Search results for: Mathias Marquardt

Authors: Anusha P. Wijesundara, Dulap I. Rathnayake, Nihal D. Perera

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Bathymetric information is fundamental importance to coastal and marine planning and management, nautical navigation, and scientific studies of marine environments. Satellite-derived bathymetry data provide detailed information in areas where conventional sounding data is lacking and conventional surveys are inaccessible. The two empirical approaches of log-linear bathymetric inversion model and non-linear bathymetric inversion model are applied for deriving bathymetry from high-resolution multispectral satellite imagery. This study compares these two approaches by means of geographical error analysis for the site Kankesanturai using WorldView-2 satellite imagery. Based on the Levenberg-Marquardt method calibrated the parameters of non-linear inversion model and the multiple-linear regression model was applied to calibrate the log-linear inversion model. In order to calibrate both models, Single Beam Echo Sounding (SBES) data in this study area were used as reference points. Residuals were calculated as the difference between the derived depth values and the validation echo sounder bathymetry data and the geographical distribution of model residuals was mapped. The spatial autocorrelation was calculated by comparing the performance of the bathymetric models and the results showing the geographic errors for both models. A spatial error model was constructed from the initial bathymetry estimates and the estimates of autocorrelation. This spatial error model is used to generate more reliable estimates of bathymetry by quantifying autocorrelation of model error and incorporating this into an improved regression model. Log-linear model (R²=0.846) performs better than the non- linear model (R²=0.692). Finally, the spatial error models improved bathymetric estimates derived from linear and non-linear models up to R²=0.854 and R²=0.704 respectively. The Root Mean Square Error (RMSE) was calculated for all reference points in various depth ranges. The magnitude of the prediction error increases with depth for both the log-linear and the non-linear inversion models. Overall RMSE for log-linear and the non-linear inversion models were ±1.532 m and ±2.089 m, respectively.

Keywords: log-linear model, multi spectral, residuals, spatial error model

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Authors: Mathias M. L. Zarate, Erwin

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In the design of an H-shaped steel beam, lateral torsional buckling is one of the main considerations. The beam should be braced properly so that the plastic moment capacity can be well developed. Although the design guideline for lateral bracing has been available for decades ago, the design guideline was established based on past research which mostly used a very simple model to derive the proposed design equations provided. Most of the beam structures considered in the past research were simply supported beams subjected to transverse loading. Recalling that a simply supported beam is a kind of beam structure that is the most susceptible to lateral torsional buckling, the design requirements derived based on this structure may be too conservative for beams with other boundary conditions. As in the beams of a moment-resisting frame, both beam ends are rigidly connected to the column and thus have higher resistance against the lateral torsional buckling. On the other hand, the use of lateral bracing to support the bottom flange of the beams in building structures often becomes an obstacle. If the actual boundary condition of the beams in moment resisting is considered when analyzing the beam bracing requirements, the chance that a beam does not need to be braced at the bottom flange will become higher. Nowadays, by utilizing the power of computers to conduct numerical analysis, the buckling behavior of the beams with various kinds of boundary conditions and loading configurations can be explored conveniently as long as a reliable analysis model can be developed. In this study, finite element analysis is conducted to investigate the buckling behavior of H-shaped steel beams with various kinds of cross-sections and lengths subjected to seismic-type loading. The considered beam is laterally restrained along the top flange of the beam to account for the presence of floor slab, while no lateral bracing is provided to the bottom flange. The lateral torsional buckling of the beam is evaluated by observing the magnitude of the twist angle during the loading history. The main parameters that affect the buckling potential of the beam are found to be the beam length-to-beam depth ratio and the beam depth-to-flange width ratio. Higher values of these two parameters indicate a higher lateral torsional buckling potential in the beam. The limit values for the two design parameters are found to be related to the desired acceptance criteria which in this case is the magnitude of the twist angle considered as acceptable.

Keywords: H-shaped steel beam, lateral bracing design, lateral torsional buckling, seismic design

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Authors: Mathias U. Bonet, Pericles Pilidis, Georgios Doulgeris

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An aero-derivative marine Gas Turbine engine model is simulated to be installed as the main propulsion prime mover to power a cruise ship which is designed and routed to transport intending Muslim pilgrims for the annual hajj pilgrimage from Nigeria to the Islamic port city of Jeddah in Saudi Arabia. A performance assessment of the Gas Turbine engine has been conducted by examining the effect of varying aerodynamic and hydrodynamic conditions encountered at various geographical locations along the scheduled transit route during the voyage. The investigation focuses on the overall behavior of the Gas Turbine engine employed to power and propel the ship as it operates under ideal and adverse conditions to be encountered during calm and rough weather according to the different seasons of the year under which the voyage may be undertaken. The variation of engine performance under varying operating conditions has been considered as a very important economic issue by determining the time the speed by which the journey is completed as well as the quantity of fuel required for undertaking the voyage. The assessment also focuses on the increased resistance caused by the fouling of the submerged portion of the ship hull surface with its resultant effect on the power output of the engine as well as the overall performance of the propulsion system. Daily ambient temperature levels were obtained by accessing data from the UK Meteorological Office while the varying degree of turbulence along the transit route and according to the Beaufort scale were also obtained as major input variables of the investigation. By assuming the ship to be navigating the Atlantic Ocean and the Mediterranean Sea during winter, spring and summer seasons, the performance modeling and simulation was accomplished through the use of an integrated Gas Turbine performance simulation code known as ‘Turbomach’ along with a Matlab generated code named ‘Poseidon’, all of which have been developed at the Power and Propulsion Department of Cranfield University. As a case study, the results of the various assumptions have further revealed that the marine Gas Turbine is a reliable and available alternative to the conventional marine propulsion prime movers that have dominated the maritime industry before now. The techno-economic and environmental assessment of this type of propulsion prime mover has enabled the determination of the effect of changes in weather and sea conditions on the ship speed as well as trip time and the quantity of fuel required to be burned throughout the voyage.

Keywords: ambient temperature, hull fouling, marine gas turbine, performance, propulsion, voyage

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Authors: Morgan Johansson, Joosef Leppanen, Mathias Flansbjer, Fabio Lozano, Josef Makdesi

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Concrete is commonly used for protective structures and how impact loading affects different types of concrete structures is an important issue. Often the knowledge gained from static loading is also used in the design of impulse loaded structures. A large plastic deformation capacity is essential to obtain a large energy absorption in an impulse loaded structure. However, the structural response of an impact loaded concrete beam may be very different compared to a statically loaded beam. Consequently, the plastic deformation capacity and failure modes of the concrete structure can be different when subjected to dynamic loads; and hence it is not sure that the observations obtained from static loading are also valid for dynamic loading. The aim of this paper is to investigate the residual plastic deformation capacity in reinforced concrete beams subjected to drop weight impact tests. A test-series consisting of 18 simply supported beams (0.1 x 0.1 x 1.18 m, ρs = 0.7%) with a span length of 1.0 m and subjected to a point load in the beam mid-point, was carried out. 2x6 beams were first subjected to drop weight impact tests, and thereafter statically tested until failure. The drop in weight had a mass of 10 kg and was dropped from 2.5 m or 5.0 m. During the impact tests, a high-speed camera was used with 5 000 fps and for the static tests, a camera was used with 0.5 fps. Digital image correlation (DIC) analyses were conducted and from these the velocities of the beam and the drop weight, as well as the deformations and crack propagation of the beam, were effectively measured. Additionally, for the static tests, the applied load and midspan deformation were measured. The load-deformation relations for the beams subjected to an impact load were compared with 6 reference beams that were subjected to static loading only. The crack pattern obtained were compared using DIC, and it was concluded that the resulting crack formation depended much on the test method used. For the static tests, only bending cracks occurred. For the impact loaded beams, though, distinctive diagonal shear cracks also formed below the zone of impact and less wide shear cracks were observed in the region half-way to the support. Furthermore, due to wave propagation effects, bending cracks developed in the upper part of the beam during initial loading. The results showed that the plastic deformation capacity increased for beams subjected to drop weight impact tests from a high drop height of 5.0 m. For beams subjected to an impact from a low drop height of 2.5 m, though, the plastic deformation capacity was in the same order of magnitude as for the statically loaded reference beams. The beams tested were designed to fail due to bending when subjected to a static load. However, for the impact tested beams, one beam exhibited a shear failure at a significantly reduced load level when it was tested statically; indicating that there might be a risk of reduced residual load capacity for impact loaded structures.

Keywords: digital image correlation (DIC), drop weight impact, experiments, plastic deformation capacity, reinforced concrete

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Authors: Renée M. Ripken, Johannes G. E. Gardeniers, Séverine Le Gac

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Controlling the multiphase behavior of aqueous biomass mixtures is essential when working in the biomass conversion industry. Here, the vapor/liquid equilibria (VLE) of ethylene glycol, glycerol, and xylitol were studied for temperatures between 25 and 200 °C and pressures of 1 to 10 bar. These experiments were performed in a microfluidic platform, which exhibits excellent heat transfer properties so that equilibrium is reached fast. Firstly, the saturated vapor pressure as a function of the temperature and the substrate mole fraction of the substrate was calculated using AspenPlus with a Redlich-Kwong-Soave Boston-Mathias (RKS-BM) model. Secondly, we developed a high-pressure and high-temperature microfluidic set-up for experimental validation. Furthermore, we have studied the multiphase flow pattern that occurs after the saturation temperature was achieved. A glass-silicon microfluidic device containing a 0.4 or 0.2 m long meandering channel with a depth of 250 μm and a width of 250 or 500 μm was fabricated using standard microfabrication techniques. This device was placed in a dedicated chip-holder, which includes a ceramic heater on the silicon side. The temperature was controlled and monitored by three K-type thermocouples: two were located between the heater and the silicon substrate, one to set the temperature and one to measure it, and the third one was placed in a 300 μm wide and 450 μm deep groove on the glass side to determine the heat loss over the silicon. An adjustable back pressure regulator and a pressure meter were added to control and evaluate the pressure during the experiment. Aqueous biomass solutions (10 wt%) were pumped at a flow rate of 10 μL/min using a syringe pump, and the temperature was slowly increased until the theoretical saturation temperature for the pre-set pressure was reached. First and surprisingly, a significant difference was observed between our theoretical saturation temperature and the experimental results. The experimental values were 10’s of degrees higher than the calculated ones and, in some cases, saturation could not be achieved. This discrepancy can be explained in different ways. Firstly, the pressure in the microchannel is locally higher due to both the thermal expansion of the liquid and the Laplace pressure that has to be overcome before a gas bubble can be formed. Secondly, superheating effects are likely to be present. Next, once saturation was reached, the flow pattern of the gas/liquid multiphase system was recorded. In our device, the point of nucleation can be controlled by taking advantage of the pressure drop across the channel and the accurate control of the temperature. Specifically, a higher temperature resulted in nucleation further upstream in the channel. As the void fraction increases downstream, the flow regime changes along the channel from bubbly flow to Taylor flow and later to annular flow. All three flow regimes were observed simultaneously. The findings of this study are key for the development and optimization of a microreactor for hydrogen production from biomass.

Keywords: biomass conversion, high pressure and high temperature microfluidics, multiphase, phase diagrams, superheating

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Authors: A. Basu, A. A. Purohit, M. M. Vaidya, M. D. Kudale

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Mumbai, being traditionally the epicenter of India's trade and commerce, the existing major ports such as Mumbai and Jawaharlal Nehru Ports (JN) situated in Thane estuary are also developing its waterfront facilities. Various developments over the passage of decades in this region have changed the tidal flux entering/leaving the estuary. The intake at Pir-Pau is facing the problem of shortage of water in view of advancement of shoreline, while jetty near Ulwe faces the problem of ship scheduling due to existence of shallower depths between JN Port and Ulwe Bunder. In order to solve these problems, it is inevitable to have information about tide levels over a long duration by field measurements. However, field measurement is a tedious and costly affair; application of artificial intelligence was used to predict water levels by training the network for the measured tide data for one lunar tidal cycle. The application of two layered feed forward Artificial Neural Network (ANN) with back-propagation training algorithms such as Gradient Descent (GD) and Levenberg-Marquardt (LM) was used to predict the yearly tide levels at waterfront structures namely at Ulwe Bunder and Pir-Pau. The tide data collected at Apollo Bunder, Ulwe, and Vashi for a period of lunar tidal cycle (2013) was used to train, validate and test the neural networks. These trained networks having high co-relation coefficients (R= 0.998) were used to predict the tide at Ulwe, and Vashi for its verification with the measured tide for the year 2000 & 2013. The results indicate that the predicted tide levels by ANN give reasonably accurate estimation of tide. Hence, the trained network is used to predict the yearly tide data (2015) for Ulwe. Subsequently, the yearly tide data (2015) at Pir-Pau was predicted by using the neural network which was trained with the help of measured tide data (2000) of Apollo and Pir-Pau. The analysis of measured data and study reveals that: The measured tidal data at Pir-Pau, Vashi and Ulwe indicate that there is maximum amplification of tide by about 10-20 cm with a phase lag of 10-20 minutes with reference to the tide at Apollo Bunder (Mumbai). LM training algorithm is faster than GD and with increase in number of neurons in hidden layer and the performance of the network increases. The predicted tide levels by ANN at Pir-Pau and Ulwe provides valuable information about the occurrence of high and low water levels to plan the operation of pumping at Pir-Pau and improve ship schedule at Ulwe.

Keywords: artificial neural network, back-propagation, tide data, training algorithm

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Authors: Mathias U. Bonet, Pericles Pilidis, Georgios Doulgeris

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A gas turbine-powered cruise Liner is scheduled to transport pilgrim passengers from Lagos-Nigeria to the Islamic port city of Jeddah in Saudi Arabia. Since the gas turbine is an air breathing machine, changes in the density and/or mass flow at the compressor inlet due to an encounter with variations in weather conditions induce negative effects on the performance of the power plant during the voyage. In practice, all deviations from the reference atmospheric conditions of 15 ^oC and 1.103 bar tend to affect the power output and other thermodynamic parameters of the gas turbine cycle. Therefore, this paper seeks to evaluate how a simple cycle marine gas turbine power plant would react under a variety of scenarios that may be encountered during a voyage as the ship sails across the Atlantic Ocean and the Mediterranean Sea before arriving at its designated port of discharge. It is also an assessment that focuses on the effect of varying aerodynamic and hydrodynamic conditions which deteriorate the efficient operation of the propulsion system due to an increase in resistance that results from some projected levels of the ship hull fouling. The investigated passenger ship is designed to run at a service speed of 22 knots and cover a distance of 5787 nautical miles. The performance evaluation consists of three separate voyages that cover a variety of weather conditions in winter, spring and summer seasons. Real-time daily temperatures and the sea states for the selected transit route were obtained and used to simulate the voyage under the aforementioned operating conditions. Changes in engine firing temperature, power output as well as the total fuel consumed per voyage including other performance variables were separately predicted under both calm and adverse weather conditions. The collated data were obtained online from the UK Meteorological Office as well as the UK Hydrographic Office websites, while adopting the Beaufort scale for determining the magnitude of sea waves resulting from rough weather situations. The simulation of the gas turbine performance and voyage analysis was effected through the use of an integrated Cranfield-University-developed computer code known as ‘Turbomatch’ and ‘Poseidon’. It is a project that is aimed at developing a method for predicting the off design behavior of the marine gas turbine when installed and operated as the main prime mover for both propulsion and powering of all other auxiliary services onboard a passenger cruise liner. Furthermore, it is a techno-economic and environmental assessment that seeks to enable the forecast of the marine gas turbine part and full load performance as it relates to the fuel requirement for a complete voyage.

Keywords: cruise ship, gas turbine, hull fouling, performance, propulsion, weather

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Authors: Mohammad Rahman, Guy Burton, Megan Mathias

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Strategic management as an organizational practice was adopted by the public sector in the New Public Management (NPM) era that began in most parts of the world in the 1980s. Strategy as a new public management concept was subscribed by governments in both developed and developing world, as they were persuaded that clearly defined vision, mission and goals, as well as programs and projects - aligned with the goals - could potentially help achieve government vision at the national level and organizational goals at the service-delivery level. The advocates for strategic management in the public sector saw an inherent link between strategy and performance, claiming that the implementation of organizational strategy has an effect on the overall performance of an organization. Arguably, many government entities that have failed in enhancing team and individual performance had poorly-designed strategy or weak strategy implementation. Another key argument about low-level performance is linked with lack of strategic sense-making and orientation by middle managers in particular. Scholars maintain that employees at all levels need to understand strategic management plan in order to facilitate its implementation. Therefore, involving employees (particularly the middle managers) from the beginning potentially helps an organization avoid the drop in performance, and on the contrary would increase their commitment. The United Arab Emirates (UAE) is well known for adopting public sector reform strategies and tools since the 1990s. This observation is contextually pertinent in the case of the Government of Dubai, which has provided a Strategy Execution Guide to all of its entities to achieve high level strategic success in service delivery. The Dubai public sector also adopts road maps for e-Government, Smart Dubai, Expo 2020, investment, environment, education, health and other sectors. Evidently, some of these strategies are bringing tangible (e.g. Smart Dubai transformation) results in a transformational manner. However, the amount of academic research and literature on the strategy process vis-à-vis staff performance in the Government of Dubai is limited. In this backdrop, this study examines how individual performance of public sector employees in Dubai is linked with their sense-making, engagement and orientation with strategy development and implementation processes. Based on a theoretical framework, this study will undertake a sample-based questionnaire survey amongst middle managers in Dubai public sector to (a) measure the level of engagement of middle managers in strategy development and implementation processes as perceived by them; (b) observe the organizational landscape in which role expectations are placed on middle managers; and (c) examine the impact of employee engagement in strategy development process and the conditions for role expectations on individual performance. The paper is expected to provide new insights on the interface between strategic sense-making and performance in order to contribute a better understanding of the current culture/practices of staff engagement in strategic management in the public sector of Dubai.

Keywords: employee performance, government of Dubai, middle managers, strategic sense-making

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Authors: Edgar Santoyo, Daniel Perez-Zarate, Agustin Acevedo, Lorena Diaz-Gonzalez, Mirna Guevara

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Four new gas geothermometers have been derived from a multivariate geo chemometric analysis of a geothermal fluid chemistry database, two of which use the natural logarithm of CO₂ and H2S concentrations (mmol/mol), respectively, and the other two use the natural logarithm of the H₂S/H₂ and CO₂/H₂ ratios. As a strict compilation criterion, the database was created with gas-phase composition of fluids and bottomhole temperatures (BHTM) measured in producing wells. The calibration of the geothermometers was based on the geochemical relationship existing between the gas-phase composition of well discharges and the equilibrium temperatures measured at bottomhole conditions. Multivariate statistical analysis together with the use of artificial neural networks (ANN) was successfully applied for correlating the gas-phase compositions and the BHTM. The predicted or simulated bottomhole temperatures (BHTANN), defined as output neurons or simulation targets, were statistically compared with measured temperatures (BHTM). The coefficients of the new geothermometers were obtained from an optimized self-adjusting training algorithm applied to approximately 2,080 ANN architectures with 15,000 simulation iterations each one. The self-adjusting training algorithm used the well-known Levenberg-Marquardt model, which was used to calculate: (i) the number of neurons of the hidden layer; (ii) the training factor and the training patterns of the ANN; (iii) the linear correlation coefficient, R; (iv) the synaptic weighting coefficients; and (v) the statistical parameter, Root Mean Squared Error (RMSE) to evaluate the prediction performance between the BHTM and the simulated BHTANN. The prediction performance of the new gas geothermometers together with those predictions inferred from sixteen well-known gas geothermometers (previously developed) was statistically evaluated by using an external database for avoiding a bias problem. Statistical evaluation was performed through the analysis of the lowest RMSE values computed among the predictions of all the gas geothermometers. The new gas geothermometers developed in this work have been successfully used for predicting subsurface temperatures in high-temperature geothermal systems of Mexico (e.g., Los Azufres, Mich., Los Humeros, Pue., and Cerro Prieto, B.C.) as well as in a blind geothermal system (known as Acoculco, Puebla). The last results of the gas geothermometers (inferred from gas-phase compositions of soil-gas bubble emissions) compare well with the temperature measured in two wells of the blind geothermal system of Acoculco, Puebla (México). Details of this new development are outlined in the present research work. Acknowledgements: The authors acknowledge the funding received from CeMIE-Geo P09 project (SENER-CONACyT).

Keywords: artificial intelligence, gas geochemistry, geochemometrics, geothermal energy

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Authors: Caixia Gao, Chuanrong Li, Lingli Tang, Lingling Ma, Yonggang Qian, Ning Wang

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Knowledge of land surface temperature (LST) is of crucial important in energy balance studies and environment modeling. Satellite thermal infrared (TIR) imagery is the primary source for retrieving LST at the regional and global scales. Due to the combination of atmosphere and land surface of received radiance by TIR sensors, atmospheric effect correction has to be performed to remove the atmospheric transmittance and upwelling radiance. Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) provides measurements every 15 minutes in 12 spectral channels covering from visible to infrared spectrum at fixed view angles with 3km pixel size at nadir, offering new and unique capabilities for LST, LSE measurements. However, due to its high temporal resolution, the atmosphere correction could not be performed with radiosonde profiles or reanalysis data since these profiles are not available at all SEVIRI TIR image acquisition times. To solve this problem, a two-part six-parameter semi-empirical diurnal temperature cycle (DTC) model has been applied to the temporal interpolation of ECMWF reanalysis data. Due to the fact that the DTC model is underdetermined with ECMWF data at four synoptic times (UTC times: 00:00, 06:00, 12:00, 18:00) in one day for each location, some approaches are adopted in this study. It is well known that the atmospheric transmittance and upwelling radiance has a relationship with water vapour content (WVC). With the aid of simulated data, the relationship could be determined under each viewing zenith angle for each SEVIRI TIR channel. Thus, the atmospheric transmittance and upwelling radiance are preliminary removed with the aid of instantaneous WVC, which is retrieved from the brightness temperature in the SEVIRI channels 5, 9 and 10, and a group of the brightness temperatures for surface leaving radiance (Tg) are acquired. Subsequently, a group of the six parameters of the DTC model is fitted with these Tg by a Levenberg-Marquardt least squares algorithm (denoted as DTC model 1). Although the retrieval error of WVC and the approximate relationships between WVC and atmospheric parameters would induce some uncertainties, this would not significantly affect the determination of the three parameters, td, ts and β (β is the angular frequency, td is the time where the Tg reaches its maximum, ts is the starting time of attenuation) in DTC model. Furthermore, due to the large fluctuation in temperature and the inaccuracy of the DTC model around sunrise, SEVIRI measurements from two hours before sunrise to two hours after sunrise are excluded. With the knowledge of td , ts, and β, a new DTC model (denoted as DTC model 2) is accurately fitted again with these Tg at UTC times: 05:57, 11:57, 17:57 and 23:57, which is atmospherically corrected with ECMWF data. And then a new group of the six parameters of the DTC model is generated and subsequently, the Tg at any given times are acquired. Finally, this method is applied to SEVIRI data in channel 9 successfully. The result shows that the proposed method could be performed reasonably without assumption and the Tg derived with the improved method is much more consistent with that from radiosonde measurements.

Keywords: atmosphere correction, diurnal temperature cycle model, land surface temperature, SEVIRI

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Authors: Emilie Gout, Mathias Monnot, Olivier Boutin, Pierre Vanloot, Philippe Moulin

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Water is a precious resource. Treating industrial wastewater remains a considerable technical challenge of our century. The effluent considered for this study is landfill leachate treated by reverse osmosis (RO). Nowadays, in most developed countries, sanitary landfilling is the main method to deal with municipal solid waste. Rainwater percolates through solid waste, generating leachates mostly comprised of organic and inorganic matter. Whilst leachate ages, its composition varies, becoming more and more bio-refractory. RO is already used for landfill leachates as it generates good quality permeate. However, its mains drawback is the production of highly polluted concentrates that cannot be discharged in the environment or reused, which is an important industrial issue. It is against this background that the study of coupling RO with wet air oxidation (WAO) was set to intensify and optimize processes to meet current regulations for water discharge in the environment. WAO is widely studied for effluents containing bio-refractory compounds. Oxidation consists of a destruction reaction capable of mineralizing the recalcitrant organic fraction of pollution into carbon dioxide and water when complete. WAO process in subcritical conditions requires a high-energy consumption, but it can be autothermic in a certain range of chemical oxygen demand (COD) concentrations (10-100 g.L⁻¹). Appropriate COD concentrations are reached in landfill leachate RO concentrates. Therefore, the purpose of this work is to report the performances of mineralization during WAO on RO concentrates. The coupling of RO/WAO has shown promising results in previous works on both synthetic and real effluents in terms of organic carbon (TOC) reduction by WAO and retention by RO. Non-catalytic WAO with air as oxidizer was performed in a lab-scale stirred autoclave (1 L) on landfill leachates RO concentrates collected in different seasons in a sanitary landfill in southern France. The yield of WAO depends on operating parameters such as total pressure, temperature, and time. Compositions of the effluent are also important aspects for process intensification. An experimental design methodology was used to minimize the number of experiments whilst finding the operating conditions achieving the best pollution reduction. The simulation led to a set of 18 experiments, and the responses to highlight process efficiency are pH, conductivity, turbidity, COD, TOC, and inorganic carbon. A 70% oxygen excess was chosen for all the experiments. First experiments showed that COD and TOC abatements of at least 70% were obtained after 90 min at 300°C and 20 MPa, which attested the possibility to treat RO leachate concentrates with WAO. In order to meet French regulations and validate process intensification with industrial effluents, some continuous experiments in a bubble column are foreseen, and some further analyses will be performed, such as biological oxygen demand and study of gas composition. Meanwhile, other industrial effluents are treated to compare RO-WAO performances. These effluents, coming from pharmaceutical, petrochemical, and tertiary wastewater industries, present different specific pollutants that will provide a better comprehension of the hybrid process and prove the intensification and feasibility of the process at an industrial scale. Acknowledgments: This work has been supported by the French National Research Agency (ANR) for the Project TEMPO under the reference number ANR-19-CE04-0002-01.

Keywords: hybrid process, landfill leachates, process intensification, reverse osmosis, wet air oxidation

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Authors: Manon Frédout, Laëtitia Bucari, Mathias Aloui, Gaëtan Duhamel, Olivier Rovellotti, Javier Blanco

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Ecosystems must be resilient to ensure cleaner air, better water and soil quality, and thus healthier citizens. Technology can be an excellent tool to support urban ecosystem restoration projects, especially when based on Open Source and promoting Open Data. This is the goal of the ecoTeka application: one single digital tool for tree management which allows decision-makers to improve their urban forestry practices, enabling more responsible urban planning and climate change adaptation. EcoTeka provides city councils with three main functionalities tackling three of their challenges: easier biodiversity inventories, better green space management, and more efficient planning. To answer the cities’ need for reliable tree inventories, the application has been first built with open data coming from the websites OpenStreetMap and OpenTrees, but it will also include very soon the possibility of creating new data. To achieve this, a multi-source algorithm will be elaborated, based on existing artificial intelligence Deep Forest, integrating open-source satellite images, 3D representations from LiDAR, and street views from Mapillary. This data processing will permit identifying individual trees' position, height, crown diameter, and taxonomic genus. To support urban forestry management, ecoTeka offers a dashboard for monitoring the city’s tree inventory and trigger alerts to inform about upcoming due interventions. This tool was co-constructed with the green space departments of the French cities of Alès, Marseille, and Rouen. The third functionality of the application is a decision-making tool for urban planning, promoting biodiversity and landscape connectivity metrics to drive ecosystem restoration roadmap. Based on landscape graph theory, we are currently experimenting with new methodological approaches to scale down regional ecological connectivity principles to local biodiversity conservation and urban planning policies. This methodological framework will couple graph theoretic approach and biological data, mainly biodiversity occurrences (presence/absence) data available on both international (e.g., GBIF), national (e.g., Système d’Information Nature et Paysage) and local (e.g., Atlas de la Biodiversté Communale) biodiversity data sharing platforms in order to help reasoning new decisions for ecological networks conservation and restoration in urban areas. An experiment on this subject is currently ongoing with Montpellier Mediterranee Metropole. These projects and studies have shown that only 26% of tree inventory data is currently geo-localized in France - the rest is still being done on paper or Excel sheets. It seems that technology is not yet used enough to enrich the knowledge city councils have about biodiversity in their city and that existing biodiversity open data (e.g., occurrences, telemetry, or genetic data), species distribution models, landscape graph connectivity metrics are still underexploited to make rational decisions for landscape and urban planning projects. This is the goal of ecoTeka: to support easier inventories of urban biodiversity and better management of urban spaces through rational planning and decisions relying on open databases. Future studies and projects will focus on the development of tools for reducing the artificialization of soils, selecting plant species adapted to climate change, and highlighting the need for ecosystem and biodiversity services in cities.

Keywords: digital software, ecological design of urban landscapes, sustainable urban development, urban ecological corridor, urban forestry, urban planning

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Authors: Denis Jordan, Daniel Golkowski, Mathias Lukas, Katharina Merz, Caroline Mlynarcik, Max Maurer, Valentin Riedl, Stefan Foerster, Eberhard F. Kochs, Andreas Bender, Ruediger Ilg

Abstract:

Introduction: So far, clinical assessments that rely on behavioral responses to differentiate coma states or even predict outcome in coma patients are unreliable, e.g. because of some patients’ motor disabilities. The present study was aimed to provide prognosis in coma patients using markers from electroencephalogram (EEG), blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) and [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET). Unsuperwised principal component analysis (PCA) was used for multimodal integration of markers. Methods: Approved by the local ethics committee of the Technical University of Munich (Germany) 20 patients (aged 18-89) with severe brain damage were acquired through intensive care units at the Klinikum rechts der Isar in Munich and at the Therapiezentrum Burgau (Germany). At the day of EEG/fMRI/PET measurement (date I) patients (<3.5 month in coma) were grouped in the minimal conscious state (MCS) or vegetative state (VS) on the basis of their clinical presentation (coma recovery scale-revised, CRS-R). Follow-up assessment (date II) was also based on CRS-R in a period of 8 to 24 month after date I. At date I, 63 channel EEG (Brain Products, Gilching, Germany) was recorded outside the scanner, and subsequently simultaneous FDG-PET/fMRI was acquired on an integrated Siemens Biograph mMR 3T scanner (Siemens Healthineers, Erlangen Germany). Power spectral densities, permutation entropy (PE) and symbolic transfer entropy (STE) were calculated in/between frontal, temporal, parietal and occipital EEG channels. PE and STE are based on symbolic time series analysis and were already introduced as robust markers separating wakefulness from unconsciousness in EEG during general anesthesia. While PE quantifies the regularity structure of the neighboring order of signal values (a surrogate of cortical information processing), STE reflects information transfer between two signals (a surrogate of directed connectivity in cortical networks). fMRI was carried out using SPM12 (Wellcome Trust Center for Neuroimaging, University of London, UK). Functional images were realigned, segmented, normalized and smoothed. PET was acquired for 45 minutes in list-mode. For absolute quantification of brain’s glucose consumption rate in FDG-PET, kinetic modelling was performed with Patlak’s plot method. BOLD signal intensity in fMRI and glucose uptake in PET was calculated in 8 distinct cortical areas. PCA was performed over all markers from EEG/fMRI/PET. Prognosis (persistent VS and deceased patients vs. recovery to MCS/awake from date I to date II) was evaluated using the area under the curve (AUC) including bootstrap confidence intervals (CI, *: p<0.05). Results: Prognosis was reliably indicated by the first component of PCA (AUC=0.99*, CI=0.92-1.00) showing a higher AUC when compared to the best single markers (EEG: AUC<0.96*, fMRI: AUC<0.86*, PET: AUC<0.60). CRS-R did not show prediction (AUC=0.51, CI=0.29-0.78). Conclusion: In a multimodal analysis of EEG/fMRI/PET in coma patients, PCA lead to a reliable prognosis. The impact of this result is evident, as clinical estimates of prognosis are inapt at time and could be supported by quantitative biomarkers from EEG, fMRI and PET. Due to the small sample size, further investigations are required, in particular allowing superwised learning instead of the basic approach of unsuperwised PCA.

Keywords: coma states and prognosis, electroencephalogram, entropy, functional magnetic resonance imaging, machine learning, positron emission tomography, principal component analysis

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