In 2016, federal law changed state child welfare mandates related to prenatally substance-exposed infants. Little is known regarding the status or implications of policy implementation. The current study examined thematic clusters among states' policies responsive to this 2016 mandate. Cluster analysis identified four distinct categories of states' implementation: (1) "innovators/early adopters," (2) "early majority," (3) "late majority," and (4) "laggards." Innovator/early adopter states ( = 14) were most likely to have implemented plan of safe care policies consistent with Child Abuse Prevention and Treatment Act (CAPTA). Early majority states ( = 15) have started developing some aspects of CAPTA 2016 but have some aspects that are still in development. Late majority states ( = 17) have adopted few aspects of CAPTA 2016 but had implemented more CAPTA 2003 and 2010 aspects than states in the laggard cluster. Laggard states ( = 6) have implemented the fewest CAPTA prenatal substance exposure domains. In bivariate analyses, the only variable associated with clusters was Census region (e.g., New England), suggesting that states' implementation decisions may be influenced by their regional neighbors.
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Sci Rep
December 2024
School of Electrical Engineering, Vellore Institute of Technology, Chennai, 600127, India.
Spherical tanks have been predominantly used in process industries due to their large storage capability. The fundamental challenges in process industries require a very efficient controller to control the various process parameters owing to their nonlinear behavior. The current research work in this paper aims to propose the Approximate Generalized Time Moments (AGTM) optimization technique for designing Fractional-Order PI (FOPI) and Fractional-Order PID (FOPID) controllers for the nonlinear Single Spherical Tank Liquid Level System (SSTLLS).
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December 2024
Department of Production Engineering, KTH Royal Institute of Technology, 11428, Stockholm, Sweden.
This study investigates the implementation of collaborative route planning between trucks and drones within rural logistics to improve distribution efficiency and service quality. The paper commences with an analysis of the unique characteristics and challenges inherent in rural logistics, emphasizing the limitations of traditional methods while highlighting the advantages of integrating truck and drone technologies. It proceeds to review the current state of development for these two technologies and presents case studies that illustrate their application in rural logistics.
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December 2024
Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
Reservoir computing (RC) is a powerful machine learning algorithm for information processing. Despite numerous optical implementations, its speed and scalability remain limited by the need to establish recurrent connections and achieve efficient optical nonlinearities. This work proposes a streamlined photonic RC design based on a new paradigm, called next-generation RC, which overcomes these limitations.
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December 2024
Institute for Quantum Computing, University of Waterloo, Waterloo, ON, Canada.
Methods to prepare and characterize neutron helical waves carrying orbital angular momentum (OAM) were recently demonstrated at small-angle neutron scattering (SANS) facilities. These methods enable access to the neutron orbital degree of freedom which provides new avenues of exploration in fundamental science experiments as well as in material characterization applications. However, it remains a challenge to recover phase profiles from SANS measurements.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA 02215, United States.
microRNAs (miRNAs) are central post-transcriptional gene expression regulators in healthy and diseased states. Despite decades of effort, deciphering miRNA targets remains challenging, leading to an incomplete miRNA interactome and partially elucidated miRNA functions. Here, we introduce microT-CNN, an avant-garde deep convolutional neural network model that moves the needle by integrating hundreds of tissue-matched (in-)direct experiments from 26 distinct cell types, corresponding to a unique training and evaluation set of >60 000 miRNA binding events and ~30 000 unique miRNA-gene target pairs.
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