The study proposes a differentiated approach to the localization of public services (unlike methods focusing solely on locational efficiency in the distribution of such services), with a nonlinear model that incorporates an accessibility indicator and allows rejecting solutions in which accessibility fails to comply with acceptably established minimum parameters. The method aims to minimize the total time spent by a region's population to reach a public services network, while controlling the range between the highest and lowest accessibility to the services. The resulting solution is not as efficient as other models (e.g., p-median) in relation to total cost for the population as a whole to access the system, but it seeks to prevent the most distant areas from experiencing greater difficulty due to their disproportional traveling time. The model was tested in a region in the hospital network of the State of Santa Catarina, Brazil, and the results show that incorporation of the indicator suggests improvement when compared to the current distribution of hospitals in that area. The proposed methodology can be a useful tool for planning balanced resource allocation during installation of health services for the population.
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http://dx.doi.org/10.1590/0102-311X00185615 | DOI Listing |
iScience
February 2025
ENI-G, a Joint Initiative of the University Medical Center Göttingen and the Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
Cricket song recognition is thought to evolve through modifications of a shared neural network. However, the species has an unusual recognition pattern that challenges this view: females respond to both normal male song pulse periods and periods twice as long. Of the three minimal models tested, only a single-neuron model with an oscillating membrane could explain this unusual behavior.
View Article and Find Full Text PDFHeliyon
January 2025
Faculty of Material and Manufacturing Technologies, Malek Ashtar University of Technology, P.O. Box 1774-15875, Tehran, Iran.
The potential of epoxy-graphene oxide (GO) nanocomposites to improve the mechanical characteristics of conventional epoxy resins is causing them to gain prominence. This makes them appropriate for advanced engineering applications, including structural materials, automotive, and aerospace. This study aimed to develop an epoxy/GO composite with improved mechanical properties through synthesizing epoxy/GO samples with varying GO content (from 0.
View Article and Find Full Text PDFRNA velocities and generalizations emerge as powerful approaches for extracting time-resolved information from high-throughput snapshot single-cell data. Yet, several inherent limitations restrict applying the approaches to genes not suitable for RNA velocity inference due to complex transcriptional dynamics, low expression, or lacking splicing dynamics, or data of non-transcriptomic modality. Here, we present GraphVelo, a graph-based machine learning procedure that uses as input the RNA velocities inferred from existing methods and infers velocity vectors lying in the tangent space of the low-dimensional manifold formed by the single cell data.
View Article and Find Full Text PDFAnimals survive in dynamic environments changing at arbitrary timescales, but such data distribution shifts are a challenge to neural networks. To adapt to change, neural systems may change a large number of parameters, which is a slow process involving forgetting past information. In contrast, animals leverage distribution changes to segment their stream of experience into tasks and associate them with internal task abstracts.
View Article and Find Full Text PDFUnderstanding how the collective activity of neural populations relates to computation and ultimately behavior is a key goal in neuroscience. To this end, statistical methods which describe high-dimensional neural time series in terms of low-dimensional latent dynamics have played a fundamental role in characterizing neural systems. Yet, what constitutes a successful method involves two opposing criteria: (1) methods should be expressive enough to capture complex nonlinear dynamics, and (2) they should maintain a notion of interpretability often only warranted by simpler linear models.
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