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Creators/Authors contains: "Li, Sen"

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  1. Abstract

    It has been widely accepted that phonological awareness (PA), rapid automatized naming (RAN), and verbal short-term memory (VSTM) deficits are three core facets of phonological deficits in developmental dyslexia (DD) of alphabetic orthographies. Yet, whether these three phonological facets also represent key phonological deficits of DD in Chinese, a logographic language, has never been investigated. The current study aimed to examine profiles of phonological deficits and comorbidity in Chinese DD. We tested 128 children with DD aged between 8 and 11 years and 135 age-matched controls on 9 tasks, including 2 PA tasks (phoneme deletion and onset/rime deletion), 3 RAN tasks (digit, object and color), 2 VSTM task (spoonerisms and digit span), an orthographic awareness task (orthographic judgment), and a morphological awareness task (morphological production). With the control of morphological and orthographic awareness, results from latent profile analysis revealed three latent profiles, namely the RAN deficit group, the severe PA deficit group, and the mild VSTM deficit group. Individual analysis using a Venn plot showed that 83.59% of DD exhibited phonological deficits, among whom 58.59% with RAN deficit, 49.22% with PA deficit, and 47.66% with VSTM deficit, and all three groups shared overlap. The results have important implications for the identification and remediation of Chinese DD.

     
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    This paper considers off-street parking for the cruising vehicles of transportation network companies (TNCs) to reduce the traffic congestion. We propose a novel business that integrates the shared parking service into the TNC platform. In the proposed model, the platform (a) provides interfaces that connect passengers, drivers and garage operators (commercial or private garages); (b) determines the ride fare, driver payment, and parking rates; (c) matches passengers to TNC vehicles for ride-hailing services; and (d) matches vacant TNC vehicles to unoccupied parking garages to reduce the cruising cost. A queuing-theoretic model is proposed to capture the matching process of passengers, drivers, and parking garages. A market-equilibrium model is developed to capture the incentives of the passengers, drivers, and garage operators. An optimization-based model is formulated to capture the optimal pricing of the TNC platform. Through a realistic case study, we show that the proposed business model will offer a Pareto improvement that benefits all stakeholders, which leads to higher passenger surplus, higher drivers surplus, higher garage operator surplus, higher platform profit, and reduced traffic congestion. 
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    The value created by aggregating behind-the-meter distributed energy storage devices for grid services depends on how much storage is in the system and the power network operation conditions. To understand whether market-driven distributed storage investment will result in a socially desirable outcome, we formulate and analyze a network storage investment game. By explicitly characterizing the set of Nash equilibria (NE) for two examples, we establish that the uniqueness and efficiency of NE depend critically on the power network conditions. Furthermore, we show it is guaranteed that NE support social welfare for general power networks, provided we include two modifications in our model. These modifications suggest potential directions for regulatory interventions. 
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    Frequency regulation is crucial for balancing the supply and demand of modern electricity grids. To provide regulation services, it is important to understand the capability of flexible resources to track regulation signals. This paper studies the problem of submitting capacity bids to a forward regulation market based on historical regulation signals. We consider an aggregator who manages a group of flexible resources with linear dynamic constraints. He seeks to find the optimal capacity bid, so that real-time regulation signals can be followed with an arbitrary guaranteed probability. We formulate this problem as a chance-constrained program with unknown regulation signal distributions. A sampling and discarding algorithm is proposed. It provably provides near-optimal solutions at a guaranteed probability of success without knowing the distribution of the regulation signals. This result holds for resources with arbitrary linear dynamics and allows arbitrary intra-hour data correlations. We validate the proposed algorithm with real data via numerical simulations. Two cases are studied: (1) CAISO market, where providers separately submit capacity estimates for regulation up and regulation down signals, (2) PJM market, where regulation up and down capacities are the same. Simulation results show that the proposed algorithm provides near-optimal capacity estimates for both cases. 
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  7. Abstract

    Cell membrane coating nanotechnology, which endows nanoparticles with unique properties, displays excellent translational potential in cancer diagnosis and therapy. However, the preparation and evaluation of these cell membrane‐coated nanoparticles are based on cell lines and cell‐line‐based xenograft mouse models. The feasibility of cell membrane‐camouflaged nanomaterials is tested in a preclinical setting. Head and neck squamous cell carcinoma (HNSCC) patient‐derived tumor cell (PDTC) membranes are coated onto gelatin nanoparticles (GNPs) and the resulting PDTC@GNPs show efficient targeting to homotypic tumor cells and tissues in patient‐derived xenograft (PDX) models. When the donor‐derived cell membrane of PDTC@GNPs matched those of the host cells, significant targeting capability is observed. In contrast, mismatch between the donor and host results in weak targeting. Furthermore, it is demonstrated that autologous separation and administration of cellular membranes and anticancer cisplatin (Pt)‐loaded PDTC@GNPs, respectively, lead to almost complete tumor ablation in a subcutaneous model and effectively inhibit tumor recurrence in a postsurgery model. The work presented here reinforces the translation of these biomimetic nanoparticles for clinical applications and offers a simple, safe, and effective strategy for personalized cancer treatment.

     
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