Tongji University

About: Tongji University is a education organization based out in Shanghai, China. It is known for research contribution in the topics: Population & Adsorption. The organization has 76116 authors who have published 81176 publications receiving 1248911 citations. The organization is also known as: Tongji & Tóngjì Dàxué.

Nanocatalysts-Augmented and Photothermal-Enhanced Tumor-Specific Sequential Nanocatalytic Therapy in Both NIR-I and NIR-II Biowindows.

Abstract: The tumor microenvironment (TME) has been increasingly recognized as a crucial contributor to tumorigenesis. Based on the unique TME for achieving tumor-specific therapy, here a novel concept of photothermal-enhanced sequential nanocatalytic therapy in both NIR-I and NIR-II biowindows is proposed, which innovatively changes the condition of nanocatalytic Fenton reaction for production of highly efficient hydroxyl radicals (•OH) and consequently suppressing the tumor growth. Evidence suggests that glucose plays a vital role in powering cancer progression. Encouraged by the oxidation of glucose to gluconic acid and H2 O2 by glucose oxidase (GOD), an Fe3 O4 /GOD-functionalized polypyrrole (PPy)-based composite nanocatalyst is constructed to achieve diagnostic imaging-guided, photothermal-enhanced, and TME-specific sequential nanocatalytic tumor therapy. The consumption of intratumoral glucose by GOD leads to the in situ elevation of the H2 O2 level, and the integrated Fe3 O4 component then catalyzes H2 O2 into highly toxic •OH to efficiently induce cancer-cell death. Importantly, the high photothermal-conversion efficiency (66.4% in NIR-II biowindow) of the PPy component elevates the local tumor temperature in both NIR-I and NIR-II biowindows to substaintially accelerate and improve the nanocatalytic disproportionation degree of H2 O2 for enhancing the nanocatalytic-therapeutic efficacy, which successfully achieves a remarkable synergistic anticancer outcome with minimal side effects.

Free-floating bike sharing: Solving real-life large-scale static rebalancing problems

Abstract: Free-floating bike sharing (FFBS) is an innovative bike sharing model. FFBS saves on start-up cost, in comparison to station-based bike sharing (SBBS), by avoiding construction of expensive docking stations and kiosk machines. FFBS prevents bike theft and offers significant opportunities for smart management by tracking bikes in real-time with built-in GPS. However, like SBBS, the success of FFBS depends on the efficiency of its rebalancing operations to serve the maximal demand as possible. Bicycle rebalancing refers to the reestablishment of the number of bikes at sites to desired quantities by using a fleet of vehicles transporting the bicycles. Static rebalancing for SBBS is a challenging combinatorial optimization problem. FFBS takes it a step further, with an increase in the scale of the problem. This article is the first effort in a series of studies of FFBS planning and management, tackling static rebalancing with single and multiple vehicles. We present a Novel Mixed Integer Linear Program for solving the Static Complete Rebalancing Problem. The proposed formulation, can not only handle single as well as multiple vehicles, but also allows for multiple visits to a node by the same vehicle. We present a hybrid nested large neighborhood search with variable neighborhood descent algorithm, which is both effective and efficient in solving static complete rebalancing problems for large-scale bike sharing programs. Computational experiments were carried out on the 1 Commodity Pickup and Delivery Traveling Salesman Problem (1-PDTSP) instances used previously in the literature and on three new sets of instances, two (one real-life and one general) based on Share-A-Bull Bikes (SABB) FFBS program recently launched at the Tampa campus of University of South Florida and the other based on Divvy SBBS in Chicago. Computational experiments on the 1-PDTSP instances demonstrate that the proposed algorithm outperforms a tabu search algorithm and is highly competitive with exact algorithms previously reported in the literature for solving static rebalancing problems in SBSS. Computational experiments on the SABB and Divvy instances, demonstrate that the proposed algorithm is able to deal with the increase in scale of the static rebalancing problem pertaining to both FFBS and SBBS, while deriving high-quality solutions in a reasonable amount of CPU time.