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Vortex cooled thermoplastic chamber thruster for potential application to polymer based 3D printed space propulsion systems
Is gravity quantum? Experiments could finally probe one of physics’ biggest questions
Biobank-scale genetic characterization of Alzheimer’s disease and related dementias across diverse ancestries
Abstract Alzheimer’s disease and related dementias (AD/ADRDs) pose a significant global public health challenge. To effectively implement personalized therapeutic interventions on a global scale, it is essential to identify disease-causing, risk, and resilience factors across diverse ancestral backgrounds. This study leveraged biobank-scale data to conduct a large multi-ancestry whole-genome sequencing characterization of AD/ADRDs. We thoroughly explored the role of protein-coding and splicing variants from key genes associated with AD/ADRDs across 11 ancestries, utilizing data from five distinct biobanks, including a total of 25,001 cases and 93,542 controls. We compiled the most extensive catalog of known and novel genetic variation in AD/ADRDs in a global context, providing clinical insights into their genetic-phenotypic correlations. A thorough assessment of APOE revealed ancestry-driven modulation of APOE-associated AD/ADRDs, as well as disease-modifying effects conferred by several variants among APOE ε4 carriers. Finally, we present an accessible and user-friendly platform to support future ADRD research (https://niacard.shinyapps.io/MAMBARD_browser/).
Carrion from large carnivores and food from humans subsidize mesocarnivores year round
Abstract The return of large carnivores is predicted to suppress meso-carnivores, though it has only been observed in a minority of contemporary studies. Isle Royale is a remote island wilderness in Lake Superior, USA, managed for outdoor recreation. Following their natural extirpation, the National Parks Service translocated gray wolves (Canis lupus) in 2018–2019 and we expected the return of suppression and trophic facilitation of meso-carnivores. Nevertheless, we hypothesized that human resource subsidies led to a breakdown in meso-predator release. From Fall 2021-Winter 2024, we captured 16 individual red foxes (Vulpes vulpes), collected whiskers, and subsampled them to recreate the yearly diet of the population using stable isotope analysis. Notably, meso-carnivores were not demographically suppressed. Coinciding with the dates when Isle Royale National Park opened, fox diets were generalized, dissimilar between individuals, and primarily composed of human foods (0.28 ± 0.02) during summer. In winter, fox diets were specialized and exhibited high similarity, being composed primarily of carrion subsidized by wolves (0.62 ± 0.04). We propose that the complementarity of human resource subsidies to communities and ecosystems, broadly, may help explain the limited reach of the anticipated interactions following the return of large carnivores, such as meso-predator release.
Photophoretic flight of perforated structures in near-space conditions
Halogen substitution strategy of spacer cations in two-dimensional perovskite ferroelectrics gives giant anomalous photovoltaic effect
The role of loneliness, work meaning and organizational support in compassion satisfaction among primary care professionals during the pandemic
Bending the curve of land degradation to achieve global environmental goals
A decision-space model explains context-specific decision-making
Abstract Optimal decision-making requires consideration of internal and external contexts. Biased decision-making is a transdiagnostic symptom of neuropsychiatric disorders. We created a computational model demonstrating how the striosome compartment of the striatum constructs a context-dependent mathematical space for decision-making computations, and how the matrix compartment uses this space to define action value. The model explains multiple experimental results and unifies other theories like reward prediction error, roles of the direct versus indirect pathways, and roles of the striosome versus matrix, under one framework. We also found, through new analyses, that striosome and matrix neurons increase their synchrony during difficult tasks, caused by a necessary increase in dimensionality of the space. The model makes testable predictions about individual differences in disorder susceptibility, decision-making symptoms shared among neuropsychiatric disorders, and differences in neuropsychiatric disorder symptom presentation. The model provides evidence for the central role that striosomes play in neuroeconomic and disorder-affected decision-making.
Feature selection for specific prediction targets at the user level in a district heating network
Abstract With the challenge of district heating network transition as part of the global objective of clean energy, machine learning provides a methodological approach for understanding the relationships between various influencing factors and demand-side properties of district heating networks, which is decisive for reducing losses, enhancing sustainability, and guaranteeing residential comfort. This work focuses on accelerating the application of modern machine learning methods to modeling district heating networks by generating knowledge on feature engineering and selection for newly suggested prediction targets, namely volume flow, supply, and return temperatures, directly at the building level. A systematic workflow for data acquisition, feature engineering, and selecting the most relevant predictors is presented. For this, statistical and machine learning methods are applied to engineer respective features and establish specific interdependencies, including meteorological conditions, human behavioral patterns, and operational parameters, based on a model region in northern Germany. The qualitative results indicate that the highest impact is for temporal predictors and operational features derived from the infeed facility’s data, i.e., approximately 15 to 20% of the total predictor relevance. In comparison to studies targeting the heat load and suggesting outside air temperature as the most relevant predictor, it was found that for the herein proposed prediction targets, this feature is of secondary relevance (roughly 6–10%). The findings of this study provide a feature engineering and selection strategy, as well as relevant knowledge gain, which is a prerequisite for efficient modeling of district heating networks based on machine learning in the future.
Overcoming five key challenges to make the energy transition a just labor transition
Analysis of the size effect in the nanoscratching process of single crystalline and polycrystalline materials
Dysregulation of GTPase-activating protein-binding protein1 in the pathogenesis of metabolic dysfunction-associated steatotic liver disease
A forewarning model for the reverse supply chain of urban End-of-Life power batteries based on a mix method of BWM and RBFNN
Efficient fibre-pigtailed source of indistinguishable single photons
Abstract Semiconductor quantum dots in microcavities are an excellent platform for the efficient generation of indistinguishable single photons. However, their use in a wide range of quantum technologies requires their controlled fabrication and integration in compact closed-cycle cryocoolers, with a key challenge being the efficient and stable extraction of the single photons into a single-mode fibre. Here we report on a method for the fibre-pigtailing of deterministically fabricated single-photon sources. Our technique allows for nanometre-scale alignment accuracy between the source and a fibre, alignment that persists all the way from room temperature to 2.4 K. We demonstrate high performance of the device under near-resonant optical excitation with a photon indistinguishability of 97.5 % and a brightness at the output fibre of the system of 20.8 %. We show that the indistinguishability and single-photon rate are stable for over ten hours of continuous operation in a single cooldown. We further confirm that the device performance is not degraded by nine successive cooldown-warmup cycles.
Role of ethanol to water ratio in hydrogen production by ethanol steam reforming in atmospheric pressure 915 MHz microwave plasma
Fibrous network nature of plant cell walls enables tunable mechanics for development
Abstract During plant development, the mechanical properties of the cell walls must be tuned to regulate the growth of the cells. Cell growth involves significant stretching of the cell walls, yet little is known about the mechanical properties of cell walls under such substantial deformation, or how these mechanical properties change to regulate development. Here, we investigated the mechanical behavior of the Arabidopsis leaf epidermal cells being stretched. We found that the mechanical properties arise from the cell wall, which behaves as a fibrous network material. The epidermis exhibited a non-linear stiffening behavior that fell into three regimes. Each regime corresponded to distinct nonlinear behaviors in terms of transverse deformation (i.e., Poisson effect) and unrecoverable deformation (i.e., plasticity). Using a model, we demonstrated that the transition from reorientation and bending-dominated to stretch-dominated deformation modes of cellulose microfibrils cause these nonlinear behaviors. We found the stiffening behavior is more pronounced at later developmental stages. Finally, we show the spiral2-2 mutant has anisotropic mechanical properties, likely contributing to the spiraling of leaves. Our findings reveal the fibrous network nature of cell walls gives a high degree of tunability in mechanical properties, which allows cells to adjust these properties to support proper development.
Thermophotoinduced electron emission from conductive composite based on polytetrafluoroethylene with carbon nanotubes
Abstract This work explores the potential of conductive polymer–carbon nanocomposites, specifically polytetrafluoroethylene with multi-walled carbon nanotubes (PTFE–CNTs), as efficient electron emitters for emission electronics, low-temperature thermionic energy conversion (TEC), sensors, elements of information storage devices, and materials with targeted control of electromagnetic waves’ absorption/reflection. Our main scientific contribution is the demonstration of electron emission from investigated composite PTFE + 10 wt% CNTs at significantly reduced operating temperatures (near 200 °C), much lower than those for pure CNTs and conventional materials for emission electronics and energy. The research combines experimental studies of electron emission under concentrated solar and pulsed laser radiation, structural characterization of samples by electron microscopy, and positron spectroscopy. The last method and contact potential difference method are used to investigate the electronic properties, including charge transfer between composite components, and the work function of the material. Results indicate that electrons can be emitted from both the surface and subsurface (through the polymer layer) of CNTs. The emission current can be enhanced by the Schottky effect due to the electrical fields of adsorbed cations. Additionally, a novel method for tuning the composite’s work function due to irradiation by low-energy electrons is proposed. It opens pathways for targeted control not only of emission performance but other electronic properties of polymer–carbon nanocomposites, including absorption and reflection of electromagnetic waves in a wide frequency range.