic's disruptions Key themes include how contingency planning must now be a central part of our research designs;how cyberspace has increasingly become "the field"for the time being;and how scholars can build lasting, mutually beneficial partnerships with "field citizens,"now and in the future © The Author(s), 2021 Published by Cambridge University Press on behalf of the American Political Science Association

Hatred of terrorists is an obstacle to the implementation of effective counterterrorism policies-it invites indiscriminate retaliation, whereas many of the greatest successes in counterterrorism have come from understanding terrorists' personal and political motivations. Drawing from psychological research, traditional prejudice reduction strategies are generally not well suited to the task of reducing hatred of terrorists. Instead, in 2 studies, we explored education's potential ability to reduce extreme negative attitudes toward terrorists. Study 1 compared students in a college course on terrorism (treatment) with wait-listed students, measuring prosocial attitudes toward a hypothetical terrorist. Initially, all students reported extremely negative attitudes; however, at the end of the semester, treatment students' attitudes were significantly improved. Study 2 replicated the effect within a sample of treatment and control classes drawn from universities across the United States. The present work was part of an ongoing research project, focusing on foreign policy and the perceived threat of terrorism; thus classes did not explicitly aim to reduce prejudice, making the effect of treatment somewhat surprising. One possibility is that learning about terrorists "crowds out" the initial pejorative associations-that is, the label terrorism may ultimately call more information to mind, diluting its initial negative associative links. Alternatively, students may learn to challenge how the label terrorist is being applied. In either case, learning about terrorism can decrease the extreme negative reactions it evokes, which is desirable if one wishes to implement effective counterterrorism policies. (PsycINFO Database Record

The currently discussed increase in inequality within many countries arise not only from higher poverty rates but also from the growing population at the upper end of the distribution and the resulting tensions between rich and poor. The rise of wealth in several dimensions was coming back as an issue for quality-of-life applications. Although achieving comfortable living conditions is a common goal, the shape of the distributions may have direct impacts on well-being, even for the broad majority of the population that does not fall into the high-wealth category. This is because most people below the wealth line experience at least some degree of prosperity, which I refer to here as partial wealth.

There are many applications for CAE simulation models in the automotive industry. Simulations are usually less expensive and faster than conducting the real experiment, but still in some applications the simulation run times exceed several days. If the CAE simulation models could be replaced by metamodels, then a drastic runtime reduction would be achieved. divis intelligent solutions has developed reliable metamodels of CAE simulation models within a FAT research project.

Item does not contain fulltext PURPOSE: Surgical costs are under scrutiny and surgeons are being held increasingly responsible for cost containment. In some instances, implants are the largest component of total procedure cost, yet previous studies reveal that surgeons' knowledge of implant prices is poor. Our study aims to (1) understand drivers behind implant selection and (2) assess whether educating surgeons about implant costs affects implant selection. METHODS: We surveyed 226 orthopedic surgeons across 6 continents. The survey presented 8 clinical cases of upper extremity fractures with history, radiographs, and implant options. Surgeons were randomized to receive either a version with each implant's average selling price ("price-aware" group), or a version without prices ("price-naive" group). Surgeons selected a surgical implant and ranked factors affecting implant choice. Descriptive statistics and univariate, multivariable, and subgroup analyses were performed. RESULTS: For cases offering implants within the same class (eg, volar locking plates), price-awareness reduced implant cost by 9% to 11%. When offered different models of distal radius volar locking plates, 25% of price-naive surgeons selected the most expensive plate compared with only 7% of price-aware surgeons. For cases offering different classes of implants (eg, plate vs external fixator), there was no difference in implant choice between price-aware and price-naive surgeons. Familiarity with the implant was the most common reason for choosing an implant in both groups (35% vs 46%). Price-aware surgeons were more likely to rank cost as a factor (29% vs 21%). CONCLUSIONS: Price awareness significantly influences surgeons' choice of a specific model within the same implant class. Merely including prices with a list of implants leads surgeons to select less expensive implants. This implies that an untapped opportunity exists to reduce surgical expenditures simply by enhancing surgeons' cost awareness. TYPE OF STUDY/LEVEL OF EVIDENCE: Economic/Decision Analyses I.

Offering a thorough assessment of the recent developments in the economic literature on happiness and quality of life, this Handbook astutely considers both methods of estimation and policy application. The expert contributors critically present in-depth research on a wide range of topics including culture and media, inequality, and the relational and emotional side of human life. Accessible and far-reaching, it will prove an invaluable resource for students and scholars of welfare and economics.

Environmental modeling frameworks support scientific model development by providing model developers with domain specific software libraries which are used to aid model implementation. This paper presents an investigation on the framework invasiveness of environmental modeling frameworks. Invasiveness, similar to object-oriented coupling, is defined as the quantity of dependencies between model code and a modeling framework. We investigated relationships between invasiveness and the quality of modeling code, and also the utility of using a lightweight framework design approach in an environmental modeling framework. Five metrics to measure framework invasiveness were proposed and applied to measure dependencies between model and framework code of several implementations of Thornthwaite and the Precipitation-Runoff Modeling System (PRMS), two well-known hydrological models. Framework invasiveness measures were compared with existing common software metrics including size (lines of code), cyclomatic complexity, and object-oriented coupling. Models with lower framework invasiveness tended to be smaller, less complex, and have less coupling. In addition, the lightweight framework implementations of the Thornthwaite and PRMS models were less invasive than the traditional framework model implementations. Our results show that model implementations with higher degrees of framework invasiveness also had structural characteristics which previously have been shown to predict poor maintainability, a non-functional code quality attribute of concern. We conclude that using a framework with a lightweight framework design shows promise in helping to improve the quality of model code and that the lightweight framework design approach merits further attention by environmental modeling framework developers.

Das weiterentwickelte und optimierte Design eines auf Bulk-Silizium-Technologie basierenden 3-D-Kraftsensors wird beschrieben. In vorangegangenen Untersuchungen konnte gezeigt werden, dass sich dieser Kraftsensor zum Einsatz in der taktilen Wegmesstechnik als Antastsensor eignet. Das Sensorelement besteht aus einer mit mikrotechnischen Herstellungsverfahren gefertigten Silizium-Bossmembran. In diesem Bericht wird ein neues Design und eine neue Auswertungsstrategie für diesen Sensortyp beschrieben.

In this paper the J2000, a new modelling system for the distributed simulation of the water balance in large river basins, will be introduced and described briefly. The introduction comprises an overview of the model's purpose and concept, the simulated hydrological processes, the distribution concept for the spatial subdivision of the catchment as well as the methods for regionalisation of climate data from point sources. As an example for the model's capabilities simulation results of a large catchment in Germany are shown. The model description is complemented by the presentation of results of two land use change scenarios to prove the model's ability to be used for the spatial quantification of the impact of land use changes on the water balance.

Complex climatic interactions control hydrological processes in high mountains that in their turn regulate the erosive forces shaping the relief. To unravel the hydrological cycle of a glaciated watershed (Gunt River) considered representative of the Pamirs' hydrologic regime we developed a remote sensing-based approach. At the boundary between two distinct climatic zones dominated by Westerlies and Indian summer monsoon, the Pamir is poorly instrumented and only a few in situ meteorological and hydrological data are available. We adapted a suitable conceptual distributed hydrological model (J2000g). Interpolations of the few available in situ data are inadequate due to strong, relief induced, spatial heterogeneities. Instead we use raster data, preferably from remote sensing sources depending on availability and validation. We evaluate remote sensing-based precipitation and temperature products. MODIS MOD11 surface temperatures show good agreement with in situ data, perform better than other products and represent a good proxy for air temperatures. For precipitation we tested remote sensing products as well as the HAR10 climate model data and the interpolation-based APHRODITE dataset. All products show substantial differences both in intensity and seasonal distribution with in-situ data. Despite low resolutions, the datasets are able to sustain high model efficiencies (NSE ≥0.85). In contrast to neighbouring regions in the Himalayas or the Hindukush, discharge is dominantly the product of snow and glacier melt and thus temperature is the essential controlling factor. 80% of annual precipitation is provided as snow in winter and spring contrasting peak discharges during summer. Hence, precipitation and discharge are negatively correlated and display complex hysteresis effects that allow to infer the effect of inter-annual climatic variability on river flow. We infer the existence of two subsurface reservoirs. The groundwater reservoir (providing 40% of annual discharge) recharges in spring and summer and releases slowly during fall and winter. A not fully constrained shallow reservoir with very rapid retention times buffers melt waters during spring and summer. This study highlights the importance of a better understanding of the hydrologic cycle to constrain natural hazards such as floods and landslides as well as water availability in the downstream areas. The negative glacier mass balance (−0.6 m w.e. yr−1) indicates glacier retreat, that will effect the currently 30% contribution of glacier melt to stream flow.

In recent years, many methods have been developed for the different datamining fields. One of the fields is fuzzy modeling, which is predominantly applied, if interpretable results are desired. In order to build and test fuzzy models, powerful software tools for fuzzy modeling and simulation are needed. In this paper two programs, WINROSA and DORA for Windows, are presented, which fullfill both requirements. WINROSA incorporates the Fuzzy-ROSA method, which is a method for automatic rule generation on the basis of relevance tests. DORA for Windows is a simulation tool with emphasis on fuzzy systems. The features of both programs are presented and it is shown how both programs complement each other.

A 17-year-old patient with pre-existing grade II spondylolisthesis of L5/S1 sustained a partial disruption of the left sacroiliac joint with haematoma of the iliac muscle after a fall. The haematoma probably led to occlusion of the left ureter, resulting in a urinary tract infection. After initial conservative treatment the patient developed fever and radicular pain of the left leg. Magnetic resonance imaging (MRI) revealed a left-sided epidural abscess at L5/S1, which had probably spread from the infected iliac haematoma along the injured sacroiliac joint. Prompt surgical drainage and antibiotic coverage with cefuroxime and flucloxacillin led to rapid clinical improvement. Staphylococcus aureus was identified as the pathogen. At follow-up 6 months postoperatively all symptoms had resolved, while MRI still revealed residual osseous oedema of the sacroiliac joint. The haematoma of the iliac muscle resolved without surgical intervention.

Purpose To address the factors that surgeons use to decide between 2 options for treatment when the evidence is inconclusive. Methods We tested the null hypothesis that the factors surgeons use do not vary by training, demographics, and practice. A total of 337 surgeons rated the importance of 7 factors when deciding between treatment and following the natural history of the disease and 12 factors when deciding between 2 operative treatments using a 5-point Likert scale between "very important" and "very unimportant." Results According to the percentages of statements rated very important or somewhat important, the most popular factors influencing recommendations when evidence is inconclusive between treatment and following the natural course of the illness were "works in my hands," "familiarity with the treatment," and "what my mentor taught me." The most important factors when evidence shows no difference between 2 surgeries were "fewer complications," "quicker recovery," "burns fewer bridges," "works in my hands" and "familiarity with the procedure." Europeans rated "works in my hands" and "cheapest/most resourceful" of significantly greater importance and "what others are doing," "highest reimbursement," and "shorter procedure" of significantly lower importance than surgeons in the United States. Observers with fewer than 10 years in independent practice rated "what my mentor taught me," "what others are doing" and "highest reimbursement" of significantly lower importance compared to observers with 10 or more years in independent practice. Conclusions Surgeons deciding between 2 treatment options, when the evidence is inconclusive, fall back to factors that relate to their perspective and reflect their culture and circumstances, more so than factors related to the patient's perspective, although this may be different for younger surgeons. Clinical relevance Hand surgeons might benefit from consensus fallback preferences when evidence is inconclusive. It is possible that falling back to personal comfort makes us vulnerable to unhelpful commercial and societal influences.

Background. Electronic patient files generate an enormous amount of medical data. These data can be used for research, such as prognostic modeling. Automatization of statistical prognostication processes allows automatic updating of models when new data is gathered. The increase of power behind an automated prognostic model makes its predictive capability more reliable. Cox proportional hazard regression is most frequently used in prognostication. Automatization of a Cox model is possible, but we expect the updating process to be time-consuming. A possible solution lies in an alternative modeling technique called random survival forests (RSFs). RSF is easily automated and is known to handle the proportionality assumption coherently and automatically. Performance of RSF has not yet been tested on a large head and neck oncological dataset. This study investigates performance of head and neck overall survival of RSF models. Performances are compared to a Cox model as the "gold standard." RSF might be an interesting alternative modeling approach for automatization when performances are similar. Methods. RSF models were created in R (Cox also in SPSS). Four RSF splitting rules were used: log-rank, conservation of events, log-rank score, and log-rank approximation. Models were based on historical data of 1371 patients with primary head-and-neck cancer, diagnosed between 1981 and 1998. Models contain 8 covariates: tumor site, T classification, N classification, M classification, age, sex, prior malignancies, and comorbidity. Model performances were determined by Harrell's concordance error rate, in which 33% of the original data served as a validation sample. Results. RSF and Cox models delivered similar error rates. The Cox model performed slightly better (error rate, 0.2826). The log-rank splitting approach gave the best RSF performance (error rate, 0.2873). In accord with Cox and RSF models, high T classification, high N classification, and severe comorbidity are very important covariates in the model, whereas sex, mild comorbidity, and a supraglottic larynx tumor are less important. A discrepancy arose regarding the importance of M1 classification (see Discussion). Conclusion. Both approaches delivered similar error rates. The Cox model gives a clinically understandable output on covariate impact, whereas RSF becomes more of a "black box." RSF complements the Cox model by giving more insight and confidence toward relative importance of model covariates. RSF can be recommended as the approach of choice in automating survival analyses.

On sun performance data for a 1 kW HCPV RD&D system is presented. The HCPV modules are fabricated with transfer printed micro-cells that operate at a concentration of 1000 suns. This system, installed in Tucson, AZ, is the first field validation of this novel approach that addresses the cost, performance and reliability aspects of HCPV systems. The performance of the Semprius HCPV system is compared with that of alternative PV systems, also being tested at the same location. Finally, current developments on Semprius' technical roadmap are described.

Semprius has developed a novel microcell based, highly scalable HCPV module that addresses performance, cost and reliability requirements for utility scale solar installations. Semprius has fabricated dual junction cell based engineering prototype modules with 1000X concentration based on this technology. A 1 kW HCPV system using these modules was installed in Tucson to validate the technology and acquire on‐sun data. Eight months of on‐sun results from this system are presented.

In this chapter we investigate the motivation behind the development of modelling frameworks that explicitly target the environmental domain. Despite many commercial and industrial-strength frameworks being available, we claim that there is a definite niche for environmental-specific frameworks. We first introduce a general definition of what is an environmental integrated modelling framework, leading to an outline of the requirements for a generic software architecture for such frameworks. This identifies the need for a knowledge layer to support the modelling layer and an experimentation layer to support the execution of models. The chapter then focuses on the themes of knowledge representation, model management and model execution. We advocate that appropriate knowledge representation and management tools can facilitate model integration and linking. We stress that a model development process adhering to industry standards and good practices, called “model engineering,” is to be pursued. We focus on the requirements of the experimental frame, which can ensure transparency and traceability in the execution of simulation scenarios and optimisation problems associated with complex integrated assessment studies. A promising trend for knowledge representation is the use of ontologies that have the capacity to elicit the meaning of knowledge in a manner that is logical, consistent and understandable by computers and the knowledge worker community. This new path in knowledge-based computing will support retention of institutional knowledge, while putting modelling back in the hands of modellers. Environmental modelling will then become a conceptual activity, focusing on model design rather than model implementation, with code generation being delegated to some degree to ontology-aware tools. In this respect, we envision the whole model lifecycle to change drastically, becoming more of a theoretical activity and less of a coding-intensive, highly engineering-oriented task.

Description and analysis of the personal income distribution in Germany rely heavily on two major surveys, the EVS (Income and Consumption Survey) and the SOEP (Socio-Economic Panel Study). These surveys, however, do not deliver exactly the same results in terms of income levels and structure, as well as on inequality and poverty. Some of the discrepancies have led to confusions in the political debate following the first official Poverty and Wealth Report in Germany.1 Thus, the purpose of this paper is to highlight the methodological differences between EVS and SOEP against the background of the recommendations of the “Canberra Group” (Expert Group on Household Income Statistics) and to give a comparison of some substantive results on the basis of both surveys. In describing and discussing systematically the features of the two major inquiries on household income, we aim to improve the understanding of differences in fmdings when measuring the German income distribution using SOEP and EVS, respectively.

Fuzzy-modeling is predominately applied, if transparent and interpretable results are desired. However, especially for data-based approaches it turns out that interpretability and high accuracy are often partly contradictory modeling objectives. Considering the interpretability the complexity of the final rule base, i.e. the number and structure of the rules as well as the number and shape of the membership functions, is a critical factor. Obviously, a high accuracy often comes along with a high complexity of the rule base. Furthermore, many of the common approaches are based on optimizing the rule base as a whole only with respect to the modeling error. Therefore it cannot be guaranteed that each individual rule is locally reasonable. Alternative approaches, based on optimizing or testing individual rules, generally do not reach the aimed accuracy and can lead to large and redundant rule bases. We overcome these drawbacks by new concepts presented in this contribution: They allow to improve the accuracy and to reduce the size of rule bases, consisting of individually tested rules. The applicability of the approaches is demonstrated for various tasks of different complexity. It turns out, that with the new concepts a data-based generation of small interpretable rule bases with high accuracy is possible even for high dimensional problems.

As a consequence of the continually increasing complexity of technical systems and processes, the requirement for high-performing modeling techniques is increasing. The methods of Computational Intelligence (CI), such as fuzzy modeling or artificial neural networks, are specifically designed to deal with imprecise, incomplete, and partially incorrect information in dynamically changing environments and large variable spaces. Fuzzy modeling has the additional advantage that, with certain restrictions, interpretable models are obtained. However, especially for data-based approaches, there are still difficulties in generating efficiently interpretable rule bases with the required accuracy. Moreover, particularly with regard to complex applications, efficiency, interpretability, and accuracy are often partly contradictory modeling objectives. The main focus of this contribution is on determining an adequate compromise in this conflict area. The applicability of the concepts presented is demonstrated by means of complex real-world applications in the domains of power management control, classification in quality control, and prediction in financial service.

We propose a hybrid fuzzy-evolutionary system for fuzzy modelling in high dimensional search spaces. The system architecture is based on a Michigan-style approach (one individual represents one fuzzy rule). The design of the evolutionary algorithm makes use of a distance measure in the search space that in turn reflects some heuristic assumptions about the fitness landscape. Additionally, strategy parameters are dynamically adapted by means of a fuzzy controller. The approach is successfully applied to a complex benchmark problem as well as to several real-world modelling tasks such as the cancellation behaviour of insurance clients and the classification of automatic gearboxes.

Der Einsatz von Techniken des Data–Minings und insbesondere der datenbasierten Modellierung hat im Bereich der Automatisierungstechnik in den letzten Jahren einen regelrechten Boom erfahren. Die Gründe hierfür sind zum einen das Bedürfnis der Anwender nach Modellen für immer komplexere Systeme. Zum anderen ergeben sich aufgrund leistungsfähigerer Rechnersysteme und der Entwicklung neuer Methoden, besonders aus dem Bereich der Computational Intelligence, vielfältige Möglichkeiten, auch solche Probleme zu lösen, für die herkömmliche Lösungsansätze bisher nicht erfolgreich waren. Aus Akzeptanzgründen werden in der industriellen Praxis häufig interpretierbare Modelle gefordert. Zudem weisen sie den Vorteil auf, dass mit ihnen vorhandenes Wissen integriert und in datenbasierten Ansätzen neues Wissen extrahiert werden kann. Ein Verfahren zur datenbasierten Fuzzy–Modellierung, das sich insbesondere dann bewährt hat, wenn kompakte interpretierbare Regelsätze für komplexe Problemstellungen automatisch generiert werden sollen, ist das Fuzzy–ROSA–Verfahren. In diesem Beitrag wird das Fuzzy–ROSA–Verfahren zunächst in seiner jetzigen Form vorgestellt. Dann wird genauer auf jüngste Weiterentwicklungen eingegangen, die zu einem sehr viel breiteren Anwendungsfeld geführt haben. Es wird gezeigt, wie mit neuen Regeltest- und Bewertungsstrategien unterschiedliche Modellierungsziele berücksichtigt und welche Suchraumgrößen aufgrund der Effizienzsteigerung der Regelsuche bearbeitet werden können. Des Weiteren wird die insbesondere aus methodischer Sicht interessante Erweiterung des Fuzzy–ROSA–Verfahrens zur Generierung interpretierbarer Takagi–Sugeno–Systeme mit hoher Modellierungsgüte ausführlich vorgestellt.

Introduction This training manual grew out of the experience of staff members teaching fundamental methods and techniques of photograph conservation to students and interns in the Picture Conservation Division of the National Archives of Canada. During a period of 12 years, more than 40 different individuals worked in our Photograph Conservation Laboratory for periods varying from four months to more than a year. Several returned after their first term for a second and third work period in order to increase their expertise. None of these interns had any experience in the conservation of photographs, but our laboratory rules required that they have certain credentials, i.e., a background that includes formal training in either fine art conservation (with emphasis on paper documents), or in photographic technology, or in chemistry. Training in fine art conservation is provided in Canada by the Master of Art Conservation Program at Queen's University, in Kingston, Ontario, as well as by a number of smaller colleges. There are about half a dozen similar academic programs teaching the conservation of cultural property in the United States of America. Ryerson Polytechnical Institute in Toronto, Ontario, is the only college in Canada that offers courses in photographic technology, but there are several such colleges in the U.S., of which the Rochester Institute of Technology is perhaps the best known. Chemistry can, of course, be studied at almost any college or university. We recommend that individuals who intend to follow the training sequence (program) outlined in this manual receive formal training in at least one of the above-mentioned disciplines. Throughout the manual we refer to the user of this guide as student. We use the term student in its widest sense, i.e., as designating a person who wishes to learn something by doing it and by studying. It does not matter whether that person is employed by a cultural institution, is a student in a conservation course, or is working through this manual on his or her own.

Es wurde der Serumeisenspiegel vor und wahrend der antiluische Kur bei 34 Patientinnen bestimmt. Es zeigte sich im zweiten Stadium der Lues und bei Lues lat. eine Erhohung des Serumeisenspiegels, der im Verlaufe der antiluischen Behandlung einen weiteren Anstieg um 30–40 γ-% aufwies.