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Remote healthcare technology can help tackle societal issues by improving access to quality healthcare services and enhancing diagnoses through in-place monitoring. These services can be implemented through a combination of mobile devices, applications, wearable sensors, and other smart technology. It is paramount to handle sensitive data that is collected in ways that meet users' privacy expectations. We surveyed 384 people in Canada aged 20 to 93 years old to explore participants' comfort with data collection, sharing preferences, and potential privacy concerns related to remote healthcare technology. We explore these topics within the context of various healthcare scenarios including health emergencies and managing chronic health conditions.

We extend the formalism of Conjectural Variations games to Stackelberg games involving multiple leaders and a single follower. To solve these nonconvex games, a common assumption is that the leaders compute their strategies having perfect knowledge of the follower's best response. However, in practice, the leaders may have little to no knowledge about the other players' reactions. To deal with this lack of knowledge, we assume that each leader can form conjectures about the other players' best responses, and update its strategy relying on these conjectures. Our contributions are twofold: (i) On the theoretical side, we introduce the concept of Conjectural Stackelberg Equilibrium -- keeping our formalism conjecture agnostic -- with Stackelberg Equilibrium being a refinement of it. (ii) On the algorithmic side, we introduce a two-stage algorithm with guarantees of convergence, which allows the leaders to first learn conjectures on a training data set, and then update their strategies. Theoretical results are illustrated numerically.

In ultrathin ferromagnetic films sandwiched between two distinct heavy metal layers or between a heavy metal and an oxide layer, the Dzyaloshinskii-Moriya interaction (DMI) is recognized as being of interfacial origin. Its chirality and strength are determined by the properties of the adjacent heavy metals and the degree of oxidation at the interfaces. Here, we demonstrate that the chirality of the DMI can change solely with variations in the thickness of the ferromagnetic layer - an effect that has not been experimentally observed or explained until now. Our experimental observation in the trilayer system Ta/FeCoB/TaOx is supported by ab initio calculations: they reveal that variations in orbital filling and inter-atomic distances at the interface, driven by the number of ferromagnetic atomic layers, lead to an inversion of DMI chirality. This mechanism takes place for ferromagnetic layers with more than three atomic layers, for which the two interfaces start to be decoupled. We hence propose a new degree of freedom to tune DMI chirality and the associated chiral spin textures by tailoring crystal structure e.g. using strain or surface acoustic waves., Comment: 5 figures + supplementary materials

Dy$^{3+}$-doped silica glass whispering gallery mode microspheres are fabricated by fiber fusion splicing. They present an almost ideal spherical morphology with a radius ranging from 60 to 67 ${\mu}$m as determined by confocal laser microscopy. The host composition of the microsphere is close to that of the fiber core. The dopant Dy$^{3+}$ ions are uniformly distributed across the microsphere as evidenced by ${\mu}$-luminescence studies and present a luminescence lifetime. The Dy$^{3+}$-doped glass microspheres were excited via evanescent field coupling using a half-tapered fiber and a blue 450-nm GaN laser diode (direct pumping scheme). The yellow fluorescence of Dy$^{3+}$ ions is filtered by the whispering gallery modes (free spectral range: 0.5 nm for 67-${\mu}$m radius microsphere). The onset of stimulated emission is further observed highlighting the potential of such microresonators for narrow-linewidth light sources directly emitting visible light., Comment: 11 pages, 11 figures, journal article

This work introduces a novel methodology to assess the performance of Piezoelectric Energy Harvesters (PEHs) in order to study auxetic enhancement possibilities. For this purpose, a new approach for evaluating the intrinsic effective Electromechanical Coupling Coefficient (EMCC) of piezoelectric layers is presented. As the current assessment methods are questioned under resonance exposures, theoretical models are presented to suggest what characteristics the harvested power will depend on. A two axis graph is introduced to enable the comparison of different PEHs. The method is finally applied to PEHs with different types of substrates: filled, hollow and auxetic. First results show that, generally, auxetic structures might not increase the intrinsic EMCC but only improve the elastic energy ratio in the piezoelectric layers.

Early detection of neurocognitive disorders (NCDs) is crucial for timely intervention and disease management. Speech analysis offers a non-intrusive and scalable screening method, particularly through narrative tasks in neuropsychological assessment tools. Traditional narrative analysis often focuses on local indicators in microstructure, such as word usage and syntax. While these features provide insights into language production abilities, they often fail to capture global narrative patterns, or microstructures. Macrostructures include coherence, thematic organization, and logical progressions, reflecting essential cognitive skills potentially critical for recognizing NCDs. Addressing this gap, we propose to investigate specific cognitive and linguistic challenges by analyzing topical shifts, temporal dynamics, and the coherence of narratives over time, aiming to reveal cognitive deficits by identifying narrative impairments, and exploring their impact on communication and cognition. The investigation is based on the CU-MARVEL Rabbit Story corpus, which comprises recordings of a story-telling task from 758 older adults. We developed two approaches: the Dynamic Topic Models (DTM)-based temporal analysis to examine the evolution of topics over time, and the Text-Image Temporal Alignment Network (TITAN) to evaluate the coherence between spoken narratives and visual stimuli. DTM-based approach validated the effectiveness of dynamic topic consistency as a macrostructural metric (F1=0.61, AUC=0.78). The TITAN approach achieved the highest performance (F1=0.72, AUC=0.81), surpassing established microstructural and macrostructural feature sets. Cross-comparison and regression tasks further demonstrated the effectiveness of proposed dynamic macrostructural modeling approaches for NCD detection., Comment: 12 pages, 8 figures