Analysis of spatial access to healthcare services is critical for effective health resource planning. Gravity‐based spatial access models have been widely used to estimate spatial access to ...healthcare services. Among them, the floating catchment area (FCA) methods have been proved to be informative and helpful to the designation of Health Professional Shortage Areas (HPSAs). This article integrates the Huff Model with the FCA method to articulate population selection on services. Through the proposed approach, population demand on healthcare services is adjusted by a Huff Model‐based selection probability that reflects the impacts of both distance impedance and service site capacity. The new approach moderates the over‐ or under‐estimating of population demand that occurred with previous methods. Furthermore, the method uses a continuous distance impedance weight function instead of the arbitrarily defined subzones of previous studies. A case study of spatial access to primary care in Springfield, MO, showed that the proposed method can effectively moderate the population demand on service sites and therefore can generate more reliable spatial access measures.
Prostate cancer cells demonstrate a remarkable ＂addiction＂ to androgen receptor （AR） signaling in all stages of disease progression. As such, suppression of AR signaling remains the therapeutic goal ...in systemic treatment of prostate cancer. A number of molecular alterations arise in patients treated with AR-directed therapies. These molecular alterations may indicate the emergence of treatment resistance and may be targeted for the development of novel agents for prostate cancer. The presence of functional androgen receptor splice variants may represent a potential explanation for resistance to abiraterone and enzalutamide, newer AR-directed agents developed to treat metastatic castration-resistant prostate cancer （mCRPC）. In the last 8 years, many androgen receptor splice variants have been identified and characterized. Among these, androgen receptor splice variant-7 （AR-V7） has been investigated extensively. In AR-V7, the entire COOH-terminal ligand-binding domain of the canonical AR is truncated and replaced with a variant-specific peptide of 16 amino acids. Functionally, AR-V7 is capable of mediating constitutive nuclear localization and androgen receptor signaling in the absence of androgens, or in the presence of enzalutamide. In this review, we will focus on clinical translational studies involving detection/measurement of AR-V7. Methods have been developed to detect AR-V7 in clinical mCRPC specimens. AR-V7 can be reliably measured in both tissue and circulating tumor cells derived from mCRPC patients, making it possible to conduct both cross-sectional and longitudinal clinical correlative studies. Current evidence derived from studies focusing on detection of AR-V7 in mCRPC support its potential clinical utility as a treatment selection marker.
Pro-social behaviors are voluntary behaviors that benefit other people or society as a whole, such as charitable donations, cooperation, trust, altruistic punishment, and fairness. These behaviors ...have been widely described through non self-interest decision-making in behavioral experimental studies and are thought to be increased by social preference motives. Importantly, recent studies using a combination of neuroimaging and brain stimulation, designed to reveal the neural mechanisms of pro-social behaviors, have found that a wide range of brain areas, specifically the prefrontal cortex, anterior insula, anterior cingulate cortex, and amygdala, are correlated or causally related with pro-social behaviors. In this review, we summarize the research on the neural basis of various kinds of pro-social behaviors and describe a common shared neural circuitry of these pro-social behaviors. We introduce several general ways in which experimental economics and neuroscience can be combined to develop important contributions to understanding social decision-making and pro-social behaviors. Future research should attempt to explore the neural circuitry between the frontal lobes and deeper brain areas.
We focus on wireless sensor networks (WSNs) that perform data collection with the objective of obtaining the whole dataset at the sink (as opposed to a function of the dataset). In this case, ...energy-efficient data collection requires the use of data aggregation. Whereas many data aggregation schemes have been investigated, they either compromise the fidelity of the recovered data or require complicated in-network compressions. In this paper, we propose a novel data aggregation scheme that exploits compressed sensing (CS) to achieve both recovery fidelity and energy efficiency in WSNs with arbitrary topology. We make use of diffusion wavelets to find a sparse basis that characterizes the spatial (and temporal) correlations well on arbitrary WSNs, which enables straightforward CS-based data aggregation as well as high-fidelity data recovery at the sink. Based on this scheme, we investigate the minimum-energy compressed data aggregation problem. We first prove its NP-completeness, and then propose a mixed integer programming formulation along with a greedy heuristic to solve it. We evaluate our scheme by extensive simulations on both real datasets and synthetic datasets. We demonstrate that our compressed data aggregation scheme is capable of delivering data to the sink with high fidelity while achieving significant energy saving.
We report a general synthetic strategy for highly robust growth of diverse lateral heterostructures, multiheterostructures, and superlattices from two-dimensional (2D) atomic crystals. A reverse flow ...during the temperature-swing stage in the sequential vapor deposition growth process allowed us to cool the existing 2D crystals to prevent undesired thermal degradation and uncontrolled homogeneous nucleation, thus enabling highly robust block-by-block epitaxial growth. Raman and photoluminescence mapping studies showed that a wide range of 2D heterostructures (such as WS₂-WSe₂ and WS₂-MoSe₂), multiheterostructures (such as WS2-WSe2-MoS2 and WS2-MoSe2-WSe2), and superlattices (such as WS₂-WSe₂-WS₂-WSe₂-WS₂) were readily prepared with precisely controlled spatial modulation. Transmission electron microscope studies showed clear chemical modulation with atomically sharp interfaces. Electrical transport studies of WSe₂-WS₂ lateral junctions showed well-defined diode characteristics with a rectification ratio up to 10⁵.
Quantum computing offers the potential to revolutionize information processing by exploiting the principles of quantum mechanics. Among the diverse quantum bit (qubit) technologies, silicon‐based ...semiconductor spin qubits have emerged as a promising contender due to their potential scalability and compatibility with existing semiconductor technologies. In this paper, the latest developments of spin qubits in gate‐defined semiconducting nanostructures made of silicon and germanium, starting from the basic properties of electron and hole states in group‐IV semiconductors, are reviewed. Specifically, various nanostructures that exploit their unique microscopic properties for qubit implementations, elaborating on the advances and challenges in experiments, are discussed. Strategies for enhancing qubit performance, such as designing new nanostructures and identifying suitable operating points, particularly those involving the valleys of electron qubits and the heavy‐hole–light‐hole mixing of hole qubits, are also highlighted. This comprehensive review thus provides valuable insights into the current state‐of‐the‐art in semiconductor quantum computing and suggests avenues for future research.
Silicon‐based spin quantum bits (qubits) have emerged as a promising platform for large‐scale quantum computing due to their compatibility with the semiconductor fabrication technology. This review covers the latest developments of spin qubits in various gate‐defined semiconducting nanostructures made of silicon and germanium, and highlights strategies for enhancing qubit performance, such as designing new nanostructures and identifying suitable operating points.
The control of polarization and wavefront plays an important role in many optical systems. In this work, a monolayer metasurface is proposed to simultaneously realize circular asymmetric transmission ...(AT) and wavefront shaping based on asymmetric spin–orbit interactions. Circularly polarized incidence, accompanied with arbitrary wavefront modulation, experiences spin‐selected destructive or constructive interference. An extinction ratio of ≈10:1 and an AT parameter of ≈0.69 at 9.6 µm, as well as a full width half‐maximum of ≈2.9 µm (≈30% of the peak wavelength), are measured with the designed metasurface. These measured results are more than four times of those achieved with previous monolayer chiral structures. As far as it is known, this is the first report on the realization of simultaneous giant AT and arbitrary wavefront modulation with only one metasurface. Due to its fabrication simplicity and the multifunctionality of the designed metasurface, this work may provide a promising route to replace bulky cascading optical components with only one ultrathin metasurface for chiroptical spectroscopy, chiral imaging, optical communication, and so forth.
All‐dielectric metasurfaces, based on asymmetric spin–orbit interactions, are proposed to achieve giant and broadband circular asymmetric transmission, accomplished with spin‐selective wavefront shaping in transmission or reflection field. This work may have potential applications in the generation of complex optical fields and provide new ideas for studying chiral and functional materials.
•A novel deep transfer learning model is proposed for aeronautics composite materials.•An inspection method combining deep learning and sliding-window approach is explored.•It's the first application ...of transfer learning in composites' defect detection.
Composite materials are increasingly used as structural components in military and civilian aircraft. To ensure their high reliability, numerous non-destructive testing (NDT) techniques have been used to detect defects during production and maintenance. However, most of these techniques are non-automatic, with diagnostic results determined subjectively by operators. Some deep learning methods have been proposed to identify defects in images obtained through NDT, but they need labeled image samples with defects, which can be expensive or unavailable. We propose a deep transfer learning model to accurately extract features for the inclusion of defects in X-ray images of aeronautics composite materials (ACM), whose samples are scarce. We researched an automatic inclusion defect detection method for X-ray images of ACM using our proposed model. Experimental results show that the model can reach 96% classification accuracy (F1_measure) with satisfactory detection results.
Abstract
The electrochemical N
2
fixation to produce ammonia is attractive but significantly challenging with low yield and poor selectivity. Herein, we first used density function theory ...calculations to reveal adjacent bi-Ti
3+
pairs formed on anatase TiO
2
as the most active electrocatalytic centers for efficient N
2
lying-down chemisorption and activation. Then, by doping of anatase TiO
2
with Zr
4+
that has similar
d
-electron configuration and oxide structure but relatively larger ionic size, the adjacent bi-Ti
3+
sites were induced and enriched via a strained effect, which in turn enhanced the formation of oxygen vacancies. The Zr
4+
-doped anatase TiO
2
exhibited excellent electrocatalytic N
2
fixation performances, with an ammonia production rate (8.90 µg·h
−1
·cm
−2
) and a Faradaic efficiency of 17.3% at −0.45 V versus reversible hydrogen electrode under ambient aqueous conditions. Moreover, our work suggests a viewpoint to understand and apply the same-valance dopants in heterogeneous catalysis, which is generally useful but still poorly understood.
The changeable molecular dynamics of flexible polar cations in the variable confined space between inorganic chains brings about a new type of two‐step nonlinear optical (NLO) switch with genuine ...“off–on–off” second harmonic generation (SHG) conversion between one NLO‐active state and two NLO‐inactive states.
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