Assembling real networks from synthetic and unstructured subsets: the corporate reporting case

The analysis of interfirm business transaction networks provides invaluable insight into the trading dynamics and economic structure of countries. However, there is a general scarcity of data available recording real, accurate and extensive information for these types of networks. As a result, and in common with other types of network studies - such as protein interactions for instance - research tends to rely on partial and incomplete datasets, i.e. subsets, with less certain conclusions. Hereh, we make use of unstructured financial and corporate reporting data in Japan as the base source to construct a financial reporting network, which is then compared and contrasted to the wider real business transaction network. The comparative analysis between these two rich datasets - the proxy, partially derived network and the real, complete network at macro as well as local structural levels - provides an enhanced understanding of the non trivial relationships between partial sampled subsets and fully formed networks. Furthermore, we present an elemental agent based pruning algorithm that reconciles and preserves key structural differences between these two networks, which may serve as an embryonic generic framework of potentially wider use to network research, enabling enhanced extrapolation of conclusions from partial data or subsets

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators

We address the challenge of sound propagation simulations in 3D virtual rooms with moving sources, which have applications in virtual/augmented reality, game audio, and spatial computing. Solutions to the wave equation can describe wave phenomena such as diffraction and interference. However, simulating them using conventional numerical discretization methods with hundreds of source and receiver positions is intractable, making stimulating a sound field with moving sources impractical. To overcome this limitation, we propose using deep operator networks to approximate linear wave-equation operators. This enables the rapid prediction of sound propagation in realistic 3D acoustic scenes with moving sources, achieving millisecond-scale computations. By learning a compact surrogate model, we avoid the offline calculation and storage of impulse responses for all relevant source/listener pairs. Our experiments, including various complex scene geometries, show good agreement with reference solutions, with root mean squared errors ranging from 0.02 Pa to 0.10 Pa. Notably, our method signifies a paradigm shift as no prior machine learning approach has achieved precise predictions of complete wave fields within realistic domains. We anticipate that our findings will drive further exploration of deep neural operator methods, advancing research in immersive user experiences within virtual environments.$Comment: 25 pages, 10 figures, 4 table

Constraints on the second order transport coefficients of an uncharged fluid

In this note we have tried to determine how the existence of a local entropy current with non-negative divergence constrains the second order transport coefficients of an uncharged fluid, following the procedure described in \cite{Romatschke:2009kr}. Just on symmetry ground the stress tensor of an uncharged fluid can have 15 transport coefficients at second order in derivative expansion. The condition of entropy-increase gives five relations among these 15 coefficients. So finally the relativistic stress tensor of an uncharged fluid can have 10 independent transport coefficients at second order.Comment: 43 page

Discard survival in Trammel net and Danish seine

European plaice (Pleuronectes platessa) is a key species for Danish commercial and recreational fishing. A discard ban in the reformed European Union’s Common Fisheries Policy includes the possibility of exempting from the landing obligation “species for which scientific evidence demonstrates high survival rates”. Although smaller coastal fishing vessels make up a substantial part of the commercial Danish fishing fleet, discard survival in plaice from these vessels is not well studied. To address this issue, a study on discard survival in plaice from trammel net and Danish Seine was established as a cooperation between Aalborg University, Copenhagen University and Foreningen for Skånsomt Kystfiskeri. Methodology was developed to collect, assess and observe discard survival in plaice from trammel net and Danish Seine. Experiments were conducted in 2017 and 2018 from three commercial coastal fishing vessels. Livewells were designed to house captured individuals for up to 11 days for observation of short-term survival rate. Catch-damage-index (CDI) and Reflex Action Mortality Predictor (RAMP) were used to assess fish condition immediately after capture and at the end of the observation periods. Results showed 100% survival rate in plaice from trammel net and 87% survival rate in plaice from Danish Seine. For the majority of fish assessed after capture, reflex impairments were absent and injuries were primarily minor bruises, fin fraying, and net marks. Assessments of injuries and reflex impairments after observation showed the condition of the fish generally did not worsen during the observation periods. The project is financed by the European Fisheries Fund and the Ministry of Environment and Food of Denmark

On the Energy Transfer Performance of Mechanical Nanoresonators Coupled with Electromagnetic Fields

We study the energy transfer performance in electrically and magnetically coupled mechanical nanoresonators. Using the resonant scattering theory, we show that magnetically coupled resonators can achieve the same energy transfer performance as for their electrically coupled counterparts, or even outperform them within the scale of interest. Magnetic and electric coupling are compared in the Nanotube Radio, a realistic example of a nano-scale mechanical resonator. The energy transfer performance is also discussed for a newly proposed bio-nanoresonator composed of a magnetosomes coated with a net of protein fibers.Comment: 9 Pages, 3 Figure

Identification of tightly regulated groups of genes during Drosophila melanogaster embryogenesis

Time-series analysis of whole-genome expression data during Drosophila melanogaster development indicates that up to 86% of its genes change their relative transcript level during embryogenesis. By applying conservative filtering criteria and requiring ‘sharp' transcript changes, we identified 1534 maternal genes, 792 transient zygotic genes, and 1053 genes whose transcript levels increase during embryogenesis. Each of these three categories is dominated by groups of genes where all transcript levels increase and/or decrease at similar times, suggesting a common mode of regulation. For example, 34% of the transiently expressed genes fall into three groups, with increased transcript levels between 2.5–12, 11–20, and 15–20 h of development, respectively. We highlight common and distinctive functional features of these expression groups and identify a coupling between downregulation of transcript levels and targeted protein degradation. By mapping the groups to the protein network, we also predict and experimentally confirm new functional associations

Visuohaptic augmented feedback for enhancing motor skills acquisition

Serious games are accepted as an effective approach to deliver augmented feedback in motor (re-) learning processes. The multi-modal nature of the conventional computer games (e.g. audiovisual representation) plus the ability to interact via haptic-enabled inputs provides a more immersive experience. Thus, particular disciplines such as medical education in which frequent hands on rehearsals play a key role in learning core motor skills (e.g. physical palpations) may benefit from this technique. Challenges such as the impracticality of verbalising palpation experience by tutors and ethical considerations may prevent the medical students from correctly learning core palpation skills. This work presents a new data glove, built from off-the-shelf components which captures pressure sensitivity designed to provide feedback for palpation tasks. In this work the data glove is used to control a serious game adapted from the infinite runner genre to improve motor skill acquisition. A comparative evaluation on usability and effectiveness of the method using multimodal visualisations, as part of a larger study to enhance pressure sensitivity, is presented. Thirty participants divided into a game-playing group (n = 15) and a control group (n = 15) were invited to perform a simple palpation task. The game-playing group significantly outperformed the control group in which abstract visualisation of force was provided to the users in a blind-folded transfer test. The game-based training approach was positively described by the game-playing group as enjoyable and engaging