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1 Mechanical manometers -- 1.1 Liquid manometers -- 1.2 The McLeod gauge -- 1.3 The diaphragm manometer -- 1.4 Viscous or friction-type gauges -- References -- 2 Thermal conductivity gauges -- 2.1 Basic principles -- 2.2 Measurement of thermal conductivity -- 2.3 Sensitivity -- 2.4 End losses -- 2.5 Accommodation coefficient and relative sensitivity -- 2.6 Alternative methods of bridge control -- 2.7 Useful range of the constant-voltage bridge -- 2.8 The lower limit to the useful pressure range -- 2.9 The importance of bridge-voltage and temperature fluctuations at high pressure -- 2.10 Compensation for temperature and voltage fluctuations -- 2.11 Physical changes in the gauge wire (ageing effects) -- 2.12 Extension of working range to atmospheric pressure -- 2.13 Commercial gauges for laboratory and industrial use -- 2.14 The thermocouple gauge -- References -- 3 Thermionic cathode ionization gauges -- 3.1 Positive ion production in a gas -- 3.2 The principle of the thermionic cathode ionization gauge -- 3.3 The relative sensitivity for different gases -- 3.4 The measurement of low pressures -- 3.5 Extension of the range of the BA gauge to very low pressures -- 3.6 The precision to which measurements can be made with the hot cathode gauge -- 3.7 Gauges specially designed to operate at high pressure -- 3.8 Chemical and physical reactions in the hot cathode ionization gauge -- References -- 4 Cold-cathode ionization gauges -- 4.1 The development of cold-cathode (crossed-field) gauges -- 4.2 Commercial gauges for high- and ultra-high vacuum applications -- References -- 5 Gauge calibration -- 5.1 Basic considerations -- 5.2 Calibration against the transfer gauge -- 5.3 Comparison with absolute gauges -- 5.4 Series expansion techniques -- 5.5 Dynamic flow techniques -- 5.6 The measurement of gas throughput -- References -- 6 Gas analysis in vacuum systems: magnetic, crossed-field and time-of-flight analysers -- 6.1 Introduction -- 6.2 The magnetic deflection mass spectrometer -- 6.3 The trochoidal (or cycloidal) mass spectrometer -- 6.4 The omegatron -- 6.5 Time-of-flight (TOF) mass spectrometer -- 6.6 Interpretation of mass spectra -- References -- 7 Gas analysis in vacuum systems: quadrupole mass analysers -- 7.1 Introduction -- 7.2 Principles of the quadrupole mass filter -- 7.3 Design of small residual gas analysers (RGAs) -- 7.4 The operating characteristics of the RGAs designed for general laboratory and industrial use -- 7.5 The use of electron multipliers for signal detection -- 7.6 Non-conventional methods of quadrupole operation -- 7.7 The monopole mass spectrometer -- 7.8 The three-dimensional quadrupole ion trap -- References.

[EN] The structure of UiO-66(Ce) is formed by CeO2-x defective nanoclusters connected by terephthalate ligands. The initial presence of accessible Ce3+ sites in the as-synthesized UiO-66(Ce) has been determined by X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared (FTIR)-CO analyses. Moreover, linear scan voltammetric measurements reveal a reversible Ce4+/Ce3+ interconversion within the UiO-66(Ce) material, while nanocrystalline ceria shows an irreversible voltammetric response. This suggests that terephthalic acid ligands facilitate charge transfer between subnanometric metallic nodes, explaining the higher oxidase-like activity of UiO-66(Ce) compared to nanoceria for the mild oxidation of organic dyes under aerobic conditions. Based on these results, we propose the use of Ce-based metal-organic frameworks (MOFs) as efficient catalysts for the halogenation of activated arenes, as 1,3,5-trimethoxybenzene (TMB), using oxygen as a green oxidant. Kinetic studies demonstrate that UiO-66(Ce) is at least three times more active than nanoceria under the same reaction conditions. In addition, the UiO-66(Ce) catalyst shows an excellent stability and can be reused after proper washing treatments. Finally, a general mechanism for the oxidative halogenation reaction is proposed when using Ce-MOF as a catalyst, which mimics the mechanistic pathway described for metalloenzymes. The superb control in the generation of subnanometric CeO2-x defective clusters connected by adequate organic ligands in MOFs offers exciting opportunities in the design of Ce-based redox catalysts. ; This work has been supported by the Spanish Government through the "Severo Ochoa" (SEV-2016-0683, MINECO) and RTI2018-101033-B-I00 (MCIU/AEI/FEDER, UE). J. M. Salas is acknowledged for his contribution to CO-IR experiments. The Electron Microscopy Service of the UPV is also acknowledged for their help in sample characterization. ; Rojas-Buzo, S.; Concepción Heydorn, P.; Olloqui-Sariego, JL.; Moliner Marin, M.; Corma Canós, A. (2021). ...

This paper serves three purposes, using a vector-error-correction model (VECM) to analyse the effects of permanent changes in R&D variables. First, we re-consider the traditional result of zero or negative foreign R&D spillovers to US productivity from private and public foreign R&D stocks. Both have a positive and statistically significant effect on labour-augmenting technical change (LATC) and public R&D in the USA. Moreover, US private R&D reacts positively to foreign private R&D and negatively to foreign public R&D shocks. Second, we also find new results for the effects of changes of US R&D. Foreign public and private R&D react positively to US public R&D. All the mentioned variables react positively to changes in US private R&D. Third, based on the time profile of the simulated VECM estimate, we calculate the sum of discounted net gains for (i) additional private and public US R&D, and (ii) for policies reacting to foreign private and public R&D shocks with additional domestic private and public R&D. Additional private and public US R&D expenditures have very high internal rates of return and are profitable also in reaction to shocks from foreign R&D. All LATC reactions are transitional, suggesting semi-endogenous growth for the USA.

Focusing on the period of Milton Friedman's collaboration with Anna J. Schwartz, from 1948 to 1991, this 1996 work examines the history of debates between Friedman and his critics over money's causal role in business cycles. Professor Hammond shows that critics' reactions were grounded in two distinctive features of Friedman and Schwartz's way of doing economic analysis - their National Bureau business cycle methods and Friedman's Marshallian methodology. With the post-war dominance of Cowles Commission methods and Walrasian methodology, Friedman and Schwartz's monetary economics appeared to contemporary critics to be 'measurement without theory'. Drawing extensively upon unpublished materials, Professor Hammond's treatment offers new insights on Milton Friedman's attempts to settle debates with his critics and his eventual recognition of the methodological impediments. The book will interest monetary economists and macroeconomists, as well as historians of economics and methodologists

A series of models are proposed to describe the production of military grade nitrocellulose from dense cellulose materials in mixtures of nitric acid, sulfuric acid, and water. This effort is conducted to provide a predictive capability for analyzing the rate and extent of reaction achieved under a range of reaction conditions used in the industrial nitrocellulose manufacturing process for sheeted cellulose materials. Because this capability does not presently exist, nitrocellulose producers have historically relied on a very narrow range of cellulose raw materials and resorted to trial and error methods to develop processing conditions for new materials. This tool enables nitrocellulose manufacturers to rapidly adapt to changing market conditions, supply disruptions, or normal variation in the quality of cellulose raw materials and provides process engineers with an improved capability for process control and analysis. This work includes measurement of the kinetics of nitration for cellulose fibers in mixed acids, an evaluation of simultaneous mass transfer and swelling in slivers cut from sheeted cellulose materials, and a structural analysis of slivers cut on industrial rotary cutting machines to consider features that may increase the reactivity of these materials. The kinetics of nitration of all high purity cellulose fibers are demonstrated to be equivalent, and the nitration of dense cellulose materials is shown to be a mass transfer limited process except in the case of small wood pulp slivers in mixed acids used in the production of Grade B nitrocellulose. In addition, it is shown that diffusion and unidirectional swelling occur on similar timescales during the nitration of slivers cut from sheeted wood pulp, resulting in variable diffusivity of mixed acids through the wood pulp sliver structure during the nitration reaction. Finally, delaminated regions or galleries that are formed as a result of the shearing action of the rotary cutting machine used in the industrial nitrocellulose manufacturing ...

8 págs.; 5 figs.; 1 tab. ; Open Access funded by Creative Commons Atribution Licence 4.0 ; In the last decade, the development of theoretical methods has allowed chemists to reproduce and explain almost all of the experimental data associated with elementary atom plus diatom collisions. However, there are still a few examples where theory cannot account yet for experimental results. This is the case for the preferential population of one of the Λ-doublet states produced by chemical reactions. In particular, recent measurements of the OD( Π) product of the O(P)+D reaction have shown a clear preference for the Π (A′) Λ-doublet states, in apparent contradiction with ab initio calculations, which predict a larger reactivity on the A″ potential energy surface. Here we present a method to calculate the Λ-doublet ratio when concurrent potential energy surfaces participate in the reaction. It accounts for the experimental Λ-doublet populations via explicit consideration of the stereodynamics of the process. Furthermore, our results demonstrate that the propensity of the π (A′) state is a consequence of the different mechanisms of the reaction on the two concurrent potential energy surfaces. ; We acknowledge funding by the Spanish Ministry of Economy and Competitiveness (grants CTQ2012-37404-C02, CTQ2015-65033-P and Consolider Ingenio 2010 CSD2009-00038). M.B. gratefully acknowledges the support of the U.K. EPSRC (via Programme Grant EP/L005913/1). P.G.J. acknowledges the Spanish Ministry of Economy and Competitiveness for the Juan de la Cierva fellowship (IJCI-2014-20615). A.Z. acknowledges the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement number 610256 (NANOCOSMOS). ; Peer Reviewed

As part of a mail survey about their work experiences, university faculty members reported their specific emotional reactions to group inequities in faculty pay and benefits. The results indicate that sadness, fear, and anger are distinct emotional responses to a collective disadvantage. Group‐based anger mediated the relationship between collective disadvantage and willingness to protest whereas group‐based sadness mediated the relationship between collective disadvantage and organizational loyalty. Based on an integration of cognitive appraisal models of emotion with RD theory, four other predictors of intergroup emotions—(1) the legitimacy of the process that produced the deprivation, (2) whether another agent was responsible, (3) group efficacy, and (4) whether the situation would improve or become worse—were identified and tested. The measurement of specific emotional reactions to perceived collective disadvantage extends and refines RD approaches to collective action and organizational loyalty.

This study examines the relationship between forearm EMGs and keyboard reaction forces in 10 people during keyboard tasks performed at a comfortable speed. A linear fit of EMG force data for each person and finger was calculated during static fingertip loading. An average r2 of .71 was observed for forces below 50% of the maximal voluntary contraction (MVC). These regressions were used to characterize EMG data in force units during the typing task. Averaged peak reaction forces measured during typing ranged from 3.33 N (thumb) to 1.84 N (little finger), with an overall average of 2.54 N, which represents about 10% MVC and 5.4 times the key switch make force (0.47 N). Individual peak or mean finger forces obtained from EMG were greater (1.2 to 3.2 times) than force measurements; hence the range of r2 for EMG force was .10 to .46. A closer correspondence between EMG and peak force was obtained using EMG averaged across all fingers. For 5 of the participants the force computed from EMG was within ±20% of the reaction force. For the other 5 participants forces were overestimated. For 9 participants the difference between EMG estimated force and the reaction force was less than 13% MVC. It is suggested that the difference between EMG and finger force partly results from the amount of muscle load not captured by the measured applied force.

A comprehensive performance measurement system was developed for two departments of a small retail corporation. The measurement system measured key dimensions of performance and integrated multiple measures into a composite index using a ProMES (productivity measurement and evaluation system) methodology. Subsequent to development, the measurement system was used in one of the departments as a monthly feedback system. A significant improvement in overall performance occurred in the department receiving feedback, whereas no improvement was found in a comparison department that did not receive feedback. In addition to the effects of feedback, a process methodology was used to assess users' reactions to the measurement system. Qualitative analysis procedures indicated that their reactions were slightly positive, although many suggestions for improvements were offered. Changes to the measurement system were made as a result of the suggestions.

The relative contribution of number of fixations and fixation duration to reaction time in visual search was investigated. Ten participants (age 20--24 years) took part in each of two experiments. In Experiment 1, the experimental factors were display type (icon and file name), organization (arrangements with and without grouping), and number of stimuli presented (4, 8, and 16). In Experiment 2, a search task for a target stimulus (three prespecified random letters) was conducted, and the experimental factor was the display's layout complexity. Multiple regression analysis was used to examine whether reaction time was explained by a mediational model in which reaction time is mediated by eye movements and display features are not directly related to reaction time. The mediational model was not supported, and the effects of display features on reaction time were not attributable solely to eye movements. The interaction between number of fixations and fixation duration was also explored as a function of display features. As the display feature changed and the task became more difficult, the contribution of the number of fixations to explain the variation in reaction time became dominant for both experiments. Potential applications include measurements of cognitive ability, eye muscle balance disorders, and binocular fusion ability.

AbstractThis study demonstrates evidence that the mass transfer process of an enzyme (a biocatalyst) is the rate‐limiting step in the starch hydrolysis reaction during food digestion. The significance of this work has been to compare the reaction rate of starch hydrolysis by salivary enzymes with the mass transfer rate of rate‐limiting enzymes. This research has applied mass transfer and reaction engineering theory in a quantitative study of starch hydrolysis, and a dimensionless group, the Damköhler number (Da), has been calculated based on glucose measurements from a beaker and stirrer system. The values of the Da number in this study (0.3–19) indicate that both the time constant for mass transfer and the time constant for reaction are significant parameters. Scanning electron microscopy images emphasize that compression (simulated mastication) helps to break the plant cell wall of starch. Mass‐transfer resistance needs to be considered during food digestion studies. The Da numbers are significantly affected by both compression forces (internal mass‐transfer coefficients) and stirrer speeds (external mass‐transfer coefficients) in this beaker and stirrer system.

Ein Army Colonel empfindet Mitleid mit einem Roboter, der versuchsweise Landminen entschärft und deklariert den Test als inhuman (Garreau, 2007). Roboter bekommen militärische Beförderungen, Beerdigungen und Ehrenmedaillen (Garreau, 2007; Carpenter, 2013). Ein Schildkrötenroboter wird entwickelt, um Kindern beizubringen, Roboter gut zu behandeln (Ackermann, 2018). Der humanoide Roboter Sophia wurde erst kürzlich Saudi-Arabischer Staatsbürger und es gibt bereits Debatten, ob Roboter Rechte bekommen sollen (Delcker, 2018). Diese und ähnliche Entwicklungen zeigen schon jetzt die Bedeutsamkeit von Robotern und die emotionale Wirkung die diese auslösen. Dennoch scheinen sich diese emotionalen Reaktionen auf einer anderen Ebene abzuspielen, gemessen an Kommentaren in Internetforen. Dort ist oftmals die Rede davon, wieso jemand überhaupt emotional auf einen Roboter reagieren kann. Tatsächlich ist es, rein rational gesehen, schwierig zu erklären, warum Menschen mit einer leblosen ('mindless') Maschine mitfühlen sollten. Und dennoch zeugen nicht nur oben genannte Berichte, sondern auch erste wissenschaftliche Studien (z.B. Rosenthal- von der Pütten et al., 2013) von dem emotionalen Einfluss den Roboter auf Menschen haben können. Trotz der Bedeutsamkeit der Erforschung emotionaler Reaktionen auf Roboter existieren bislang wenige wissenschaftliche Studien hierzu. Tatsächlich identifizierten Kappas, Krumhuber und Küster (2013) die systematische Analyse und Evaluation sozialer Reaktionen auf Roboter als eine der größten Herausforderungen der affektiven Mensch-Roboter Interaktion. Nach Scherer (2001; 2005) bestehen Emotionen aus der Koordination und Synchronisation verschiedener Komponenten, die miteinander verknüpft sind. Motorischer Ausdruck (Mimik), subjektives Erleben, Handlungstendenzen, physiologische und kognitive Komponenten gehören hierzu. Um eine Emotion vollständig zu erfassen, müssten all diese Komponenten gemessen werden, jedoch wurde eine solch umfassende Analyse bisher noch nie durchgeführt (Scherer, 2005). Hauptsächlich werden Fragebögen eingesetzt (vgl. Bethel & Murphy, 2010), die allerdings meist nur das subjektive Erleben abfragen. Bakeman und Gottman (1997) geben sogar an, dass nur etwa 8% der psychologischen Forschung auf Verhaltensdaten basiert, obwohl die Psychologie traditionell als das 'Studium von Psyche und Verhalten' (American Psychological Association, 2018) definiert wird. Die Messung anderer Emotionskomponenten ist selten. Zudem sind Fragebögen mit einer Reihe von Nachteilen behaftet (Austin, Deary, Gibson, McGregor, Dent, 1998; Fan et al., 2006; Wilcox, 2011). Bethel und Murphy (2010) als auch Arkin und Moshkina (2015) plädieren für einen Multi-Methodenansatz um ein umfassenderes Verständnis von affektiven Prozessen in der Mensch-Roboter Interaktion zu erlangen. Das Hauptziel der vorliegenden Dissertation ist es daher, mithilfe eines Multi-Methodenansatzes verschiedene Komponenten von Emotionen (motorischer Ausdruck, subjektive Gefühlskomponente, Handlungstendenzen) zu erfassen und so zu einem vollständigeren und tiefgreifenderem Bild emotionaler Prozesse auf Roboter beizutragen. Um dieses Ziel zu erreichen, wurden drei experimentelle Studien mit insgesamt 491 Teilnehmern durchgeführt. Mit unterschiedlichen Ebenen der "apparent reality" (Frijda, 2007) sowie Macht / Kontrolle über die Situation (vgl. Scherer & Ellgring, 2007) wurde untersucht, inwiefern sich Intensität und Qualität emotionaler Reaktionen auf Roboter ändern und welche weiteren Faktoren (Aussehen des Roboters, emotionale Expressivität des Roboters, Behandlung des Roboters, Autoritätsstatus des Roboters) Einfluss ausüben. Experiment 1 basierte auf Videos, die verschiedene Arten von Robotern (tierähnlich, anthropomorph, maschinenartig), die entweder emotional expressiv waren oder nicht (an / aus) in verschiedenen Situationen (freundliche Behandlung des Roboters vs. Misshandlung) zeigten. Fragebögen über selbstberichtete Gefühle und die motorisch-expressive Komponente von Emotionen: Mimik (vgl. Scherer, 2005) wurden analysiert. Das Facial Action Coding System (Ekman, Friesen, & Hager, 2002), die umfassendste und am weitesten verbreitete Methode zur objektiven Untersuchung von Mimik, wurde hierfür verwendet. Die Ergebnisse zeigten, dass die Probanden Gesichtsausdrücke (Action Unit [AU] 12 und AUs, die mit positiven Emotionen assoziiert sind, sowie AU 4 und AUs, die mit negativen Emotionen assoziiert sind) sowie selbstberichtete Gefühle in Übereinstimmung mit der Valenz der in den Videos gezeigten Behandlung zeigten. Bei emotional expressiven Robotern konnten stärkere emotionale Reaktionen beobachtet werden als bei nicht-expressiven Robotern. Der tierähnliche Roboter Pleo erfuhr in der Misshandlungs-Bedingung am meisten Mitleid, Empathie, negative Gefühle und Traurigkeit, gefolgt vom anthropomorphen Roboter Reeti und am wenigsten für den maschinenartigen Roboter Roomba. Roomba wurde am meisten Antipathie zugeschrieben. Die Ergebnisse knüpfen an frühere Forschungen an (z.B. Krach et al., 2008; Menne & Schwab, 2018; Riek et al., 2009; Rosenthal-von der Pütten et al., 2013) und zeigen das Potenzial der Mimik für eine natürliche Mensch-Roboter Interaktion. Experiment 2 und Experiment 3 übertrugen die klassischen Experimente von Milgram (1963; 1974) zum Thema Gehorsam in den Kontext der Mensch-Roboter Interaktion. Die Gehorsamkeitsstudien von Milgram wurden als sehr geeignet erachtet, um das Ausmaß der Empathie gegenüber einem Roboter im Verhältnis zum Gehorsam gegenüber einem Roboter zu untersuchen. Experiment 2 unterschied sich von Experiment 3 in der Ebene der "apparent reality" (Frijda, 2007): in Anlehnung an Milgram (1963) wurde eine rein text-basierte Studie (Experiment 2) einer live Mensch-Roboter Interaktion (Experiment 3) gegenübergestellt. Während die abhängigen Variablen von Experiment 2 aus den Selbstberichten emotionaler Gefühle sowie Einschätzungen des hypothetischen Verhaltens bestand, erfasste Experiment 3 subjektive Gefühle sowie reales Verhalten (Reaktionszeit: Dauer des Zögerns; Gehorsamkeitsrate; Anzahl der Proteste; Mimik) der Teilnehmer. Beide Experimente untersuchten den Einfluss der Faktoren "Autoritätsstatus" (hoch / niedrig) des Roboters, der die Befehle erteilt (Nao) und die emotionale Expressivität (an / aus) des Roboters, der die Strafen erhält (Pleo). Die subjektiven Gefühle der Teilnehmer aus Experiment 2 unterschieden sich zwischen den Gruppen nicht. Darüber hinaus gaben nur wenige Teilnehmer (20.2%) an, dass sie den "Opfer"-Roboter definitiv bestrafen würden. Ein ähnliches Ergebnis fand auch Milgram (1963). Das reale Verhalten von Versuchsteilnehmern in Milgrams' Labor-Experiment unterschied sich jedoch von Einschätzungen hypothetischen Verhaltens von Teilnehmern, denen Milgram das Experiment nur beschrieben hatte. Ebenso lassen Kommentare von Teilnehmern aus Experiment 2 darauf schließen, dass das beschriebene Szenario möglicherweise als fiktiv eingestuft wurde und Einschätzungen von hypothetischem Verhalten daher kein realistisches Bild realen Verhaltens gegenüber Roboter in einer live Interaktion zeichnen können. Daher wurde ein weiteres Experiment (Experiment 3) mit einer Live Interaktion mit einem Roboter als Autoritätsfigur (hoher Autoritätsstatus vs. niedriger) und einem weiteren Roboter als "Opfer" (emotional expressiv vs. nicht expressiv) durchgeführt. Es wurden Gruppenunterschiede in Fragebögen über emotionale Reaktionen gefunden. Dem emotional expressiven Roboter wurde mehr Empathie entgegengebracht und es wurde mehr Freude und weniger Antipathie berichtet als gegenüber einem nicht-expressiven Roboter. Außerdem konnten Gesichtsausdrücke beobachtet werden, die mit negativen Emotionen assoziiert sind während Probanden Nao's Befehl ausführten und Pleo bestraften. Obwohl Probanden tendenziell länger zögerten, wenn sie einen emotional expressiven Roboter bestrafen sollten und der Befehl von einem Roboter mit niedrigem Autoritätsstatus kam, wurde dieser Unterschied nicht signifikant. Zudem waren alle bis auf einen Probanden gehorsam und bestraften Pleo, wie vom Nao Roboter befohlen. Dieses Ergebnis steht in starkem Gegensatz zu dem selbstberichteten hypothetischen Verhalten der Teilnehmer aus Experiment 2 und unterstützt die Annahme, dass die Einschätzungen von hypothetischem Verhalten in einem Mensch-Roboter-Gehorsamkeitsszenario nicht zuverlässig sind für echtes Verhalten in einer live Mensch-Roboter Interaktion. Situative Variablen, wie z.B. der Gehorsam gegenüber Autoritäten, sogar gegenüber einem Roboter, scheinen stärker zu sein als Empathie für einen Roboter. Dieser Befund knüpft an andere Studien an (z.B. Bartneck & Hu, 2008; Geiskkovitch et al., 2016; Menne, 2017; Slater et al., 2006), eröffnet neue Erkenntnisse zum Einfluss von Robotern, zeigt aber auch auf, dass die Wahl einer Methode um Empathie für einen Roboter zu evozieren eine nicht triviale Angelegenheit ist (vgl. Geiskkovitch et al., 2016; vgl. Milgram, 1965). Insgesamt stützen die Ergebnisse die Annahme, dass die emotionalen Reaktionen auf Roboter tiefgreifend sind und sich sowohl auf der subjektiven Ebene als auch in der motorischen Komponente zeigen. Menschen reagieren emotional auf einen Roboter, der emotional expressiv ist und eher weniger wie eine Maschine aussieht. Sie empfinden Empathie und negative Gefühle, wenn ein Roboter misshandelt wird und diese emotionalen Reaktionen spiegeln sich in der Mimik. Darüber hinaus unterscheiden sich die Einschätzungen von Menschen über ihr eigenes hypothetisches Verhalten von ihrem tatsächlichen Verhalten, weshalb videobasierte oder live Interaktionen zur Analyse realer Verhaltensreaktionen empfohlen wird. Die Ankunft sozialer Roboter in der Gesellschaft führt zu nie dagewesenen Fragen und diese Dissertation liefert einen ersten Schritt zum Verständnis dieser neuen Herausforderungen. ; An Army Colonel feels sorry for a robot that defuses landmines on a trial basis and declares the test inhumane (Garreau, 2007). Robots receive military promotions, funerals and medals of honor (Garreau, 2007; Carpenter, 2013). A turtle robot is being developed to teach children to treat robots well (Ackermann, 2018). The humanoid robot Sophia recently became a Saudi Arabian citizen and there are now debates whether robots should have rights (Delcker, 2018). These and similar developments already show the importance of robots and the emotional impact they have. Nevertheless, these emotional reactions seem to take place on a different level, judging by comments in internet forums alone: Most often, emotional reactions towards robots are questioned if not denied at all. In fact, from a purely rational point of view, it is difficult to explain why people should empathize with a mindless machine. However, not only the reports mentioned above but also first scientific studies (e.g. Rosenthal- von der Pütten et al., 2013) bear witness to the emotional influence of robots on humans. Despite the importance of researching emotional reactions towards robots, there are few scientific studies on this subject. In fact, Kappas, Krumhuber and Küster (2013) identified effective testing and evaluation of social reactions towards robots as one of the major challenges of affective Human-Robot Interaction (HRI). According to Scherer (2001; 2005), emotions consist of the coordination and synchronization of different components that are linked to each other. These include motor expression (facial expressions), subjective experience, action tendencies, physiological and cognitive components. To fully capture an emotion, all these components would have to be measured, but such a comprehensive analysis has never been performed (Scherer, 2005). Primarily, questionnaires are used (cf. Bethel & Murphy, 2010) but most of them only capture subjective experiences. Bakeman and Gottman (1997) even state that only about 8% of psychological research is based on behavioral data, although psychology is traditionally defined as the 'study of the mind and behavior' (American Psychological Association, 2018). The measurement of other emotional components is rare. In addition, questionnaires have a number of disadvantages (Austin, Deary, Gibson, McGregor, Dent, 1998; Fan et al., 2006; Wilcox, 2011). Bethel and Murphy (2010) as well as Arkin and Moshkina (2015) argue for a multi-method approach to achieve a more comprehensive understanding of affective processes in HRI. The main goal of this dissertation is therefore to use a multi-method approach to capture different components of emotions (motor expression, subjective feeling component, action tendencies) and thus contribute to a more complete and profound picture of emotional processes towards robots. To achieve this goal, three experimental studies were conducted with a total of 491 participants. With different levels of 'apparent reality' (Frijda, 2007) and power/control over the situation (cf. Scherer & Ellgring, 2007), the extent to which the intensity and quality of emotional responses to robots change were investigated as well as the influence of other factors (appearance of the robot, emotional expressivity of the robot, treatment of the robot, authority status of the robot). Experiment 1 was based on videos showing different types of robots (animal-like, anthropomorphic, machine-like) in different situations (friendly treatment of the robot vs. torture treatment) while being either emotionally expressive or not. Self-reports of feelings as well as the motoric-expressive component of emotion: facial expressions (cf. Scherer, 2005) were analyzed. The Facial Action Coding System (Ekman, Friesen, & Hager, 2002), the most comprehensive and most widely used method for objectively assessing facial expressions, was utilized for this purpose. Results showed that participants displayed facial expressions (Action Unit [AU] 12 and AUs associated with positive emotions as well as AU 4 and AUs associated with negative emotions) as well as self-reported feelings in line with the valence of the treatment shown in the videos. Stronger emotional reactions could be observed for emotionally expressive robots than non-expressive robots. Most pity, empathy, negative feelings and sadness were reported for the animal-like robot Pleo while watching it being tortured, followed by the anthropomorphic robot Reeti and least for the machine-like robot Roomba. Most antipathy was attributed to Roomba. The findings are in line with previous research (e.g., Krach et al., 2008; Menne & Schwab, 2018; Riek et al., 2009; Rosenthal-von der Pütten et al., 2013) and show facial expressions' potential for a natural HRI. Experiment 2 and Experiment 3 transferred Milgram's classic experiments (1963; 1974) on obedience into the context of HRI. Milgram's obedience studies were deemed highly suitable to study the extent of empathy towards a robot in relation to obedience to a robot. Experiment 2 differed from Experiment 3 in the level of 'apparent reality' (Frijda, 2007): based on Milgram (1963), a purely text-based study (Experiment 2) was compared with a live HRI (Experiment 3). While the dependent variables of Experiment 2 consisted of self-reports of emotional feelings and assessments of hypothetical behavior, Experiment 3 measured subjective feelings and real behavior (reaction time: duration of hesitation; obedience rate; number of protests; facial expressions) of the participants. Both experiments examined the influence of the factors "authority status" (high / low) of the robot giving the orders (Nao) and the emotional expressivity (on / off) of the robot receiving the punishments (Pleo). The subjective feelings of the participants from Experiment 2 did not differ between the groups. In addition, only few participants (20.2%) stated that they would definitely punish the "victim" robot. Milgram (1963) found a similar result. However, the real behavior of participants in Milgram's laboratory experiment differed from the estimates of hypothetical behavior of participants to whom Milgram had only described the experiment. Similarly, comments from participants in Experiment 2 suggest that the scenario described may have been considered fictitious and that assessments of hypothetical behavior may not provide a realistic picture of real behavior towards robots in a live interaction. Therefore, another experiment (Experiment 3) was performed with a live interaction with a robot as authority figure (high authority status vs. low) and another robot as "victim" (emotional expressive vs. non-expressive). Group differences were found in questionnaires on emotional responses. More empathy was shown for the emotionally expressive robot and more joy and less antipathy was reported than for a non-expressive robot. In addition, facial expressions associated with negative emotions could be observed while subjects executed Nao's command and punished Pleo. Although subjects tended to hesitate longer when punishing an emotionally expressive robot and the order came from a robot with low authority status, this difference did not reach significance. Furthermore, all but one subject were obedient and punished Pleo as commanded by the Nao robot. This result stands in stark contrast to the self-reported hypothetical behavior of the participants from Experiment 2 and supports the assumption that the assessments of hypothetical behavior in a Human-Robot obedience scenario are not reliable for real behavior in a live HRI. Situational variables, such as obedience to authorities, even to a robot, seem to be stronger than empathy for a robot. This finding is in line with previous studies (e.g. Bartneck & Hu, 2008; Geiskkovitch et al., 2016; Menne, 2017; Slater et al., 2006), opens up new insights into the influence of robots, but also shows that the choice of a method to evoke empathy for a robot is not a trivial matter (cf. Geiskkovitch et al., 2016; cf. Milgram, 1965). Overall, the results support the assumption that emotional reactions to robots are profound and manifest both at the subjective level and in the motor component. Humans react emotionally to a robot that is emotionally expressive and looks less like a machine. They feel empathy and negative feelings when a robot is abused and these emotional reactions are reflected in facial expressions. In addition, people's assessments of their own hypothetical behavior differ from their actual behavior, which is why video-based or live interactions are recommended for analyzing real behavioral responses. The arrival of social robots in society leads to unprecedented questions and this dissertation provides a first step towards understanding these new challenges. ; Are there emotional reactions towards social robots? Could you love a robot? Or, put the other way round: Could you mistreat a robot, tear it apart and sell it? Media reports people honoring military robots with funerals, mourning the "death" of a robotic dog, and granting the humanoid robot Sophia citizenship. But how profound are these reactions? Three experiments take a closer look on emotional reactions towards social robots by investigating the subjective experience of people as well as the motor expressive level. Contexts of varying degrees of Human-Robot Interaction (HRI) sketch a nuanced picture of emotions towards social robots that encompass conscious as well as unconscious reactions. The findings advance the understanding of affective experiences in HRI. It also turns the initial question into: Can emotional reactions towards social robots even be avoided?

Scholarly interest in the role of emotion in accounting for how people react to political figures, events, and messages has escalated over the past two plus decades in political science and psychology. However, research on the validity of the measurement of subjective self-report of emotional responses is rather limited. We introduce here a new measurement approach, a "slider" format and compare it with the long used "radio button" item format. We assess the reliability and validity of these two approaches to the measurement of affect. The study examines self-report measures of emotion to three generated news stories about terrorist threats. We report that both measurement formats are able to extract the expected threefold affect structure from a ten affect word battery. The slider format is, however, modestly more reliable, and more efficient in time to complete, has the ability to limit missing data, and generates continuous data that is less truncated than data derived from the radio button format. Finally, we report on three tests of construct validity. Both approaches exhibit equivalent results on two of those tests. However, the radio button format does poorly on one test of construct validity, that on the anticipated relationship between anxiety and interest in novel information. We present an assessment of two methods for measuring emotional reactions to stimuli such as political issues, political figures, or events. Both methods are suitable for use in online surveys or computer-driven experiments. The traditional method utilizes labeled "radio buttons" that enable a participant in a study to select by clicking on one of an array of typically five response options, ranging from lower to higher of some identified affect term (e.g., how angry one might feel). Second, the slider method offers a participant the ability to move an "arrow" up or down to indicate how much (up) or little (down) they feel. The goal of both measures is to ascertain the level of a targeted emotion, i.e., how little or how much, say anger. The slider method has been specifically developed to be used with participants using a computer. The slider approach falls within the category of visual analog scales. This method for measuring affective responses to stimuli of whatever sort has not hitherto been examined to determine its reliability and validity. The literature on the reliability and validity of these measurement strategies is thin and we found no studies including an explicit comparison.

The paper reports on measurement and data treatment of response latencies in computer
assisted surveys. Applying response latencies as a measure of mental processes, empirical
hypotheses are tested to explain the occurrence of response effects (here: acquiescence bias)
and the predictive power of generalized attitudes. Theoretically, it is assumed that behavioural
and other specific evaluative judgments in surveys are stronger influenced by acquiescence
bias and generalized attitudes when answering in a rather automatic-spontaneous response
mode, i.e. when response latencies are fast. Additionally, it is assumed that chronic attitude
accessibility acts as a moderator of acquiescence effects and predictive power of attitudes
within spontaneous mode processing. Empirical tests show evidence in favour of these
assumptions, demonstrating the usefulness of response time measurement in surveys.

10 páginas, 8 figuras, 2 tablas ; In this article, we develop a systematic approach for efficient field reconstruction in distributed process systems from a limited number of measurements. The approach generalizes previous methods for sensor placement so as to be able to handle field reconstruction problems in arbitrary spatial domains where complex nonlinear phenomena take place. Pattern formation in fluid dynamics or diffusion-reaction systems are examples exhibiting complex nonlinear distributed behaviors, especially when taking place in arbitrary 2D or 3D domains. Our approach exploits the dissipative nature of the diffusion-convection process and the underlying algebraic structure of the finite element method to efficiently construct field representations in terms of globally defined basis functions and to optimally select the placement of sensors. The results will be illustrated on a fluid dynamic process: the Rayleigh−Bénard problem ; The authors acknowledge financial support received from the Spanish Government (DPI2004-07444-C04-03) and Xunta de Galicia (PGIDIT02-PXIC40209PN). ; Peer reviewed