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Electrostatic potentials of atomic nanostructures at metal surfaces quantified by scanning quantum dot microscopy
Get PDFThe discrete and charge-separated nature of matter — electrons and nuclei — results in local electrostatic fields that are ubiquitous in nanoscale structures and relevant in catalysis, nanoelectronics and quantum nanoscience. Surface-averaging techniques provide only limited experimental access to these potentials, which are determined by the shape, material, and environment of the nanostructure. Here, we image the potential over adatoms, chains, and clusters of Ag and Au atoms assembled on Ag(111) and quantify their surface dipole moments. By focusing on the total charge density, these data establish a benchmark for theory. Our density functional theory calculations show a very good agreement with experiment and allow a deeper analysis of the dipole formation mechanisms, their dependence on fundamental atomic properties and on the shape of the nanostructures. We formulate an intuitive picture of the basic mechanisms behind dipole formation, allowing better design choices for future nanoscale systems such as single-atom catalysts
A novel methodology for investigating the through-thickness molten pool shape during remote laser beam welding
Get PDFMolten pool shape can retrieve information on thermal gradients and solidification rates which provide substantial process signatures on cracking and weld quality. However, monitoring the molten pool shape remains a challenging task due to the complex nature of the laser welding process. The current study employs a novel monitoring methodology to examine the role of through-thickness molten pool shape in solidification cracking in partially penetrated welds. A Coherent ARM (Adjustable Ring mode) fibre laser, featuring independent control of the core and ring beam with the ratio of respective beam power specified by power ratio, was employed to study different molten pool shapes. Experimental investigations further include welding of aluminium alloy and quartz glass in butt configuration with a high-speed imaging system facing the longitudinal cross-section of the weld. The molten pool shape was evaluated using the developed image processing algorithm and the curvature analysis. Furthermore, ad-hoc Digital Image Correlation (DIC) was used to examine the thermal strain development in the through-thickness direction. Results showed that the proposed methodology could provide an accurate detection of the through-thickness molten pool shape with an improved accuracy of 94.3 % compared to the advanced prior models available in the literature, with an accuracy of 30.23 % and 42.5 %. It also revealed a tail-like feature in the molten pool's rear end, which influences the crystallisation paths and facilitates premature solidification, leading to greater tensile strains during solidification. The increments in power ratio from 0.36 to 1.5 reduced the tail-like feature and reduced the shrinkage strains in the fusion zone
Flight of the bumblebee : the early excess flux of Type Ia supernova 2023bee revealed by TESS, Swift, and Young Supernova Experiment observations
Get PDFWe present high-cadence ultraviolet through near-infrared observations of the Type Ia supernova (SN Ia) 2023bee at D = 32 ± 3 Mpc, finding excess flux in the first days after explosion, particularly in our 10 minutes cadence TESS light curve and Swift UV data. Compared to a few other normal SNe Ia with early excess flux, the excess flux in SN 2023bee is redder in the UV and less luminous. We present optical spectra of SN 2023bee, including two spectra during the period where the flux excess is dominant. At this time, the spectra are similar to those of other SNe Ia but with weaker Si ii, C ii, and Ca ii absorption lines, perhaps because the excess flux creates a stronger continuum. We compare the data to several theoretical models on the origin of early excess flux in SNe Ia. Interaction with either the companion star or close-in circumstellar material is expected to produce a faster evolution than observed. Radioactive material in the outer layers of the ejecta, either from double detonation explosion or from a 56Ni clump near the surface, cannot fully reproduce the evolution either, likely due to the sensitivity of early UV observable to the treatment of the outer part of ejecta in simulation. We conclude that no current model can adequately explain the full set of observations. We find that a relatively large fraction of nearby, bright SNe Ia with high-cadence observations have some amount of excess flux within a few days of explosion. Considering potential asymmetric emission, the physical cause of this excess flux may be ubiquitous in normal SNe Ia
Intelligent non-cooperative optical networks : leveraging scattering neural networks with small training data
Get PDFArtificial intelligence (AI) is enabling intelligent communications where learning based signal classification simplifies optical network signal allocation and shifts signal processing pressure to each network edge. This work proposes a non-orthogonal signal waveform framework that leverages its unique spectral compression characteristic as a user address for efficiently forwarding messages to target users. The primary focus of this work lies in the physical layer intelligent receiver design, which can automatically identify different received signal formats without preamble notification in a non-cooperative communication approach. Traditional signal classification methods, such as convolutional neural network (CNN), rely on extensive training, resulting in a heavy dependency on large training datasets. To overcome this limitation, this work designs a specific two-layer scattering neural network that can accurately separate signals even when the training data is limited, leading to reduced training complexity. Its performance remains robust in diverse transmission conditions. Furthermore, the scattering neural network is interpretable because features are extracted based on deterministic wavelet filters rather than training based filters
A self-attention deep neural network regressor for real time blood glucose estimation in paediatric population using physiological signals
Get PDFWith the advent of modern digital technology, the physiological signals (such as electrocardiogram) are being acquired from portable wearable devices which are being used for non-invasive chronic disease management (such as Type 1 Diabetes). The diabetes management requires real-time assessment of blood glucose which is cumbersome for paediatric population due to clinical complexity and invasiveness. Therefore, real-time non-invasive blood glucose estimation is now pivotal for effective diabetes management.
In this paper, we propose a Self-Attention Deep Neural Network Regressor for real-time non-invasive blood glucose estimation for paediatric population based on automatically extracted beat morphology. The first stage performs Morphological Extractor based on Self-Attention based Long Short-Term Memory driven by Convolutional Neural Network for highlighting local features based on temporal context. The second stage is based on Morphological Regressor driven by multilayer perceptron with dropout and batch normalization to avoid overfitting. We performed feature selection via logit model followed by Spearman’s correlation among features to avoid feature redundancy. We trained as tested our model on publicly available MIT/BIH-Physionet databases and physiological signals acquired from a T1D paediatric population
Protoplanetary and debris disks in the η Chamaeleontis association : a submillimeter survey obtained with APEX/LABOCA observations
Get PDFContext. Nearby associations are ideal regions to study coeval samples of protoplanetary and debris disks down to late M-type stars. Those aged 5–10 Myr, where most of the disk should have already dissipated forming planets, are of particular interest.
Aims. We present the first complete study of both protoplanetary and debris disks in a young region, using the η Chamaeleontis (η Cha) association as a test bench to study the cold disk content. We obtained submillimeter data for the entire core population down to late M-type stars, plus a few halo members.
Methods. We performed a continuum submillimeter survey with APEX/LABOCA of all the core populations of the η Cha association. These data were combined with archival multiwavelength photometry to compile a complete spectral energy distribution. The disk properties were derived by modeling protoplanetary and debris disks using RADMC 2D and DMS, respectively. We compute a lower limit of the disk millimeter fraction, which is then compared to the corresponding disk fraction in the infrared for η Cha. We also revisit and refine the age estimate for the region, using the Gaia eDR3 astrometry and photometry for the core sources.
Results. We find that protoplanetary disks in η Cha typically have holes with radii on the order of 0.01–0.03 AU, while ring-like emission from the debris disks is located between 20 and 650 au from the central star. The parallaxes and Gaia eDR3 photometry, in combination with the PARSEC and COLIBRI isochrones, enable us to confirm an age of η Cha between 7 and 9 Myr. In general, the disk mass seems insufficient to support accretion over a long time, even for the lowest mass accretors, a clear difference with other regions and also a sign that the mass budget is further underestimated. We do not find a correlation between the stellar masses, accretion rates, and disk masses, although this could be due to sample issues (very few, mostly low-mass objects). We confirm that the presence of inner holes is not enough to stop accretion unless accompanied by dramatic changes to the total disk mass content. Comparing η Cha with other regions at different ages, we find that the physical processes responsible for debris disks (e.g., dust growth, dust trapping) efficiently act in less than 5 Myr
Developing interdisciplinary learning : spanning disciplinary and organizational boundaries
Get PDFBased on a study of a postgraduate course, we show how—through the processes associated with applying a strategic tool—students developed the understandings that allowed them to span disciplinary and organizational boundaries. We reveal how the students, working in groups and acting as consultants to industry clients, developed specific boundary-spanning skills learned through observation and practice (mimesis), and reflection. Namely, (1) working with others with different disciplines to establish roles and processes to operate successfully as a group, (2) establishing productive communication with other groups of diverse disciplines as part of project processes, (3) eliciting information from other groups of diverse specialists, and (4) managing an inclusive discussion process among other groups of diverse specialists for agreement. We discuss how these insights about mimesis and reflection add to pedagogic debates about instruction for interdisciplinary and inter-organizational learning and the implications for management education and development practice
Wind turbine fault-tolerant control via incremental model-based reinforcement learning
Get PDFA reinforcement learning (RL) based fault-tolerant control strategy is developed in this paper for wind turbine torque & pitch control under actuator & sensor faults subject to unknown system models. An incremental model-based heuristic dynamic programming (IHDP) approach, along with a critic-actor structure, is designed to enable fault-tolerance capability and achieve optimal control. Particularly, an incremental model is embedded in the critic-actor structure to quickly learn the potential system changes, such as faults, in real-time. Different from the current IHDP methods that need the intensive evaluation of the state and input matrices, only the input matrix of the incremental model is dynamically evaluated and updated by an online recursive least square estimation procedure in our proposed method. Such a design significantly enhances the online model evaluation efficiency and control performance, especially under faulty conditions. In addition, a value function and a target critic network are incorporated into the main critic-actor structure to improve our method’s learning effectiveness. Case studies for wind turbines under various working conditions are conducted based on the fatigue, aerodynamics, structures, and turbulence (FAST) simulator to demonstrate the proposed method’s solid fault-tolerance capability and adaptability. Note to Practitioners —This work achieves high-performance wind turbine control under unknown actuator & sensor faults. Such a task is still an open problem due to the complexity of turbine dynamics and potential uncertainties in practical situations. A novel data-driven and model-free control strategy based on reinforcement learning is proposed to handle these issues. The designed method can quickly capture the potential changes in the system and adjust its control policy in real-time, rendering strong adaptability and fault-tolerant abilities. It provides data-driven innovations for complex operational tasks of wind turbines and d..
Analysis and optimization of equitable US cancer clinical trial center access by travel time
Get PDFImportance: Racially minoritized and socioeconomically disadvantaged populations are currently underrepresented in clinical trials. Data-driven, quantitative analyses and strategies are required to help address this inequity.
Objective: To systematically analyze the geographical distribution of self-identified racial and socioeconomic demographics within commuting distance to cancer clinical trial centers and other hospitals in the US.
Design, Setting, and Participants: This longitudinal quantitative study used data from the US Census 2020 Decennial and American community survey (which collects data from all US residents), OpenStreetMap, National Cancer Institute–designated Cancer Centers list, Nature Index of Cancer Research Health Institutions, National Trial registry, and National Homeland Infrastructure Foundation-Level Data. Statistical analyses were performed on data collected between 2006 and 2020.
Main Outcomes and Measures: Population distributions of socioeconomic deprivation indices and self-identified race within 30-, 60-, and 120-minute 1-way driving commute times from US cancer trial sites. Map overlay of high deprivation index and high diversity areas with existing hospitals, existing major cancer trial centers, and commuting distance to the closest cancer trial center.
Results: The 78 major US cancer trial centers that are involved in 94% of all US cancer trials and included in this study were found to be located in areas with socioeconomically more affluent populations with higher proportions of self-identified White individuals (+10.1% unpaired mean difference; 95% CI, +6.8% to +13.7%) compared with the national average. The top 10th percentile of all US hospitals has catchment populations with a range of absolute sum difference from 2.4% to 35% from one-third each of Asian/multiracial/other (Asian alone, American Indian or Alaska Native alone, Native Hawaiian or Other Pacific Islander alone, some other race alone, population of 2 or more races), Black or African American, and White populations. Currently available data are sufficient to identify diverse census tracks within preset commuting times (30, 60, or 120 minutes) from all hospitals in the US (N = 7623). Maps are presented for each US city above 500 000 inhabitants, which display all prospective hospitals and major cancer trial sites within commutable distance to racially diverse and socioeconomically disadvantaged populations.
Conclusion and Relevance: This study identified biases in the sociodemographics of populations living within commuting distance to US-based cancer trial sites and enables the determination of more equitably commutable prospective satellite hospital sites that could be mobilized for enhanced racial and socioeconomic representation in clinical trials. The maps generated in this work may inform the design of future clinical trials or investigations in enrollment and retention strategies for clinical trials; however, other recruitment barriers still need to be addressed to ensure racial and socioeconomic demographics within the geographical vicinity of a clinical site can translate to equitable trial participant representation