Recent studies showed that some adults with dyslexia have difficulty processing sequentially arranged information. In a companion study, this deficit manifested as low accuracy during a word pair comparison task involving same/different decisions when two words differed in their letter sequences. This sequential deficit was associated with left/right spatial letter confusion. In the present study, we found the same underlying difficulty with sequential and spatial letter processing during word spelling. Participants were the same 22 adults with dyslexia and 20 age- and gender-matched controls as in the companion study. In the spelling task, sequential error rates were higher in the dyslexia group, compared to the controls. Measures of accuracy of serial letter order during the spelling task and the word comparison task were correlated. Only three participants, each with dyslexia, produced left/right letter reversals during spelling. These were the same participants who produced left/right errors when naming single letters. They also had profound difficulty with sequential and left/right letter processing in the spelling and word comparison tasks, and they had the most severe spelling impairment. We conclude that this pervasive, persistent difficulty with sequential and spatial reversals contributes to a severe dyslexia subtype. In the dyslexia group as a whole, additional and separate sources of errors were underspecified word representations in long-term memory and homophone errors that likely represent language-based deficits in word knowledge. In the participants, these three factors (sequential/spatial letter confusion, underspecified word form representation, language-based deficits) occurred either as single factors or in combination with each other.
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http://dx.doi.org/10.1080/02699206.2020.1780322 | DOI Listing |
Comput Stat
September 2024
Department of Statistics, Purdue University, West Lafayette, IN 47907.
State estimation for large-scale non-Gaussian dynamic systems remains an unresolved issue, given nonscalability of the existing particle filter algorithms. To address this issue, this paper extends the Langevinized ensemble Kalman filter (LEnKF) algorithm to non-Gaussian dynamic systems by introducing a latent Gaussian measurement variable to the dynamic system. The extended LEnKF algorithm can converge to the right filtering distribution as the number of stages become large, while inheriting the scalability of the LEnKF algorithm with respect to the sample size and state dimension.
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January 2025
National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China.
With advancements in autonomous driving technology, the coupling of spatial paths and temporal speeds in complex scenarios becomes increasingly significant. Traditional sequential decoupling methods for trajectory planning are no longer sufficient, emphasizing the need for spatio-temporal joint trajectory planning. The Constrained Iterative LQR (CILQR), based on the Iterative LQR (ILQR) method, shows obvious potential but faces challenges in computational efficiency and scenario adaptability.
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January 2025
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China.
Water pipelines in water diversion projects can leak, leading to soil deformation and ground subsidence, necessitating research into soil deformation monitoring technology. This study conducted model tests to monitor soil deformation around leaking buried water pipelines using distributed fiber optic strain sensing (DFOSS) technology based on optical frequency domain reflectometry (OFDR). By arranging strain measurement fibers in a pipe-soil model, we investigated how leak location, leak size, pipe burial depth, and water flow velocity affect soil strain field monitoring results.
View Article and Find Full Text PDFComput Biol Med
January 2025
University of Rwanda, Rwanda. Electronic address:
Deep learning methods have significantly improved medical image analysis, particularly in detecting COVID-19 chest X-rays. Nonetheless, these methodologies frequently inhibit some drawbacks, such as limited interpretability, extensive computational resources, and the need for extensive datasets. To tackle these issues, we introduced two novel algorithms: the Dynamic Co-Occurrence Grey Level Matrix (DC-GLM) and the Contextual Adaptation Multiscale Gabor Network (CAMSGNeT).
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January 2025
College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, Guangdong 518060, P. R. China.
Efficient separation of photogenerated charge carriers is essential for maximizing the photocatalytic efficiency of semiconductor materials in oxygen evolution reactions (OER). This study presents a novel trimetallic photocatalyst, MIL-100(Fe)/TiO/CoO, synthesized through a facile microwave-assisted hydrothermal method followed by atomic layer deposition (ALD). The porous MIL-100(Fe) serves as a support for the sequential deposition of TiO and CoO layers ALD, which enhances electron-hole pair separation and minimizes their recombination.
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