London Institute for Mathematical Sciences

The advent of the Smart Home 3.0 era imposes higher technical requirements for the construction of the home ecosystem. Music is an effective means for humans to regulate their emotions. The emergence of multimodal technology facilitates the use of music for emotional regulation by older adults in their home environment. Therefore, it is necessary to carry out further research on the emotional issues of the music interaction system in smart homes that are targeted at older adult users. This study aimed to establish the mapping relationship between music parameters and emotional states based on the Valence-Arousal emotional model and through the analysis of electroencephalogram (EEG) signals. In addition, a novel Multimodal Fusion-Adaptive Wellness Interaction model was constructed to match the most suitable music and meet the emotional needs of older adult users. A total of 68 older adult participants aged between 60 and 75 years were recruited. Four groups of music stimulus materials were formulated to conduct an experimental study on the relationship between the emotions of older adult participants and music parameters. Finally, the Multimodal Fusion-Adaptive Wellness Interaction model was constructed. With the recreational sofa as the main body, multimodal, emotional intelligent adaptive interaction was realized. During the experiment, approximately 68,970,528 EEG signal data were collected. After analyzing the EEG bands and extracting the event-related potential data, the following results were obtained: For P200, the music stimulus materials showed significant differences and activation in the left parietal and occipital lobe regions (P7, P3, O1, O2; P<.001). For N400, there were significant differences and activation in the prefrontal lobe region due to the music stimulus materials (O1, CZ, O2; P<.001). For P300, the music stimulus materials led to significant differences and activation in the occipital lobe region and the central line region of the temporal area (F7, FPZ, FZ, F8; P<.001). Older adult users respond in a way that the A1 stimulus material, namely the music material with positive valence and high arousal (+V + A), can soothe the emotional state of older adult users. This provides a theoretical basis and evidence for the music interaction regulation module in smart home systems.

Therapies targeting neurodegenerative diseases via brain ventricles and spinal parenchyma face delivery challenges. Systemic administration is ineffective due to the blood-brain barrier, while direct surgical access, especially for multi-site delivery, is highly invasive. The spinal subarachnoid space offers potential for microcatheter-based delivery, but existing robotic catheter technologies are unsuitable due to spinal anatomy constraints. This paper presents a miniaturised and sensorised steerable eversion-growing robot tailored to navigation of the subarachnoid space of the spine. The property of eversion reduces interaction forces with the anatomy, rendering our approach safer than microcatheters that need to be pushed. Our system is capable of real-time tip force estimation with three degrees of freedom (DoF) using fibre Bragg gratings (FBG). Additionally, it incorporates a micro-endoscope and a steerable tip, all within a tiny 2mm outer diameter. The system’s navigation, sensing, and imaging capabilities were evaluated using a realistic up-scaled phantom of the subarachnoid space covering the cervical spine, demonstrating interaction forces within the safe range of 2-5N during phantom navigation. Comparison study of instrument-tissue interactions further approved its clinical relevance, presenting a 73.78% decrease of the mean absolute forces to traditional insertion without the sheath in global measurements.

We apply reinforcement learning (RL) to establish whether at a given position in the Coulomb branch of the moduli space of a 4d N = 2 quantum field theory (QFT) the BPS spectrum is finite. If it is, we furthermore determine the full BPS spectrum at such point in moduli space. We demonstrate that using a RL model one can efficiently determine the suitable sequence of quiver mutations of the BPS quiver that will generate the full BPS spectrum. We analyse the performance of the RL model on random BPS quivers and show that it converges to a solution various orders of magnitude faster than a systematic brute-force scan. As a result, we show that our algorithm can be used to identify all minimal chambers of a given N = 2 QFT, a task previously intractable with computer scanning. As an example, we recover all minimal chambers of the SU ( 2 ) N f = 4 gauge theory, and discover new minimal chambers for theories that can be realized by IIB geometric engineering.

A smart city is an urban area that uses technology, data and digital infrastructure to improve the quality of life for its citizens, enhance the efficiency of city services and promote sustainability. Complex networks can enable the extraction of useful information from technologies, such as the Internet of Things, artificial intelligence and big data analytics, in a comprehensive way. This would enable common urban challenges, such as traffic congestion, pollution, waste management and energy usage, to be addressed. Network theory offers a strong framework for analyzing and visualizing complex relationships in urban environments, including transportation, social interactions and infrastructure. This interdisciplinary approach aids in comprehensive city modeling and serves as a vital tool for policymakers to improve the robustness and resilience of urban landscapes. This Review explains the advances in complexity science for smart cities, showing how the logic of this field can be applied to increasingly complex features of cities.

The implementation of an embedded encoding of molecular representations reduces memory and disk usage while maintaining performance in deep learning models.

The authors introduce a mechanism to generate a bound state of two bosons with finite angular momentum, i.e. a vortex bound state. Approaches to prepare the state and measure its chirality are discussed.

<bold>Introduction:</bold> The underlying mechanisms and factors contributing to Long COVID are poorly understood. A wide variety of symptoms have been reported several months after infection by COVID-19, including breathlessness, fatigue, brain fog and many more. <bold>Methods:</bold> During the early phase of the pandemic, we sought to investigate whether a mobile application (TIYGA ecosystem) could be used to capture data about the type, intensity (scale 1-10), and frequency of specific symptoms. We aimed to understand how to record the sequence of occurrence of new symptoms over time. <bold>Results:</bold> Thirteen participants volunteered from a Charity-led Respiratory Support Group. All participants signed a consent form and created anonymised accounts. The symptoms and interventions tracked by the app were individually personalised and chosen by the participants. There was a high burden of symptoms spanning multiple organ systems, the commonest presented in Figure 1A. The app (TIYGA ecosystem) tracked individual patient’s symptoms and provided healthcare professionals (HCPs) with data regarding severity and resolution of symptoms over time (Figure 1B). <fig><object-id>erj;64/suppl_68/PA4367/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig> <bold>Conclusion:</bold> The app (TIYGA ecosystem) allows individualised monitoring on a longitudinal basis giving HCPs a rapid overview of progress and detail about changing specific symptom prevalence. This ecosystem provides a unique digital interface that may enhance communication between patients and HCPs.

ObjectiveMusic strongly modulates our autonomic nervous system. This modulation is evident in musicians' beat-to-beat heart (RR) intervals, a marker of heart rate variability (HRV), and can be related to music features and structures. We present a novel approach to modeling musicians' RR interval variations, analyzing detailed components within a music piece to extract continuous music features and annotations of musicians' performance decisions.MethodsA professional ensemble (violinist, cellist, and pianist) performs Schubert's Trio No. 2, Op. 100, Andante con moto nine times during rehearsals. RR interval series are collected from each musician using wireless ECG sensors. Linear mixed models are used to predict their RR intervals based on music features (tempo, loudness, note density), interpretive choices (Interpretation Map), and a starting factor.ResultsThe models explain approximately half of the variability of the RR interval series for all musicians, with R-squared = 0.606 (violinist), 0.494 (cellist), and 0.540 (pianist). The features with the strongest predictive values were loudness, climax, moment of concern, and starting factor.ConclusionsThe method revealed the relative effects of different music features on autonomic response. For the first time, we show a strong link between an interpretation map and RR interval changes. Modeling autonomic response to music stimuli is important for developing medical and non-medical interventions. Our models can serve as a framework for estimating performers' physiological reactions using only music information that could also apply to listeners.