Nanoporous Oxides and Nanoporous Composites

Nanoporous oxides, such as cupric oxide (CuO), nickelous oxide (NiO), titanium dioxide (TiO2), cobaltosic oxide (Co3O4), and cerium oxide (CeO2), and noble-metal-based nanoporous composites, such as silver (Ag) ligaments loaded with CeO2, TiO2, zirconium dioxide (ZrO2) or NiO and palladium (Pd) ligaments loaded with TiO2 or ZrO2, are described in the chapter. Oxide-based nanoporous composites, such as Au loaded on CuO and CeO2 or platinum (Pt) loaded on TiO2, are also summarized. The structures, microstructures, and microstructure parameters of these materials are reviewed. The performance of the noble-based nanoporous composites is presented, including the catalytic oxidation of methanol and ethanol. Environmental protection applications, such as catalytic oxidation of carbon monoxide (CO) for the oxide-based nanoporous composites, have also been developed. Applications of rare earth elements in nanoporous materials are also reviewed

Zika Virus Non-structural Protein 4A Blocks the RLR-MAVS Signaling

Flaviviruses have evolved complex mechanisms to evade the mammalian host immune systems including the RIG-I (retinoic acid-inducible gene I) like receptor (RLR) signaling. Zika virus (ZIKV) is a re-emerging flavivirus that is associated with severe neonatal microcephaly and adult Guillain-Barre syndrome. However, the molecular mechanisms underlying ZIKV pathogenesis remain poorly defined. Here we report that ZIKV non-structural protein 4A (NS4A) impairs the RLR-mitochondrial antiviral-signaling protein (MAVS) interaction and subsequent induction of antiviral immune responses. In human trophoblasts, both RIG-I and melanoma differentiation-associated protein 5 (MDA5) contribute to type I interferon (IFN) induction and control ZIKV replication. Type I IFN induction by ZIKV is almost completely abolished in MAVS-/- cells. NS4A represses RLR-, but not Toll-like receptor-mediated immune responses. NS4A specifically binds the N-terminal caspase activation and recruitment domain (CARD) of MAVS and thus blocks its accessibility by RLRs. Our study provides in-depth understanding of the molecular mechanisms of immune evasion by ZIKV and its pathogenesis

Optimized Skip-Stop Metro Line Operation Using Smart Card Data

Skip-stop operation is a low cost approach to improving the efficiency of metro operation and passenger travel experience. This paper proposes a novel method to optimize the skip-stop scheme for bidirectional metro lines so that the average passenger travel time can be minimized. Different from the conventional “A/B” scheme, the proposed Flexible Skip-Stop Scheme (FSSS) can better accommodate spatially and temporally varied passenger demand. A genetic algorithm (GA) based approach is then developed to efficiently search for the optimal solution. A case study is conducted based on a real world bidirectional metro line in Shenzhen, China, using the time-dependent passenger demand extracted from smart card data. It is found that the optimized skip-stop operation is able to reduce the average passenger travel time and transit agencies may benefit from this scheme due to energy and operational cost savings. Analyses are made to evaluate the effects of that fact that certain number of passengers fail to board the right train (due to skip operation). Results show that FSSS always outperforms the all-stop scheme even when most passengers of the skipped OD pairs are confused and cannot get on the right train

Optimized Skip-Stop Metro Line Operation Using Smart Card Data

Skip-stop operation is a low cost approach to improving the efficiency of metro operation and passenger travel experience. This paper proposes a novel method to optimize the skip-stop scheme for bidirectional metro lines so that the average passenger travel time can be minimized. Different from the conventional “A/B” scheme, the proposed Flexible Skip-Stop Scheme (FSSS) can better accommodate spatially and temporally varied passenger demand. A genetic algorithm (GA) based approach is then developed to efficiently search for the optimal solution. A case study is conducted based on a real world bidirectional metro line in Shenzhen, China, using the time-dependent passenger demand extracted from smart card data. It is found that the optimized skip-stop operation is able to reduce the average passenger travel time and transit agencies may benefit from this scheme due to energy and operational cost savings. Analyses are made to evaluate the effects of that fact that certain number of passengers fail to board the right train (due to skip operation). Results show that FSSS always outperforms the all-stop scheme even when most passengers of the skipped OD pairs are confused and cannot get on the right train

Multiple Equilibrium Behaviors considering Human Exposure to Vehicular Emissions

Emissions produced by urban transportation activities are harmful to people’s health and they also affect people’s trip-making decisions. In this paper, we explore the multiple equilibrium behaviors considering human exposure to vehicular emissions. We assume that a portion of transportation users are environmental advocates and their route decisions are based on some composite cost functions comprise of a travel time component and an emission exposure component. We then study the multiple equilibrium behaviors with multiple types of users on a traffic network. The multiple equilibrium problems are further converted into variational inequality (VI) problems and they are solved using a method of successive average- (MSA-) based diagonalization method. Per the specific network setting, we find that as travelers become more concerned about their exposure to vehicular emissions, the system emission exposure, travel time, and the total cost get reduced; i.e., Pareto improving solutions are achieved. By analyzing the multiple equilibrium behaviors, we find that the system gets better if more users become environmental advocates. And the change of a small percentage of users should already lead to a good system improvement

Analyzing Capacity Utilization and Travel Patterns of Chinese High-Speed Trains: An Exploratory Data Mining Approach

Train capacity utilization (TCU), usually represented by passenger load factor (PLF), is a critical measure of effectiveness for rail operation. In literature, efforts are usually made to improve capacity utilization by optimizing rail operation and management strategies. Comparably little attention is paid to analyzing the factors that affect TCU and to understanding the behavioral patterns behind it. This paper applies exploratory data mining techniques to a 3-month long real world train operation data of the Beijing-Shanghai High-Speed Railway. Principal component analysis (PCA) is conducted to find the principal components that can efficiently represent the collected data. Clustering techniques are then applied to understand the unique characteristics that affect PLF and the travel pattern. The findings can be further used to guide train operation planning and facilitate better decision-making