External codes for multiple unicast networks via interference alignment.

We introduce a formal framework to study the multiple unicast problem for a coded network in which the network code is linear over a finite field and fixed. We show that the problem corresponds to an interference alignment problem over a finite field. In this context, we establish an outer bound for the achievable rate region and provide examples of networks where the bound is sharp.

The impact of ICT goods exports and environmental technology innovation on mineral rents: Evidence from OECD countries.

This study investigates the effects of Information and Communication Technology (ICT) goods exports and environmental technology innovation (ETI) on mineral rents using a panel dataset of 23 OECD countries from 2000 to 2020. Employing a fixed-effects regression and several robustness checks (FGLS, PCSE, and DKSE), we find that ICT goods exports are positively associated with mineral rents, while ETI exerts a negative impact. Notably, the positive effect of ICT goods exports was more pronounced in countries with higher levels of ICT goods exports.

Assembly-enhanced indocyanine green nanoparticles for fluorescence imaging-guided photothermal therapy.

The development of theranostic agents that offer complete biocompatibility, coupled with enhanced diagnostic and therapeutic performance, is crucial for fluorescence imaging-guided photothermal therapy in anti-tumor applications. However, the fabrication of nanotheranostics meeting the aforementioned requirements is challenged by concerns regarding biosafety and limited control over construction. Herein, we reported a class of fluorescence imaging-guided photothermal theranostic nanomaterials that are composed of amino acid derivatives and clinically used small photoactive indocyanine green molecules.

Optimizing wearable single-channel EEG sleep staging in a heterogeneous sleep-disordered population using transfer learning.

Study Objectives: While various wearable EEG devices have been developed, performance evaluation of the devices and their associated automated sleep stage classification models is mostly limited to healthy subjects. A major barrier for applying automated wearable EEG sleep staging in clinical populations is the need for large-scale data for model training. We therefore investigated transfer learning as strategy to overcome limited data availability and optimize automated single-channel EEG sleep staging in people with sleep disorders.

Intensifying Cyclopentanone Synthesis from Furfural Using Supported Copper Catalysts.

This work addresses catalytic strategies to intensify the synthesis of cyclopentanone, a bio-based platform chemical and a potential SAF precursor, via Cu-catalyzed furfural hydrogenation in aqueous media. When performed in a single step, using either uniform or staged catalytic bed configuration, high temperature and hydrogen pressures (180 °C and 38 bar) are necessary for maximum CPO yields (37 and 49 %, respectively). Parallel furanic ring hydrogenation of furfural and polymerisation of intermediates, namely furfuryl alcohol (FFA), limit CPO yields.

Spatiotemporal Control Over Protein Release from Artificial Cells via a Light-Activatable Protease.

The regulation of protein uptake and secretion by cells is paramount for intercellular signaling and complex multicellular behavior. Mimicking protein-mediated communication in artificial cells holds great promise to elucidate the underlying working principles, but remains challenging without the stimulus-responsive regulatory machinery of living cells. Therefore, systems to precisely control when and where protein release occurs should be incorporated in artificial cells.

Traversing chemical space with active deep learning for low-data drug discovery.

Deep learning is accelerating drug discovery. However, current approaches are often affected by limitations in the available data, in terms of either size or molecular diversity. Active deep learning has high potential for low-data drug discovery, as it allows iterative model improvement during the screening process.

A Statistical Analysis of Fluid Interface Fluctuations: Exploring the Role of Viscosity Ratio.

Understanding multiphase flow through porous media is integral to geologic carbon storage or hydrogen storage. The current modelling framework assumes each fluid present in the subsurface flows in its own continuously connected pathway. The restriction in flow caused by the presence of another fluid is modelled using relative permeability functions.

Structural behaviour and construction of a monumental ice structure.

This paper investigates the use of ice-based materials combined with Fiber-Reinforced Polymer (FRP) to enhance the bending capacity of ice sculptures. Through three-point bending tests and ABAQUS simulations, the study analyses the mechanical behaviour of GFRP and cellulose ice, both separately and together. The results show that FRP improves tensile resistance while cellulose ice adds mass and ductility.

Polymeric antibubbles with strong ultrasound imaging capabilities.

Antibubbles are liquid droplets encapsulated by a gas film that have recently been explored for on-demand ultrasound-triggered drug release. However, their ultrasound imaging capabilities are limited by their stiff shells stabilized with silica nanoparticles. Here, we develop polymeric antibubbles that generate greater ultrasound contrast than silica-based antibubbles, while showing better stability than conventional polymeric microbubbles.

Quantifying and visualising uncertainty in deep learning-based segmentation for radiation therapy treatment planning: What do radiation oncologists and therapists want?

Background And Purpose: During the ESTRO 2023 physics workshop on "AI for the fully automated radiotherapy treatment chain", the topic of deep learning (DL) segmentation was discussed. Despite its widespread use in radiotherapy, the time needed to evaluate and correct DL segmentations remains burdensome. While segmentation uncertainty could be beneficial for clinicians, there is a lack of understanding on what information should be presented to ease their task.

Does GLP-1 cause post-bariatric hypoglycemia: 'Computer says no'.

Background And Objective: Patients who underwent Roux-en-Y Gastric Bypass surgery for treatment of obesity or diabetes can suffer from post-bariatric hypoglycemia (PBH). It has been assumed that PBH is caused by increased levels of the hormone GLP-1. In this research, we elucidate the role of GLP-1 in PBH with a physiology-based mathematical model.

Pronouns reactivate conceptual representations in human hippocampal neurons.

During discourse comprehension, every new word adds to an evolving representation of meaning that accumulates over consecutive sentences and constrains the next words. To minimize repetition and utterance length, languages use pronouns, like the word "she," to refer to nouns and phrases that were previously introduced. It has been suggested that language comprehension requires that pronouns activate the same neuronal representations as the nouns themselves.

Self-Organized Patterns in Non-Reciprocal Active Droplet Systems.

Non-equilibrium patterns are widespread in nature and often arise from the self-organization of constituents through nonreciprocal chemotactic interactions. In this study, we demonstrate how active oil-in-water droplet mixtures with predator-prey interactions can result in a variety of self-organized patterns. By manipulating physical parameters, the droplet diameter ratio and number ratio, we identify distinct classes of patterns within a binary droplet system, rationalize the pattern formation, and quantify motilities.

An Ultrasensitive Molecular Detector for Direct Sensing of Spin Currents at Room Temperature.

The experimental analysis of pure spin currents at interfaces is one major goal in the field of magnonics and spintronics. Complementary to the established Spin-Hall effect using the spin-to-charge conversion in heavy metals for information processing, we present a novel approach based on spin pumping detection by an interfacial, resonantly excited molecular paramagnet adsorbed to the surface of the spin current generating magnet. Here, we show that the sensitivity of this electron paramagnetic resonance (EPR) detector can be enhanced by orders of magnitude through intramolecular transfer of spin polarization at room temperature.

Standardized assessment of evidence supporting the adoption of mobile health solutions: A Clinical Consensus Statement of the ESC Regulatory Affairs Committee: Developed in collaboration with the European Heart Rhythm Association (EHRA), the Association of Cardiovascular Nursing & Allied Professions (ACNAP) of the ESC, the Heart Failure Association (HFA) of the ESC, the ESC Young Community, the ESC Working Group on e-Cardiology, the ESC Council for Cardiology Practice, the ESC Council of Cardio-Oncology, the ESC Council on Hypertension, the ESC Patient Forum, the ESC Digital Health Committee, and the European Association of Preventive Cardiology (EAPC).

Mobile health (mHealth) solutions have the potential to improve self-management and clinical care. For successful integration into routine clinical practice, healthcare professionals (HCPs) need accepted criteria helping the mHealth solutions' selection, while patients require transparency to trust their use. Information about their evidence, safety and security may be hard to obtain and consensus is lacking on the level of required evidence.

Effect of urban environment on cardiovascular health: a feasibility pilot study using machine learning to predict heart rate variability in patients with heart failure.

Aims: Urbanization is related to non-communicable diseases such as congestive heart failure (CHF). Understanding the influence of diverse living environments on physiological variables such as heart rate variability (HRV) in patients with chronic cardiac disease may contribute to more effective lifestyle advice and telerehabilitation strategies. This study explores how machine learning (ML) models can predict HRV metrics, which measure autonomic nervous system responses to environmental attributes in uncontrolled real-world settings.

Realizing Synthetic Active Inference Agents, Part II: Variational Message Updates.

The free energy principle (FEP) describes (biological) agents as minimizing a variational free energy (FE) with respect to a generative model of their environment. Active inference (AIF) is a corollary of the FEP that describes how agents explore and exploit their environment by minimizing an expected FE objective. In two related papers, we describe a scalable, epistemic approach to synthetic AIF by message passing on free-form Forney-style factor graphs (FFGs).

Understanding the Changes in Brain Activation When Viewing Products with Differences in Attractiveness.

Product design and attractiveness are pivotal factors that determine people's positive reactions when viewing a product and may eventually affect their purchasing choices. Comprehending how people assess product design is crucial. Various studies have explored the link between product attractiveness and consumer behavior, but these were predominantly behavioral studies that offered limited insight into the neural processes underlying perceptions of product attractiveness.

Prediction of Thermochemical Properties of Long-Chain Alkanes Using Linear Regression: Application to Hydroisomerization.

Linear regression (LR) is used to predict thermochemical properties of alkanes at temperatures (0-1000) K to study chemical reaction equilibria inside zeolites. The thermochemical properties of C until C isomers reported by Scott are used as training data sets in the LR model which is used to predict these properties for alkanes longer than C isomers. Second-order groups are used as independent variables which account for the interactions between the neighboring groups of atoms.

Evaluation of magnetic resonance thermometry performance during MR-guided hyperthermia treatment of soft-tissue sarcomas in the lower extremities and pelvis.

Introduction: This study evaluated the performance of magnetic resonance thermometry (MRT) during deep-regional hyperthermia (HT) in pelvic and lower-extremity soft-tissue sarcomas.

Materials And Methods: 17 pelvic (45 treatments) and 16 lower-extremity (42 treatments) patients underwent standard regional HT and chemotherapy. Pairs of double-echo gradient-echo scans were acquired during the MR protocol 1.

Electrocardiogram-Based Deep Learning to Predict Mortality in Repaired Tetralogy of Fallot.

Background: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to predict mortality in adults with acquired cardiovascular diseases. However, its application to the growing repaired tetralogy of Fallot (rTOF) population remains unexplored.

Objectives: This study aimed to develop and externally validate an AI-ECG model to predict 5-year mortality in rTOF.

Charge injection mediated by inverse micelles in nonpolar solvents: A microscopic model.

Hypothesis: Nonpolar solvents with added charge control agents are widely used in various applications, such as E-paper displays. In spite of previous work, the mechanisms governing charge generation in nonpolar liquids, particularly those induced by electrochemical reactions at the liquid-solid interface, are not completely understood. We hypothesize that a physics-based model, according to the modified Butler-Volmer equation, can be used to quantitatively predict the injection of charges and the corresponding currents, in nonpolar solvents with surfactants.