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The magnetocaloric effect (MCE) in single-crystalline Eu$_5$In$_2$Sb$_6$ was studied over broad temperature ranges and magnetic field intervals, with the magnetic field oriented parallel and perpendicular to the crystallographic $\textit{c}$-axis in the orthorhombic Ca$_5$Ga$_2$As$_6$-type unit cell of the crystals. The compound is an antiferromagnetic insulator with two subsequent magnetic phase transitions at $T_{N1} \approx$ 14.0 K and $T_{N2} \approx$ 7.1 K. The compound was found to exhibit both normal and inverse MCE. Furthermore, due to substantial magnetic anisotropy, it also exhibits a rotational MCE. For conventional MCE, the maximum value of the isothermal entropy change $\Delta S_m$ is -4.7(1) J kg$^{-1}$ K$^{-1}$ at 15(1) K for a magnetic field change of 5 T, independent of the field direction. The relative cooling power (RCP) is also almost identical for both directions, and equals 81(1) J kg$^{-1}$ for a magnetic field change of 5 T. The most significant rotational MCE was observed at about 11 K, with a maximum $\Delta S_R$ value of 2.4(1) J kg$^{-1}$ K$^{-1}$ and RCP = 17.5(6) J kg$^{-1}$ for a magnetic field change of 4 T.

Modern Space Launch Vehicles (SLVs), being slender in shape and due to the use of lightweight materials, are generally flexible in nature. This structural flexibility, when coupled with sensor and actuator dynamics, can adversely affect the control of SLV, which may lead to vehicle instability and, in the worst-case scenario, to structural failure. This work focuses on modelling and simulation of rigid and flexible dynamics of an SLV and its interactions with the control system. SpaceX's Falcon 9 has been selected for this study. The flexible modes are calculated using modal analysis in Ansys. High-fidelity nonlinear simulation is developed which incorporates the flexible modes and their interactions with rigid degrees of freedom. Moreover, linearized models are developed for flexible body dynamics, over the complete trajectory until the first stage's separation. Using classical control methods, attitude controllers, that keep the SLV on its desired trajectory, are developed, and multiple filters are designed to suppress the interactions of flexible dynamics. The designed controllers along with filters are implemented in the nonlinear simulation. Furthermore, to demonstrate the robustness of designed controllers, Monte-Carlo simulations are carried out and results are presented., Comment: Presented at 20th International Bhurban Conference on Applied Sciences and Technology (IBCAST), 2023

Explainable recommender systems (RS) have traditionally followed a one-size-fits-all approach, delivering the same explanation level of detail to each user, without considering their individual needs and goals. Further, explanations in RS have so far been presented mostly in a static and non-interactive manner. To fill these research gaps, we aim in this paper to adopt a user-centered, interactive explanation model that provides explanations with different levels of detail and empowers users to interact with, control, and personalize the explanations based on their needs and preferences. We followed a user-centered approach to design interactive explanations with three levels of detail (basic, intermediate, and advanced) and implemented them in the transparent Recommendation and Interest Modeling Application (RIMA). We conducted a qualitative user study (N=14) to investigate the impact of providing interactive explanations with varying level of details on the users' perception of the explainable RS. Our study showed qualitative evidence that fostering interaction and giving users control in deciding which explanation they would like to see can meet the demands of users with different needs, preferences, and goals, and consequently can have positive effects on different crucial aspects in explainable recommendation, including transparency, trust, satisfaction, and user experience., Comment: This is an original manuscript of an article published by Taylor & Francis in the International Journal of Human-Computer Interaction on 15 Oct 2023, available online: https://doi.org/10.1080/10447318.2023.2262797

Significant attention has been paid to enhancing recommender systems (RS) with explanation facilities to help users make informed decisions and increase trust in and satisfaction with the RS. Justification and transparency represent two crucial goals in explainable recommendation. Different from transparency, which faithfully exposes the reasoning behind the recommendation mechanism, justification conveys a conceptual model that may differ from that of the underlying algorithm. An explanation is an answer to a question. In explainable recommendation, a user would want to ask questions (referred to as intelligibility types) to understand results given by the RS. In this paper, we identify relationships between Why and How explanation intelligibility types and the explanation goals of justification and transparency. We followed the Human-Centered Design (HCD) approach and leveraged the What-Why-How visualization framework to systematically design and implement Why and How visual explanations in the transparent Recommendation and Interest Modeling Application (RIMA). Furthermore, we conducted a qualitative user study (N=12) to investigate the potential effects of providing Why and How explanations together in an explainable RS on the users' perceptions regarding transparency, trust, and satisfaction. Our study showed qualitative evidence confirming that the choice of the explanation intelligibility types depends on the explanation goal and user type., Comment: 23 pages

Research on Human-Centered Learning Analytics (HCLA) has provided demonstrations of a successful co-design process for LA tools with different stakeholders. However, there is a need for 'quick and dirty' methods to allow the low-cost design of LA indicators. Recently, Indicator Specification Cards (ISC) have been proposed to help different learning analytics stakeholders co-design indicators in a systematic manner. In this paper, we aim at improving the user experience, flexibility, and reliability of the ISC-based indicator design process. To this end, we present the development details of an intuitive and theoretically-sound ISC user interface that allows the low-cost design of LA indicators. Further, we propose two approaches to support the flexible design of indicators, namely a task-driven approach and a data-driven approach., Comment: In Proceedings of 4th International Workshop on Human-Centred Learning Analytics (HCLA) co-located with the 13th International Learning Analytics and Knowledge Conference (LAK2023), Virtual, March 13, 2023

Despite the acknowledgment that the perception of explanations may vary considerably between end-users, explainable recommender systems (RS) have traditionally followed a one-size-fits-all model, whereby the same explanation level of detail is provided to each user, without taking into consideration individual user's context, i.e., goals and personal characteristics. To fill this research gap, we aim in this paper at a shift from a one-size-fits-all to a personalized approach to explainable recommendation by giving users agency in deciding which explanation they would like to see. We developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides on-demand personalized explanations of the recommendations, with three levels of detail (basic, intermediate, advanced) to meet the demands of different types of end-users. We conducted a within-subject study (N=31) to investigate the relationship between user's personal characteristics and the explanation level of detail, and the effects of these two variables on the perception of the explainable RS with regard to different explanation goals. Our results show that the perception of explainable RS with different levels of detail is affected to different degrees by the explanation goal and user type. Consequently, we suggested some theoretical and design guidelines to support the systematic design of explanatory interfaces in RS tailored to the user's context., Comment: Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (UMAP'22)

We investigate the role of a time and spin-dependent phase shift on the evolution of one-dimensional discrete-time quantum walks. By employing Floquet engineering, a time and spin-dependent phase shift ($\phi$) is imprinted onto the walker's wave function while it shifts from one lattice site to another. For a quantum walk driven by the standard protocol we show with our numerical simulations that complete revivals with equal periods occur in the probability distribution of the walk for rational values of the phase factor, i.e., $\phi/2\pi = p/q$. For an irrational value of $\phi/2\pi$ our results show partial revivals in the probability distribution with unpredictable periods, and the walker remains localized in a small region of the lattice. We further investigate revivals in a split-step quantum walk with a time and spin-dependent phase shift for rational values of $\phi/2\pi$. In contrast to the case of standard protocol, the split-step quantum walk shows partial revivals in the probability distribution. Furthermore, in view of an experimental realization we investigate the robustness of revivals against noise in the phase shift. Our results show that signatures of revivals persist for smaller values of the noise parameter, while revivals vanish for larger values. Our work is important in the context of quantum computation where quantum walks with a time and spin-dependent phase shift can be used to control and manipulate a desired quantum state on demand., Comment: 10 pages, 4 figures