A decision support system to improve service quality in multimodal rapid rail systems: A bayesian perspective

Copyright @ 2011 International Conference on Computers and Industrial EngineeringIn this study, the accessibility of the rail transit stations in a multimodal network formed by a trunk line and its feeder lines are defined. Connectivity between lines and the accessibility of the nodes identify the overall spatial structure of the network. The factors influencing the access choices of rail transit stations and satisfaction of transit travelers in rapid rail transit systems are investigated in order to gain insights into the factors and their interrelationships. The quantitative indications of the relationships are produced and the complexity of evaluating the performance of transit services is exhibited. As the interrelationships are mainly stochastic, the problem on hand is treated as a Bayesian Belief Network (BBN). A BBN approach that presents a learning mechanism is employed and is used as an alternative decision making tool to analyze the rapid rail transit services and identify policies to improve the traveler’s level of service

High-order harmonic generation from Rydberg states at fixed Keldysh parameter

Because the commonly adopted viewpoint that the Keldysh parameter $\gamma$ determines the dynamical regime in strong field physics has long been demonstrated to be misleading, one can ask what happens as relevant physical parameters, such as laser intensity and frequency, are varied while $\gamma$ is kept fixed. We present results from our one- and fully three-dimensional quantum simulations of high-order harmonic generation (HHG) from various bound states of hydrogen with $n$ up to 40, where the laser intensities and the frequencies are scaled from those for $n=1$ in order to maintain a fixed Keldysh parameter $\gamma$$< 1$ for all $n$. We find that as we increase $n$ while keeping $\gamma$ fixed, the position of the cut-off scales in well defined manner. Moreover, a secondary plateau forms with a new cut-off, splitting the HHG plateau into two regions. First of these sub-plateaus is composed of lower harmonics, and has a higher yield than the second one. The latter extends up to the semiclassical $I_p+3.17U_p$ cut-off. We find that this structure is universal, and the HHG spectra look the same for all $n\gtrsim 10$ when plotted as a function of the scaled harmonic order. We investigate the $n$-, $l$- and momentum distributions to elucidate the physical mechanism leading to this universal structure

Phase-dependent interference fringes in the wavelength scaling of harmonic efficiency

We describe phase-dependent wavelength scaling of high-order harmonic generation efficiency driven by ultra-short laser fields in the mid-infrared. We employ both numerical solution of the time-dependent Schr\"{o}dinger equation and the Strong Field Approximation to analyze the fine-scale oscillations in the harmonic yield in the context of channel-closing effects. We show, by varying the carrier-envelope phase, that the amplitude of these oscillations depend strongly on the number of returning electron trajectories. Furthermore, the peak positions of the oscillations vary significantly as a function of the carrier-envelope phase. Owing to its practical applications, we also study the wavelength dependence of harmonic yield in the "single-cycle" limit, and observe a smooth variation in the wavelength scaling originating from the vanishing fine-scale oscillations.Comment: 5 pages, 4 figure

Design and low-power implementation of an adaptive image rejection receiver

This paper deals with and details the design and implementation of a low-power; hardware-efficient adaptive self-calibrating image rejection receiver based on blind-source-separation that alleviates the RF analog front-end impairments. Hybrid strength-reduced and re-scheduled data-flow, low-power implementation of the adaptive self-calibration algorithm is developed and its efficiency is demonstrated through simulation case studies. A behavioral and structural model is developed in Matlab as well as a low-level architectural design in VHDL providing valuable test benches for the performance measures undertaken on the detailed algorithms and structures

A potential therapeutic role in multiple sclerosis for stigmast-5,22-dien-3 beta-ol myristate isolated from Capparis ovata

Multiple sclerosis (MS) is an autoimmune disease of the human central nervous system. It is one of the most common neurological disorders around the world and there is still no complete cure for MS. Purification of a terpenoid from Capparis ovata was carried out and its structure was elucidated as stigmast-5,22-dien-3 beta-ol, myristate (3 beta, 22E-stigmasteryl myristate; SDM) by NMR and mass spectral analyses. No information regarding its any health effect is available in the literature. In the present study, we have described its effects on inflammatory factors such as the expression levels of cytokines, chemokines and adhesion molecules as well as apoptosis/infiltration and myelination in SH-SY5Y cells. The expression levels of proinflammatory or inflammatory cytokines and chemokines such as NF-.B1, CCL5, CXCL9, CXCL10 and HIF1A along with T-cell activating cytokines such as IL-6 and TGFB1 were significantly downregulated with SDM treatment. Moreover, the expression levels of the main myelin proteins such as MBP, MAG and PLP that are essential for healthy myelin architecture were significantly up-regulated. The results presented in this study strongly suggest that the SDM offers a unique possibility to be used with autoimmune diseases, including MS due to its activity on the manipulation of cytokines and the promotion of myelin formation

A new perspective on the competitiveness of nations

The capability of firms to survive and to have a competitive advantage in global markets depends on, amongst other things, the efficiency of public institutions, the excellence of educational, health and communications infrastructures, as well as on the political and economic stability of their home country. The measurement of competitiveness and strategy development is thus an important issue for policy-makers. Despite many attempts to provide objectivity in the development of measures of national competitiveness, there are inherently subjective judgments that involve, for example, how data sets are aggregated and importance weights are applied. Generally, either equal weighting is assumed in calculating a final index, or subjective weights are specified. The same problem also occurs in the subjective assignment of countries to different clusters. Developed as such, the value of these type indices may be questioned by users. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices. For this purpose, a methodology composed of three steps is proposed. To start, a hierarchical clustering analysis is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient for purposes of country clustering. In the proposed methodology, 178 criteria are used for this purpose. Next, relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in our third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one

Collaborative database to track Mass Mortality Events in the Mediterranean Sea

Anthropogenic climate change, and global warming in particular, has strong and increasing impacts on marine ecosystems (Poloczanska et al., 2013; Halpern et al., 2015; Smale et al., 2019). The Mediterranean Sea is considered a marine biodiversity hotspot contributing to more than 7% of world\u2019s marine biodiversity including a high percentage of endemic species (Coll et al., 2010). The Mediterranean region is a climate change hotspot, where the respective impacts of warming are very pronounced and relatively well documented (Cramer et al., 2018). One of the major impacts of sea surface temperature rise in the marine coastal ecosystems is the occurrence of mass mortality events (MMEs). The first evidences of this phenomenon dated from the first half of \u201980 years affecting the Western Mediterranean and the Aegean Sea (Harmelin, 1984; Bavestrello and Boero, 1986; Gaino and Pronzato, 1989; Voultsiadou et al., 2011). The most impressive phenomenon happened in 1999 when an unprecedented large scale MME impacted populations of more than 30 species from different phyla along the French and Italian coasts (Cerrano et al., 2000; Perez et al., 2000). Following this event, several other large scale MMEs have been reported, along with numerous other minor ones, which are usually more restricted in geographic extend and/or number of affected species (Garrabou et al., 2009; Rivetti et al., 2014; Marb\ue0 et al., 2015; Rubio-Portillo et al., 2016, authors\u2019 personal observations). These events have generally been associated with strong and recurrent marine heat waves (Crisci et al., 2011; Kersting et al., 2013; Turicchia et al., 2018; Bensoussan et al., 2019) which are becoming more frequent globally (Smale et al., 2019). Both field observations and future projections using Regional Coupled Models (Adloff et al., 2015; Darmaraki et al., 2019) show the increase in Mediterranean sea surface temperature, with more frequent occurrence of extreme ocean warming events. As a result, new MMEs are expected during the coming years. To date, despite the efforts, neither updated nor comprehensive information can support scientific analysis of mortality events at a Mediterranean regional scale. Such information is vital to guide management and conservation strategies that can then inform adaptive management schemes that aim to face the impacts of climate change