Advanced methodologies in dynamic traffic assignment modeling of managed lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. ^ Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. ^ Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. ^ With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.^

The role of protein oxidation in ageing & disease

Ageing is a natural phenomenon of the human lifecycle, yet it is still not understood what causes the deterioration of the human body near the end of the lifespan. One popular theory is the Free Radical Theory of Ageing, which proposes that oxidative damage to biomolecules causes ageing of tissues. The ageing population is affected by many chronic diseases. This study focused on sarcopenia (muscle loss in ageing) and obesity as two models for comparison of oxidative damage in muscle proteins in mice. The aim of the study was to develop advanced mass spectrometry methods to detect specific oxidative modifications to mouse muscle proteins, including oxidation, nitration, chlorination, and carbonyl group formation, but western blotting was also used to provide complementary information on the oxidative state of proteins from aged and obese muscle. Mass spectrometry proved to be a powerful tool, enabling identification of the types of modifications present, the sites at which they were present and percentage of the peptide populations that were modified. Targeted and semi-targeted mass spectrometry methods were optimised for the identification and quantitation of the oxidised residues in muscle proteins. The development of the quantitative methods enabled comparisons of mass spectrometry instruments. Both the Time of Flight and QTRAP systems showed advantages of using the different mass analysers to quantify oxidative modifications. Several oxidised residues were characterised and quantified in both the obese and sarcopenic models, and higher levels of oxidation were found compared to their control counterparts. Residues found to be oxidised were oxidation of proline, tyrosine and tryptophan, dioxidation of methionine, allysine and nitration of tyrosine. However quantification was performed on methionine dioxidation and cysteine trioxidation containing residues in SERCA. The combination of measuring residue susceptibility and functional studies could contribute to understanding the overall role of oxidation in ageing and obesity.

Biogeochemical parameters and processes in waters and bottom sediments of the Chukchi Sea

Study of biogeochemical processes in waters and sediments of the Chukchi Sea in August 2004 revealed atypical maxima of biogenic element (N, P, and Si) concentrations and rate of microbial sulfate reduction in the surface layer (0-3 cm) of marine sediments. The C/N/P ratio in organic matter (OM) of this layer does not fit the Redfield-Richards stoichiometric model. Specific features of biogeochemical processes in the sea are likely related to the complex dynamics of water, high primary produc¬tivity (110-1400 mg C/m**2/day), low depth of the basin (<50 m for 60% of the water area), reduced food chain due to low population of zooplankton, high density of zoobenthos (up to 4230 g/m**2), and high activity of microbial processes. Drastic decrease in concentrations of biogenic elements, iodine, total alkalinity, and population of microorganisms beneath the 0-3 cm layer testify to large-scale OM decay at the water-seafloor barrier. Our original experimental data support high annual rate of OM mineralization at the bottom of the Chukchi Sea.

Advanced Business Simulations-Incorporating business and process execution data

Key Performance Indicators (KPIs) and their predictions are widely used by the enterprises for informed decision making. Nevertheless , a very important factor, which is generally overlooked, is that the top level strategic KPIs are actually driven by the operational level business processes. These two domains are, however, mostly segregated and analysed in silos with different Business Intelligence solutions. In this paper, we are proposing an approach for advanced Business Simulations, which converges the two domains by utilising process execution & business data, and concepts from Business Dynamics (BD) and Business Ontologies, to promote better system understanding and detailed KPI predictions. Our approach incorporates the automated creation of Causal Loop Diagrams, thus empowering the analyst to critically examine the complex dependencies hidden in the massive amounts of available enterprise data. We have further evaluated our proposed approach in the context of a retail use-case that involved verification of the automatically generated causal models by a domain expert.

Catalytic Processes Mediated by Metal−Organic Frameworks : Reactivity and Mechanistic Studies

The present thesis describes the development of heterogeneous catalytic methodologies using metal−organic frameworks (MOFs) as porous matrices for supporting transition metal catalysts. A wide spectrum of chemical reactions is covered. Following the introductory section (Chapter 1), the results are divided between one descriptive part (Chapter 2) and four experimental parts (Chapters 3–6). Chapter 2 provides a detailed account of MOFs and their role in heterogeneous catalysis. Specific synthesis methods and characterization techniques that may be unfamiliar to organic chemists are illustrated based on examples from this work. Pd-catalyzed heterogeneous C−C coupling and C−H functionalization reactions are studied in Chapter 3, with focus on their practical utility. A vast functional group tolerance is reported, allowing access to substrates of relevance for the pharmaceutical industry. Issues concerning the recyclability of MOF-supported catalysts, leaching and operation under continuous flow are discussed in detail. The following chapter explores puzzling questions regarding the nature of the catalytically active species and the pathways of deactivation for Pd@MOF catalysts. These questions are addressed through detailed mechanistic investigations which include in situ XRD and XAS data acquisition. For this purpose a custom reaction cell is also described in Chapter 4. The scope of Pd@MOF-catalyzed reactions is expanded in Chapter 5. A strategy for boosting the thermal and chemical robustness of MOF crystals is presented. Pd@MOF catalysts are coated with a protecting SiO2 layer, which improves their mechanical properties without impeding diffusion. The resulting nanocomposite is better suited to withstand the harsh conditions of aerobic oxidation reactions. In this chapter, the influence of the nanoparticles’ geometry over the catalyst’s selectivity is also investigated. While Chapters 3–5 dealt with Pd-catalyzed processes, Chapter 6 introduces hybrid materials based on first-row transition metals. Their reactivity is explored towards light-driven water splitting. The heterogenization process leads to stabilized active sites, facilitating the spectroscopic probing of intermediates in the catalytic cycle.

Role of Platinum Deposited on TiO2 in Photocatalytic Methanol Oxidation and Dehydrogenation Reactions

Titania modified nanoparticles have been prepared by the photodeposition method employing platinum particles on the commercially available titanium dioxide (Hombikat UV 100). The properties of the prepared photocatalysts were investigated by means of the Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), atomic force microscopy (AFM), and UV-visible diffuse spectrophotometry (UV-Vis). XRD was employed to determine the crystallographic phase and particle size of both bare and platinised titanium dioxide. The results indicated that the particle size was decreased with the increasing of platinum loading. AFM analysis showed that one particle consists of about 9 to 11 crystals. UV-vis absorbance analysis showed that the absorption edge shifted to longer wavelength for 0.5% Pt loading compared with bare titanium dioxide. The photocatalytic activity of pure and Pt-loaded TiO2 was investigated employing the photocatalytic oxidation and dehydrogenation of methanol. The results of the photocatalytic activity indicate that the platinized titanium dioxide samples are always more active than the corresponding bare TiO2 for both methanol oxidation and dehydrogenation processes. The loading with various platinum amounts resulted in a significant improvement of the photocatalytic activity of TiO2. This beneficial effect was attributed to an increased separation of the photogenerated electron-hole charge carriers.

Catalytic dehydration of glycerol to acrolein: catalysts and processes

The valorization of glycerol has been widely studied notably due to the oversupply of the latter from biodiesel production. Among the different upgrading reactions, dehydration to acrolein is of high interest due to the importance of acrolein as an intermediate for polymer industry (via acrylic acid) and for feed additive (synthon for DL-methionine). It is known that acrolein can be obtained by glycerol catalytic dehydration over acid catalysts. Zeolites and heteropolyacid catalysts are initially highly active, but deactivate rapidly with time on stream by coking, whilst mixed metal oxides are more stable catalytic systems but less selective and in addition they require an activation period. In this talk, the strategy we followed is described. It consisted in a parallel approach in which we developed supported heteropolyacid-based catalysts with increased stability and acrolein selectivity by using a ZrO2-grafted SBA-15 playing the role of the support for silico-tungstic acid active phase, as well as a new concept based on a two zones fluidized bed reactor (TZFBR) to tackle the unavoidable deactivation issue of the HPA catalysts. This type of reactor comprises – in one single capacity – reaction and regeneration zones. In the second part of the lecture the REALCAT platform was introduced. REALCAT (French acronym standing for ‘Advanced High-Throughput Technologies Platform for Biorefineries Catalysts Design’) is an highly integrated platform devoted to the acceleration of innovation in all the fields of industrial catalysis with an emphasis on emergent biorefinery catalytic processes. In this extremely competitive field, REALCAT consists in a versatile High-Throughput Technologies (HTT) platform devoted to innovation in heterogeneous, homogeneous or biocatalysts AND their combinations under the ultra-efficient very novel concept of hybrid catalysis.

New insights into the transport processes controlling the sulfate-methane-transition-zone near methane vents

Over the past years, several studies have raised concerns about the possible interactions between methane hydrate decomposition and external change. To carry out such an investigation, it is essential to characterize the baseline dynamics of gas hydrate systems related to natural geological and sedimentary processes. This is usually treated through the analysis of sulfate-reduction coupled to anaerobic oxidation of methane (AOM). Here, we model sulfate reduction coupled with AOM as a two-dimensional (2D) problem including, advective and diffusive transport. This is applied to a case study from a deep-water site off Nigeria’s coast where lateral methane advection through turbidite layers was suspected. We show by analyzing the acquired data in combination with computational modeling that a two-dimensional approach is able to accurately describe the recent past dynamics of such a complex natural system. Our results show that the sulfate-methane-transition-zone (SMTZ) is not a vertical barrier for dissolved sulfate and methane. We also show that such a modeling is able to assess short timescale variations in the order of decades to centuries.

Modelling and analysing cargo screening processes: a project outline

The efficiency of current cargo screening processes at sea and air ports is unknown as no benchmarks exists against which they could be measured. Some manufacturer benchmarks exist for individual sensors but we have not found any benchmarks that take a holistic view of the screening procedures assessing a combination of sensors and also taking operator variability into account. Just adding up resources and manpower used is not an effective way for assessing systems where human decision-making and operator compliance to rules play a vital role. For such systems more advanced assessment methods need to be used, taking into account that the cargo screening process is of a dynamic and stochastic nature. Our project aim is to develop a decision support tool (cargo-screening system simulator) that will map the right technology and manpower to the right commodity-threat combination in order to maximize detection rates. In this paper we present a project outline and highlight the research challenges we have identified so far. In addition we introduce our first case study, where we investigate the cargo screening process at the ferry port in Calais.

Development of a 1-D Catalyzed Diesel Particulate Filter Model for Simulation of the Performance and the Oxidation of Particulate Matter and Nitrogen Oxides Using Passive Oxidation and Active Regeneration Engine Experimental Data

Back-pressure on a diesel engine equipped with an aftertreatment system is a function of the pressure drop across the individual components of the aftertreatment system, typically, a diesel oxidation catalyst (DOC), catalyzed particulate filter (CPF) and selective catalytic reduction (SCR) catalyst. Pressure drop across the CPF is a function of the mass flow rate and the temperature of the exhaust flowing through it as well as the mass of particulate matter (PM) retained in the substrate wall and the cake layer that forms on the substrate wall. Therefore, in order to control the back-pressure on the engine at low levels and to minimize the fuel consumption, it is important to control the PM mass retained in the CPF. Chemical reactions involving the oxidation of PM under passive oxidation and active regeneration conditions can be utilized with computer numerical models in the engine control unit (ECU) to control the pressure drop across the CPF. Hence, understanding and predicting the filtration and oxidation of PM in the CPF and the effect of these processes on the pressure drop across the CPF are necessary for developing control strategies for the aftertreatment system to reduce back-pressure on the engine and in turn fuel consumption particularly from active regeneration. Numerical modeling of CPF's has been proven to reduce development time and the cost of aftertreatment systems used in production as well as to facilitate understanding of the internal processes occurring during different operating conditions that the particulate filter is subjected to. A numerical model of the CPF was developed in this research work which was calibrated to data from passive oxidation and active regeneration experiments in order to determine the kinetic parameters for oxidation of PM and nitrogen oxides along with the model filtration parameters. The research results include the comparison between the model and the experimental data for pressure drop, PM mass retained, filtration efficiencies, CPF outlet gas temperatures and species (NO2) concentrations out of the CPF. Comparisons of PM oxidation reaction rates obtained from the model calibration to the data from the experiments for ULSD, 10 and 20% biodiesel-blended fuels are presented.

Advanced Methodologies in Dynamic Traffic Assignment Modeling of Managed Lanes

Managed lane strategies are innovative road operation schemes for addressing congestion problems. These strategies operate a lane (lanes) adjacent to a freeway that provides congestion-free trips to eligible users, such as transit or toll-payers. To ensure the successful implementation of managed lanes, the demand on these lanes need to be accurately estimated. Among different approaches for predicting this demand, the four-step demand forecasting process is most common. Managed lane demand is usually estimated at the assignment step. Therefore, the key to reliably estimating the demand is the utilization of effective assignment modeling processes. Managed lanes are particularly effective when the road is functioning at near-capacity. Therefore, capturing variations in demand and network attributes and performance is crucial for their modeling, monitoring and operation. As a result, traditional modeling approaches, such as those used in static traffic assignment of demand forecasting models, fail to correctly predict the managed lane demand and the associated system performance. The present study demonstrates the power of the more advanced modeling approach of dynamic traffic assignment (DTA), as well as the shortcomings of conventional approaches, when used to model managed lanes in congested environments. In addition, the study develops processes to support an effective utilization of DTA to model managed lane operations. Static and dynamic traffic assignments consist of demand, network, and route choice model components that need to be calibrated. These components interact with each other, and an iterative method for calibrating them is needed. In this study, an effective standalone framework that combines static demand estimation and dynamic traffic assignment has been developed to replicate real-world traffic conditions. With advances in traffic surveillance technologies collecting, archiving, and analyzing traffic data is becoming more accessible and affordable. The present study shows how data from multiple sources can be integrated, validated, and best used in different stages of modeling and calibration of managed lanes. Extensive and careful processing of demand, traffic, and toll data, as well as proper definition of performance measures, result in a calibrated and stable model, which closely replicates real-world congestion patterns, and can reasonably respond to perturbations in network and demand properties.

Effects of advanced glycation end-products (AGEs) on retinal pigment epithelial (RPE) cells lysosomal function:implications for age-related macular degeneration (AMD)

Purpose: RPE lysosomal dysfunction is a major contributor to AMD pathogenesis. Controlled activity of a major class of RPE proteinases, the cathepsins, is crucial in maintaining correct lysosomal function. Advanced glycation end-products (AGEs) accumulate in the Bruch’s membrane (BM) with age, impacting critical RPE functions and in turn, contributing to the development of AMD. The aim of this study was to assess the effect of AGEs on lysosomal function by analysing the expression, processing and activity of the cysteine proteinases cathepsins B, L and S, and the aspartic proteinase cathepsin D. Methods: ARPE-19 cells were cultured on AGE-containing BM mimics (matrigel) for 14 days and compared to untreated substrate. Expression levels and intracellular processing of cathepsins B, D, L and S, were assessed by qPCR and immunoblotting of cell lysates. Lysosomal activity was investigated using multiple activity assays specific to each of the analysed cathepsins. Statistical analysis was performed using the Student’s independent T-test. Results: AGE exposure produced a 36% decrease in cathepsin L activity when compared to non-treated controls (p=0.02, n= 3) although no significant changes were observed in protein expression/processing under these conditions. Both the pro and active forms of cathepsin S decreased by 40% (p=0.04) and 74% (p=0.004), respectively (n=3). In contrast, the active form of the cathepsin D increased by 125% (p=0.005, n= 4). However, no changes were observed in the activity levels of both cathepsins S and D. In addition, cathepsin B expression, processing and activity also remained unaltered following AGE exposure. Conclusions: AGEs accumulation in the extracellular matrix, a phenomenon associated with the natural aging process of the BM, attenuates the expression, intracellular processing and activity of specific lysosomal effectors. Altered enzymatic function may impair important lysosomal processes such as endocytosis, autophagy and phagocytosis of photoreceptor outer segments, each of which may influence the age-related dysfunction of the RPE and subsequently, AMD pathogenesis.