The Bragg regime of the two-particle Kapitza-Dirac effect

We analyze the Bragg regime of the two-particle Kapitza-Dirac arrangement, completing the basic theory of this effect. We provide a detailed evaluation of the detection probabilities for multi-mode states, showing that a complete description must include the interaction time in addition to the usual dimensionless parameter w. The arrangement can be used as a massive two-particle beam splitter. In this respect, we present a comparison with Hong-Ou-Mandel-type experiments in quantum optics. The analysis reveals the presence of dips for massive bosons and a differentiated behavior of distinguishable and identical particles in an unexplored scenario. We suggest that the arrangement can provide the basis for symmetrization verification schemes

Feel, Don\u27t Think Review of the Application of Neuroscience Methods for Conversational Agent Research

Conversational agents (CAs) equipped with human-like features (e.g., name, avatar) have been reported to induce the perception of humanness and social presence in users, which can also increase other aspects of users’ affection, cognition, and behavior. However, current research is primarily based on self-reported measurements, leaving the door open for errors related to the self-serving bias, socially desired responding, negativity bias and others. In this context, applying neuroscience methods (e.g., EEG or MRI) could provide a means to supplement current research. However, it is unclear to what extent such methods have already been applied and what future directions for their application might be. Against this background, we conducted a comprehensive and transdisciplinary review. Based on our sample of 37 articles, we find an increased interest in the topic after 2017, with neural signal and trust/decision-making as upcoming areas of research and five separate research clusters, describing current research trends

Poly(bromoethyl acrylate) : a reactive precursor for the synthesis of functional RAFT materials

Postpolymerization modification has become a powerful tool to create a diversity of functional materials. However, simple nucleophilic substitution reactions on halogenated monomers remains relatively unexplored. Here we report the synthesis of poly(bromoethyl acrylate) (pBEA) by reversible addition–fragmentation chain transfer (RAFT) polymerization to generate a highly reactive polymer precursor for postpolymerization nucleophilic substitution. RAFT polymerization of BEA generated well-defined homopolymers and block copolymers over a range of molecular weights. The alkylbromine-containing homopolymer and block copolymer precursors were readily substituted by a range of nucleophiles in good to excellent conversion under mild and efficient reaction conditions without the need of additional catalysts. The broad range of nucleophilic species that are compatible with this postmodification strategy enables facile synthesis of complex functionalities, from permanently charged polyanions to hydrophobic polythioethers to glycopolymers

Collaborative hyperparameter tuning

International audienceHyperparameter learning has traditionally been a manual task because of the limited number of trials. Today's computing infrastructures allow bigger evaluation budgets, thus opening the way for algorithmic approaches. Recently, surrogate-based optimization was successfully applied to hyperparameter learning for deep belief networks and to WEKA classifiers. The methods combined brute force computational power with model building about the behavior of the error function in the hyperparameter space, and they could significantly improve on manual hyperparameter tuning. What may make experienced practitioners even better at hyperparameter optimization is their ability to generalize across similar learning problems. In this paper, we propose a generic method to incorporate knowledge from previous experiments when simultaneously tuning a learning algorithm on new problems at hand. To this end, we combine surrogate-based ranking and optimization techniques for surrogate-based collaborative tuning (SCoT). We demonstrate SCoT in two experiments where it outperforms standard tuning techniques and single-problem surrogate-based optimization

Predicting color and short-circuit current of colored BIPV modules

Photovoltaic modules for façade integration should have a widely modifiable appearance to adjust to the architect’s requirements. However, architects today usually have only a limited number of already manufactured samples to choose from. Changing the color will also change the photovoltaic yield. Therefore, it would be helpful to have a procedure that allows us to determine the appearance and expected yield in advance of module fabrication. We present such a method for creating a digital prototype of a colored building integrated photovoltaic module. Using reflectance and external quantum efficiency measurements of eight colored modules, we simulate the appearance and respective energy yield for arbitrary module colors. We validate our predictions for 29 different colored modules. We use textiles that have been colored by printing and laminate them onto the modules to change the appearance of the modules. However, our digital prototyping model is also applicable to other coloring techniques. We achieve an average color difference of ΔE00 = 1.34 between predicted and measured colors, which is barely perceptible to the human eye. The predicted short-circuit current density of the digital prototype deviates on average less than 1% from the measured on

Follow Me If You Want to Live - Understanding the Influence of Human-Like Design on Users’ Perception and Intention to Comply with COVID-19 Education Chatbots

Following recommendations and complying with behavioral attitudes is one major key in overcoming global pandemics, such as COVID-19. As the World Health Organization (WHO) highlights, there is an increased need to follow hygiene standards to prevent infections and in reducing the risk of infections transmissions (World-Health-Organization, 2021). This urgent need offers new use cases of digital services, such as conversational agents that educate and inform individuals about relevant counter measurements. Specifically, due to the increased fatigue in the population in the context of COVID-19, (Franzen and Wöhner, 2021), CAs can play a vital role in supporting and attaining user’s behavior. We conducted an experiment (n=116) to analyze the effect of a human-like-design CA on the intention to comply. Our results show a significant impact of a human-like design on the perception of humanness, source credibility, and trust, which are all (directly or indirectly) drivers of the intention to comply

Numerical Modeling of c-Si PV Modules by Coupling the Semiconductor with the Thermal Conduction, Convection and Radiation Equations

Commonly, the thermal behavior of solar cell modules is calculated with analytical approaches using non wavelength-dependent optical data. Here, we employ ray tracing of entire solar modules at wavelengths of 300-2500 nm to calculate heat sources. Subsequently, finite element method (FEM) simulations are used to solve the semiconductor equations coupled with the thermal conduction, thermal convection, and thermal radiation equations. The implemented model is validated with measurements from an outdoor test over the period of an entire year. Our ray tracing analysis of different solar modules under the AM.15G spectrum shows that, for a standard module about 18.9% of the sun's intensity becomes parasitically absorbed. A loss analysis shows that the biggest parasitic heat source is the cell's full-area rear side metallization. We hence propose the use of a SiNx layer as rear side mirror to reduce the parasitic absorption to 11.7%. This change can lead to a 3.2 °C lower module operating temperature, which results in an about 5 W higher electrical power output when considering a typical 260 W module