Heavy quark fragmentation functions at next-to-leading perturbative QCD

It is well-known that the dominant mechanism to produce hadronic bound states with large transverse momentum is fragmentation. This mechanism is described by the fragmentation functions (FFs) which are the universal and process-independent functions. Here, we review the perturbative FFs formalism as an appropriate tool for studying these hadronization processes and detail the extension of this formalism at next-to-leading order (NLO). Using the Suzuki's model, we calculate the perturbative QCD FF for a heavy quark to fragment into a S-wave heavy meson at NLO. As an example, we study the LO and NLO FFs for a charm quark to split into the S-wave $D$-meson and compare our analytic results both with experimental data and well-known phenomenological models

Optimal performance of voltage-probe quantum heat engines

The thermoelectric performance at a given output power of a voltage-probe heat engine, exposed to an external magnetic field, is investigated in linear irreversible thermodynamics. For the model, asymmetric parameter, general figures of merit and efficiency at a given output power are analytically derived. Results show a trade-off between efficiency and output power, and we recognize optimum-efficiency values at a given output power are enhanced compared to a B\"uttiker-probe heat engine due to the presence of a characteristic parameter, namely $d_m$. Moreover, similar to a B\"uttiker-probe heat engine, the universal bounds on the efficiency are obtained, and the efficiency at a given output power can exceed the Curzon-Ahlborn limit. These findings have practical implications for the optimization of realistic heat engines and refrigerators. By controlling the values of the asymmetric parameter, the figures of merit, and $d_m$, it may be possible to design more efficient and powerful thermoelectric devices.Comment: 14 pages including 6 multi-panel figure

A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition

Recently, physiological data such as electroencephalography (EEG) signals have attracted significant attention in affective computing. In this context, the main goal is to design an automated model that can assess emotional states. Lately, deep neural networks have shown promising performance in emotion recognition tasks. However, designing a deep architecture that can extract practical information from raw data is still a challenge. Here, we introduce a deep neural network that acquires interpretable physiological representations by a hybrid structure of spatio-temporal encoding and recurrent attention network blocks. Furthermore, a preprocessing step is applied to the raw data using graph signal processing tools to perform graph smoothing in the spatial domain. We demonstrate that our proposed architecture exceeds state-of-the-art results for emotion classification on the publicly available DEAP dataset. To explore the generality of the learned model, we also evaluate the performance of our architecture towards transfer learning (TL) by transferring the model parameters from a specific source to other target domains. Using DEAP as the source dataset, we demonstrate the effectiveness of our model in performing cross-modality TL and improving emotion classification accuracy on DREAMER and the Emotional English Word (EEWD) datasets, which involve EEG-based emotion classification tasks with different stimuli