Can you feel the advertisement tonight? The effect of ASMR cues in video advertising on purchase intention

ASMR is a sensory response characterized by physical tingles in the head and spine that can be induced by everyday life cues like watching or hearing someone's hair being brushed. As numerous videos are now deliberately designed to evoke ASMR, brands have also shown interest to include ASMR cues in their advertisements. This paper presents three studies scrutinizing ASMR experiences, both in a non-advertising and advertising context. First, a web-scraping study suggests that ASMR is typically associated with feelings of relaxation. Furthermore, two experiments show the positive influence of embedding ASMR cues in advertisements on consumers' purchase intentions. A serial mediation analysis demonstrates that this positive effect can be explained by increased feelings of relaxation, which enable a better flow-like experience. Finally, theoretical and managerial implications are discussed.ASMR is a sensory response characterized by physical tingles in the head and spine that can be induced by everyday life cues like watching or hearing someone's hair being brushed. As numerous videos are now deliberately designed to evoke ASMR, brands have also shown interest to include ASMR cues in their advertisements. This paper presents three studies scrutinizing ASMR experiences, both in a non-advertising and advertising context. First, a web-scraping study suggests that ASMR is typically associated with feelings of relaxation. Furthermore, two experiments show the positive influence of embedding ASMR cues in advertisements on consumers' purchase intentions. A serial mediation analysis demonstrates that this positive effect can be explained by increased feelings of relaxation, which enable a better flow-like experience. Finally, theoretical and managerial implications are discussed.A

A simple synthetic entryway into (N-heterocyclic carbene)gold-steroidyl complexes and their anticancer activity

A straightforward synthetic route to new N-heterocyclic carbene (NHC)-gold-steroidyl complexes is reported. The desired complexes were obtained using a weak base (such as K2CO3) through a concerted-metallation-deprotonation (CMD) reaction mechanism occurring between [Au(NHC)Cl] and ethisterone as a model steroid-based alkyne. Most complexes displayed good cytotoxicity against a panel of cancer cell lines with IC50 values in the low micromolar range. Cellular uptake of the most active complex 2a into MCF-7 breast cancer cells was facilitated by the coordinated ethisterone ligand.A straightforward synthetic route to new N-heterocyclic carbene (NHC)-gold-steroidyl complexes is reported. The desired complexes were obtained using a weak base (such as K2CO3) through a concerted-metallation-deprotonation (CMD) reaction mechanism occurring between [Au(NHC)Cl] and ethisterone as a model steroid-based alkyne. Most complexes displayed good cytotoxicity against a panel of cancer cell lines with IC50 values in the low micromolar range. Cellular uptake of the most active complex 2a into MCF-7 breast cancer cells was facilitated by the coordinated ethisterone ligand.A

Judicial reasoning in tort law : English and French traditions compared /

Description based on CIP data; item not viewed

The intersubjectification of vision as interactional thought : vision imperatives in Ancient Greek diachrony

B

De l'abbatiat laïque à l'avouerie : le cas des monastères de femmes en Lotharingie (IXe-début Xie siècle)

B

Tit for tat? EU risk-sharing and experienced reciprocity

As with previous crises, EU-wide risk-sharing has also been demanded during the Covid-19 pandemic. Yet, this crisis did not unfold in a political vacuum. Instead, public backing for EU-wide risk-sharing might have been informed by past crises experiences. Building on the idea of experienced reciprocal risk-sharing, we assume that the willingness to share risks is greater when a crisis-ridden country has also shown solidarity before, whereas readiness to cooperate may be mitigated by non-solidarity-oriented behaviour in the past. We test this assumption based on a survey experiment carried out in eleven EU countries in 2020. Our findings suggest that, when people are given information about whether another country has acted in solidarity in the past, this influences their willingness to support risk-sharing in the present. However, we also find evidence that respondents' preferences outside the experimental setting do not always match their country's recent history of reciprocal risk-sharing.As with previous crises, EU-wide risk-sharing has also been demanded during the Covid-19 pandemic. Yet, this crisis did not unfold in a political vacuum. Instead, public backing for EU-wide risk-sharing might have been informed by past crises experiences. Building on the idea of experienced reciprocal risk-sharing, we assume that the willingness to share risks is greater when a crisis-ridden country has also shown solidarity before, whereas readiness to cooperate may be mitigated by non-solidarity-oriented behaviour in the past. We test this assumption based on a survey experiment carried out in eleven EU countries in 2020. Our findings suggest that, when people are given information about whether another country has acted in solidarity in the past, this influences their willingness to support risk-sharing in the present. However, we also find evidence that respondents' preferences outside the experimental setting do not always match their country's recent history of reciprocal risk-sharing.A

Dative : loss in later Greek

B

Causal clauses

B

Data-driven virtual sensing for probabilistic condition monitoring of solenoid valves

There is an emerging industrial demand for predictive maintenance algorithms that exhibit high levels of predictive accuracy. Such condition monitoring tools must estimate dynamic quantities, such as Remaining Useful Lifetime (RUL) and the State of Health (SOH), based on a, typically, restricted set of measurements that can be obtained in an operational setting. These quantities exhibit inherent stochasticity and can only be approximately determined a posteriori to system failure. This paper proposes a generic prognostic tool for probabilistic condition monitoring of mechatronic systems, with the aim to improve the probabilistic prediction of condition metrics, specifically RUL and SOH. Therefore we propose to identify a Hidden Markov Model (HMM) from a fully instrumented measurement set, that is only available for a restricted set of run-to-failure experiments, typically gathered in an R & D setting. Although being artificial and retrospectively constructed metrics, we interpret RUL and SOH as physical measurements with the purpose to identify accurate degradation dynamics. Once the degradation model is identified, we practice the mathematical flexibility of the HMM framework to estimate several of the no longer available dynamic quantities of interest in real-time, from the limited set of measurements that are available in an operational setting. This modelling paradigm is known as virtual sensing. Predictive performance and computational efficiency are further improved by domain knowledge based pre-processing of the measurements. We apply our methodology to solenoid valves (SV), a widely used and often critical component in many industrial systems, which display a large variation in useful lifetime. Benchmark results show that the predictive capabilities of the presented methodology compares with prognostic techniques that are more computationally and memory demanding. Note to Practitioners-The motivation for this research is twofold. First there is a pending industrial need for improved diagnostic and prognostic tools. Second there is the observation that lifetime tests usually take place in an R & D setting and that expert labelling of Remaining Useful Lifetime (RUL) or State of Health (SOH) of a component or system is often based on measurement data that is not available in the industrial setting where the prognostic tools are to be deployed in the end. These two observations suggest that there is large potential in methods that can correlate the expert labelling, in particular RUL & SOH signals, with measurement data that is available in the industrial setting. Our approach has been tested in detail on the case of Solenoid Valves, which are widely used in industry and that are often safety critical. Our experiments demonstrate that the method compares with brute force approaches that overpower ours both in terms of computational as well as memory requirements. The method is furthermore generic and there is no reason to assume it would not work for other applications.There is an emerging industrial demand for predictive maintenance algorithms that exhibit high levels of predictive accuracy. Such condition monitoring tools must estimate dynamic quantities, such as Remaining Useful Lifetime (RUL) and the State of Health (SOH), based on a, typically, restricted set of measurements that can be obtained in an operational setting. These quantities exhibit inherent stochasticity and can only be approximately determined a posteriori to system failure. This paper proposes a generic prognostic tool for probabilistic condition monitoring of mechatronic systems, with the aim to improve the probabilistic prediction of condition metrics, specifically RUL and SOH. Therefore we propose to identify a Hidden Markov Model (HMM) from a fully instrumented measurement set, that is only available for a restricted set of run-to-failure experiments, typically gathered in an R & D setting. Although being artificial and retrospectively constructed metrics, we interpret RUL and SOH as physical measurements with the purpose to identify accurate degradation dynamics. Once the degradation model is identified, we practice the mathematical flexibility of the HMM framework to estimate several of the no longer available dynamic quantities of interest in real-time, from the limited set of measurements that are available in an operational setting. This modelling paradigm is known as virtual sensing. Predictive performance and computational efficiency are further improved by domain knowledge based pre-processing of the measurements. We apply our methodology to solenoid valves (SV), a widely used and often critical component in many industrial systems, which display a large variation in useful lifetime. Benchmark results show that the predictive capabilities of the presented methodology compares with prognostic techniques that are more computationally and memory demanding. Note to Practitioners-The motivation for this research is twofold. First there is a pending industrial need for improved diagnostic and prognostic tools. Second there is the observation that lifetime tests usually take place in an R & D setting and that expert labelling of Remaining Useful Lifetime (RUL) or State of Health (SOH) of a component or system is often based on measurement data that is not available in the industrial setting where the prognostic tools are to be deployed in the end. These two observations suggest that there is large potential in methods that can correlate the expert labelling, in particular RUL & SOH signals, with measurement data that is available in the industrial setting. Our approach has been tested in detail on the case of Solenoid Valves, which are widely used in industry and that are often safety critical. Our experiments demonstrate that the method compares with brute force approaches that overpower ours both in terms of computational as well as memory requirements. The method is furthermore generic and there is no reason to assume it would not work for other applications.A