Search results for: stochastic gradient descent

Authors: Zhenyu Zhang, Hsi-Hsien Wei

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Highway networks play a vital role in post-disaster recovery for disaster-damaged areas. Damaged bridges in such networks can disrupt the recovery activities by impeding the transportation of people, cargo, and reconstruction resources. Therefore, rapid restoration of damaged bridges is of paramount importance to long-term disaster recovery. In the post-disaster recovery phase, the key to restoration scheduling for a highway network is prioritization of bridge-repair tasks. Resilience is widely used as a measure of the ability to recover with which a network can return to its pre-disaster level of functionality. In practice, highways will be temporarily blocked during the downtime of bridge restoration, leading to the decrease of highway-network functionality. The failure to take downtime effects into account can lead to overestimation of network resilience. Additionally, post-disaster recovery of highway networks is generally divided into emergency bridge repair (EBR) in the response phase and long-term bridge repair (LBR) in the recovery phase, and both of EBR and LBR are different in terms of restoration objectives, restoration duration, budget, etc. Distinguish these two phases are important to precisely quantify highway network resilience and generate suitable restoration schedules for highway networks in the recovery phase. To address the above issues, this study proposes a novel resilience quantification method for the optimization of long-term bridge repair schedules (LBRS) taking into account the impact of EBR activities and restoration downtime on a highway network’s functionality. A time-dependent integer program with recursive functions is formulated for optimally scheduling LBR activities. Moreover, since uncertainty always exists in the LBRS problem, this paper extends the optimization model from the deterministic case to the stochastic case. A hybrid genetic algorithm that integrates a heuristic approach into a traditional genetic algorithm to accelerate the evolution process is developed. The proposed methods are tested using data from the 2008 Wenchuan earthquake, based on a regional highway network in Sichuan, China, consisting of 168 highway bridges on 36 highways connecting 25 cities/towns. The results show that, in this case, neglecting the bridge restoration downtime can lead to approximately 15% overestimation of highway network resilience. Moreover, accounting for the impact of EBR on network functionality can help to generate a more specific and reasonable LBRS. The theoretical and practical values are as follows. First, the proposed network recovery curve contributes to comprehensive quantification of highway network resilience by accounting for the impact of both restoration downtime and EBR activities on the recovery curves. Moreover, this study can improve the highway network resilience from the organizational dimension by providing bridge managers with optimal LBR strategies.

Keywords: disaster management, highway network, long-term bridge repair schedule, resilience, restoration downtime

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Authors: Justo Rodriguez, Daming Chen, Amador M. Guzman

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The detection, treatment, and control of rapidly propagating, deadly viruses such as COVID-19, require the development of inexpensive, fast, and accurate devices to address the urgent needs of the population. Microfluidics-based sensors are amongst the different methods and techniques for detection that are easy to use. A micro analyzer is defined as a microfluidics-based sensor, composed of a network of microchannels with varying functions. Given their size, portability, and accuracy, they are proving to be more effective and convenient than other solutions. A micro analyzer based on the concept of “Lab on a Chip” presents advantages concerning other non-micro devices due to its smaller size, and it is having a better ratio between useful area and volume. The integration of multiple processes in a single microdevice reduces both the number of necessary samples and the analysis time, leading the next generation of analyzers for the health-sciences. In some applications, the flow of solution within the microchannels is originated by a pressure gradient, which can produce adverse effects on biological samples. A more efficient and less dangerous way of controlling the flow in a microchannel-based analyzer is applying an electric field to induce the fluid motion and either enhance or suppress the mixing process. Electrokinetic flows are characterized by no less than two non-dimensional parameters: the electric Rayleigh number and its geometrical aspect ratio. In this research, stable and unstable flows have been studied numerically (and when possible, will be experimental) in a T-shaped microchannel. Additionally, unstable electrokinetic flows for Rayleigh numbers higher than critical have been characterized. The flow mixing enhancement was quantified in relation to the stretching and folding that fluid particles undergo when they are subjected to supercritical electrokinetic flows. Computational simulations were carried out using a finite element-based program while working with the flow mixing concepts developed by Gollub and collaborators. Hundreds of seeded massless particles were tracked along the microchannel from the entrance to exit for both stable and unstable flows. After post-processing, their trajectories, the folding and stretching values for the different flows were found. Numerical results show that for supercritical electrokinetic flows, the enhancement effects of the folding and stretching processes become more apparent. Consequently, there is an improvement in the mixing process, ultimately leading to a more homogenous mixture.

Keywords: microchannel, stretching and folding, electro kinetic flow mixing, micro-analyzer

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Authors: Eva L. Suarez, Daniel E. Meeroff, Yan Yong

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The U.S. Federal Emergency Management Agency (FEMA) developed flood maps based on experts’ experience and estimates of the probability of flooding. Current flood-risk models evaluate flood risk with regional and subjective measures without impact from torrential rain and nuisance flooding at the neighborhood level. Nuisance flooding occurs in small areas in the community, where a few streets or blocks are routinely impacted. This type of flooding event occurs when torrential rainstorm combined with high tide and sea level rise temporarily exceeds a given threshold. In South Florida, this threshold is 1.7 ft above Mean Higher High Water (MHHW). The National Weather Service defines torrential rain as rain deposition at a rate greater than 0.3-inches per hour or three inches in a single day. Data from the Florida Climate Center, 1970 to 2020, shows 371 events with more than 3-inches of rain in a day in 612 months. The purpose of this research is to develop a data-driven method to determine comprehensive analytical damage-avoidance criteria that account for nuisance flood events at the single-family home level. The method developed uses the Failure Mode and Effect Analysis (FMEA) method from the American Society of Quality (ASQ) to estimate the Damage Avoidance (DA) preparation for a 1-day 100-year storm. The Consequence of Nuisance Flooding (CoNF) is estimated from community mitigation efforts to prevent nuisance flooding damage. The Probability of Nuisance Flooding (PoNF) is derived from the frequency and duration of torrential rainfall causing delays and community disruptions to daily transportation, human illnesses, and property damage. Urbanization and population changes are related to the U.S. Census Bureau's annual population estimates. Data collected by the United States Department of Agriculture (USDA) Natural Resources Conservation Service’s National Resources Inventory (NRI) and locally by the South Florida Water Management District (SFWMD) track the development and land use/land cover changes with time. The intent is to include temporal trends in population density growth and the impact on land development. Results from this investigation provide the risk of nuisance flooding as a function of CoNF and PoNF for coastal areas of South Florida. The data-based criterion provides awareness to local municipalities on their flood-risk assessment and gives insight into flood management actions and watershed development.

Keywords: flood risk, nuisance flooding, urban flooding, FMEA

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Authors: Emiliano Matta

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Passive resonant vibration absorbers are among the most widely used dynamic control systems in civil engineering. They typically consist in a single-degree-of-freedom mechanical appendage of the main structure, tuned to one structural target mode through frequency and damping optimization. One classical scheme is the pendulum absorber, whose mass is constrained to move along a curved trajectory and is damped by viscous dashpots. Even though the principle is well known, the search for improved arrangements is still under way. In recent years this investigation inspired a type of bidirectional pendulum absorber (BPA), consisting of a mass constrained to move along an optimal three-dimensional (3D) concave surface. For such a BPA, the surface principal curvatures are designed to ensure a bidirectional tuning of the absorber to both principal modes of the main structure, while damping is produced either by horizontal viscous dashpots or by vertical friction dashpots, connecting the BPA to the main structure. In this paper, a variant of BPA is proposed, where damping originates from the variable tangential friction force which develops between the pendulum mass and the 3D surface as a result of a spatially-varying friction coefficient pattern. Namely, a friction coefficient is proposed that varies along the pendulum surface in proportion to the modulus of the 3D surface gradient. With such an assumption, the dissipative model of the absorber can be proven to be nonlinear homogeneous in the small displacement domain. The resulting homogeneous BPA (HBPA) has a fundamental advantage over conventional friction-type absorbers, because its equivalent damping ratio results independent on the amplitude of oscillations, and therefore its optimal performance does not depend on the excitation level. On the other hand, the HBPA is more compact than viscously damped BPAs because it does not need the installation of dampers. This paper presents the analytical model of the HBPA and an optimal methodology for its design. Numerical simulations of single- and multi-story building structures under wind and earthquake loads are presented to compare the HBPA with classical viscously damped BPAs. It is shown that the HBPA is a promising alternative to existing BPA types and that homogeneous tangential friction is an effective means to realize systems provided with amplitude-independent damping.

Keywords: amplitude-independent damping, homogeneous friction, pendulum nonlinear dynamics, structural control, vibration resonant absorbers

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Authors: Oskar Sekowski

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Current global warming causes changes in the traditional zones of viticulture worldwide. During 20th century, the average global air temperature increased by 0.89˚C. The models of climate change indicate that viticulture, currently concentrating in narrow geographic niches, may move towards the poles, to higher geographic latitudes. Global warming may cause changes in traditional viticulture regions. Therefore, there is a need to estimate the climatic conditions and climate change in areas that are not traditionally associated with viticulture, e.g., Poland. The primary objective of this paper is to prepare methodology to evaluate the climatic and meteorological potential for viticulture in Poland based on a case study. Moreover, the additional aim is to evaluate the climatic potential of a mesoregion where a university vineyard is located. The daily data of temperature, precipitation, insolation, and wind speed (1988-2018) from the meteorological station located in Łazy, southern Poland, was used to evaluate 15 climatological parameters and indices connected with viticulture. The next steps of the methodology are based on Geographic Information System methods. The topographical factors such as a slope gradient and slope exposure were created using Digital Elevation Models. The spatial distribution of climatological elements was interpolated by ordinary kriging. The values of each factor and indices were also ranked and classified. The viticultural potential was determined by integrating two suitability maps, i.e., the topographical and climatic ones, and by calculating the average for each pixel. Data analysis shows significant changes in heat accumulation indices that are driven by increases in maximum temperature, mostly increasing number of days with Tmax > 30˚C. The climatic conditions of this mesoregion are sufficient for vitis vinifera viticulture. The values of indicators and insolation are similar to those in the known wine regions located on similar geographical latitudes in Europe. The smallest threat to viticulture in study area is the occurrence of hail and the highest occurrence of frost in the winter. This research provides the basis for evaluating general suitability and climatologic potential for viticulture in Poland. To characterize the climatic potential for viticulture, it is necessary to assess the suitability of all climatological and topographical factors that can influence viticulture. The methodology used in this case study shows places where there is a possibility to create vineyards. It may also be helpful for wine-makers to select grape varieties.

Keywords: climatologic potential, climatic classification, Poland, viticulture

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Authors: Hela Ayeb Mrabtini, Ghazi Bellakhal, Jamel Chahed

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The presence of the dispersed phase in gas-liquid bubbly flow considerably alters the liquid turbulence. The bubbles induce turbulent fluctuations that enhance the global liquid turbulence level and alter the mechanisms of turbulence. RANS modeling of uniformly sheared flows on an isolated sphere centered in a control volume is performed using first and second order turbulence closures. The sphere is placed in the production-dissipation equilibrium zone where the liquid velocity is set equal to the relative velocity of the bubbles. The void fraction is determined by the ratio between the sphere volume and the control volume. The analysis of the turbulence statistics on the control volume provides numerical results that are interpreted with regard to the effect of the bubbles wakes on the turbulence structure in uniformly sheared bubbly flow. We assumed for this purpose that at low void fraction where there is no hydrodynamic interaction between the bubbles, the single-phase flow simulation on an isolated sphere is representative on statistical average of a sphere network. The numerical simulations were firstly validated against the experimental data of bubbly homogeneous turbulence with constant shear and then extended to produce numerical results for a wide range of shear rates from 0 to 10 s^-1. These results are compared with our turbulence closure proposed for gas-liquid bubbly flows. In this closure, the turbulent stress tensor in the liquid is split into a turbulent dissipative part produced by the gradient of the mean velocity which also contains the turbulence generated in the bubble wakes and a pseudo-turbulent non-dissipative part induced by the bubbles displacements. Each part is determined by a specific transport equation. The simulations of uniformly sheared flows on an isolated sphere reproduce the mechanisms related to the turbulent part, and the numerical results are in perfect accordance with the modeling of the transport equation of the turbulent part. The reduction of second order turbulence closure provides a description of the modification of turbulence structure by the bubbles presence using a dimensionless number expressed in terms of two-time scales characterizing the turbulence induced by the shear and that induced by bubbles displacements. The numerical simulations carried out in the framework of a comprehensive analysis reproduce particularly the attenuation of the turbulent friction showed in the experimental results of bubbly homogeneous turbulence subjected to a constant shear.

Keywords: gas-liquid bubbly flows, homogeneous turbulence, turbulence closure, uniform shear

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Authors: Azimollah Aleshzadeh, Enver Vural Yavuz

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The main goal of this study is to produce landslide susceptibility maps using different data-driven bivariate statistical approaches; namely, entropy weight method (EWM), evidence belief function (EBF), and information content model (ICM), at Uzundere county, Erzurum province, in the north-eastern part of Turkey. Past landslide occurrences were identified and mapped from an interpretation of high-resolution satellite images, and earlier reports as well as by carrying out field surveys. In total, 42 landslide incidence polygons were mapped using ArcGIS 10.4.1 software and randomly split into a construction dataset 70 % (30 landslide incidences) for building the EWM, EBF, and ICM models and the remaining 30 % (12 landslides incidences) were used for verification purposes. Twelve layers of landslide-predisposing parameters were prepared, including total surface radiation, maximum relief, soil groups, standard curvature, distance to stream/river sites, distance to the road network, surface roughness, land use pattern, engineering geological rock group, topographical elevation, the orientation of slope, and terrain slope gradient. The relationships between the landslide-predisposing parameters and the landslide inventory map were determined using different statistical models (EWM, EBF, and ICM). The model results were validated with landslide incidences, which were not used during the model construction. In addition, receiver operating characteristic curves were applied, and the area under the curve (AUC) was determined for the different susceptibility maps using the success (construction data) and prediction (verification data) rate curves. The results revealed that the AUC for success rates are 0.7055, 0.7221, and 0.7368, while the prediction rates are 0.6811, 0.6997, and 0.7105 for EWM, EBF, and ICM models, respectively. Consequently, landslide susceptibility maps were classified into five susceptibility classes, including very low, low, moderate, high, and very high. Additionally, the portion of construction and verification landslides incidences in high and very high landslide susceptibility classes in each map was determined. The results showed that the EWM, EBF, and ICM models produced satisfactory accuracy. The obtained landslide susceptibility maps may be useful for future natural hazard mitigation studies and planning purposes for environmental protection.

Keywords: entropy weight method, evidence belief function, information content model, landslide susceptibility mapping

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Authors: Aya Al Masri, Naima Oubenali, Safoin Aktaou, Thibault Julien, Malorie Martin, Fouad Maaloul

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Introduction: The increased number of performed 'Computed Tomography (CT)' examinations raise public concerns regarding associated stochastic risk to patients. In its Publication 102, the ‘International Commission on Radiological Protection (ICRP)’ emphasized the importance of managing patient dose, particularly from repeated or multiple examinations. We developed a Dose Archiving and Communication System that gives multiple dose indexes (organ dose, effective dose, and skin-dose mapping) for patients undergoing radiological imaging exams. The aim of this study is to compare the organ dose values given by our software for patients undergoing CT exams with those of another software named "VirtualDose". Materials and methods: Our software uses Monte Carlo simulations to calculate organ doses for patients undergoing computed tomography examinations. The general calculation principle consists to simulate: (1) the scanner machine with all its technical specifications and associated irradiation cases (kVp, field collimation, mAs, pitch ...) (2) detailed geometric and compositional information of dozens of well identified organs of computational hybrid phantoms that contain the necessary anatomical data. The mass as well as the elemental composition of the tissues and organs that constitute our phantoms correspond to the recommendations of the international organizations (namely the ICRP and the ICRU). Their body dimensions correspond to reference data developed in the United States. Simulated data was verified by clinical measurement. To perform the comparison, 270 adult patients and 150 pediatric patients were used, whose data corresponds to exams carried out in France hospital centers. The comparison dataset of adult patients includes adult males and females for three different scanner machines and three different acquisition protocols (Head, Chest, and Chest-Abdomen-Pelvis). The comparison sample of pediatric patients includes the exams of thirty patients for each of the following age groups: new born, 1-2 years, 3-7 years, 8-12 years, and 13-16 years. The comparison for pediatric patients were performed on the “Head” protocol. The percentage of the dose difference were calculated for organs receiving a significant dose according to the acquisition protocol (80% of the maximal dose). Results: Adult patients: for organs that are completely covered by the scan range, the maximum percentage of dose difference between the two software is 27 %. However, there are three organs situated at the edges of the scan range that show a slightly higher dose difference. Pediatric patients: the percentage of dose difference between the two software does not exceed 30%. These dose differences may be due to the use of two different generations of hybrid phantoms by the two software. Conclusion: This study shows that our software provides a reliable dosimetric information for patients undergoing Computed Tomography exams.

Keywords: adult and pediatric patients, computed tomography, organ dose calculation, software comparison

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Authors: Mostafa Shahriari, Theophile Chaumont-Frelet, David Pardo

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Resistivity measurements are used to characterize the Earth’s subsurface. They are categorized into two different groups: (a) those acquired on the Earth’s surface, for instance, controlled source electromagnetic (CSEM) and Magnetotellurics (MT), and (b) those recorded with borehole logging instruments such as Logging-While-Drilling (LWD) devices. LWD instruments are mostly used for geo-steering purposes, i.e., to adjust dip and azimuthal angles of a well trajectory to drill along a particular geological target. Modern LWD tools measure all nine components of the magnetic field corresponding to three orthogonal transmitter and receiver orientations. In order to map the Earth’s subsurface and perform geo-steering, we invert measurements using a gradient-based method that utilizes the derivatives of the recorded measurements with respect to the inversion variables. For resistivity measurements, these inversion variables are usually the constant resistivity value of each layer and the bed boundary positions. It is well-known how to compute derivatives with respect to the constant resistivity value of each layer using semi-analytic or numerical methods. However, similar formulas for computing the derivatives with respect to bed boundary positions are unavailable. The main contribution of this work is to provide an adjoint-based formulation for computing derivatives with respect to the bed boundary positions. The key idea to obtain the aforementioned adjoint state formulations for the derivatives is to separate the tangential and normal components of the field and treat them differently. This formulation allows us to compute the derivatives faster and more accurately than with traditional finite differences approximations. In the presentation, we shall first derive a formula for computing the derivatives with respect to the bed boundary positions for the potential equation. Then, we shall extend our formulation to 3D Maxwell’s equations. Finally, by considering a 1D domain and reducing the dimensionality of the problem, which is a common practice in the inversion of resistivity measurements, we shall derive a formulation to compute the derivatives of the measurements with respect to the bed boundary positions using a 1.5D variational formulation. Then, we shall illustrate the accuracy and convergence properties of our formulations by comparing numerical results with the analytical derivatives for the potential equation. For the 1.5D Maxwell’s system, we shall compare our numerical results based on the proposed adjoint-based formulation vs those obtained with a traditional finite difference approach. Numerical results shall show that our proposed adjoint-based technique produces enhanced accuracy solutions while its cost is negligible, as opposed to the finite difference approach that requires the solution of one additional problem per derivative.

Keywords: inverse problem, bed boundary positions, electromagnetism, potential equation

Procedia PDF Downloads 178

Authors: Yuanyuan Zhang, Yujuan Zheng, Giuseppina Barutello, Sumako Kameishi, Kungchun Chiu, Katharina Hennig, Martial Balland, Federica Cavallo, Lars Holmgren

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Angiogenesis describes that new blood vessels migrate from pre-existing ones to form 3D lumenized structure and remodeling. During directional migration toward the gradient of pro-angiogenic factors, the endothelial cells, especially the tip cells need filopodia to sense the environment and exert the pulling force. Of particular interest are the integrin proteins, which play an essential role in focal adhesion in the connection between migrating cells and extracellular matrix (ECM). Understanding how these biomechanical complexes orchestrate intrinsic and extrinsic forces is important for our understanding of the underlying mechanisms driving angiogenesis. We have previously identified Angiomotin (Amot), a member of Amot scaffold protein family, as a promoter for endothelial cell migration in vitro and zebrafish models. Hence, we established inducible endothelial-specific Amot knock-out mice to study normal retinal angiogenesis as well as tumor angiogenesis. We found that the migration ratio of the blood vessel network to the edge was significantly decreased in Amotec- retinas at postnatal day 6 (P6). While almost all the Amot defect tip cells lost migration advantages at P7. In consistence with the dramatic morphology defect of tip cells, there was a non-autonomous defect in astrocytes, as well as the disorganized fibronectin expression pattern correspondingly in migration front. Furthermore, the growth of transplanted LLC tumor was inhibited in Amot knockout mice due to fewer vasculature involved. By using MMTV-PyMT transgenic mouse model, there was a significantly longer period before tumors arised when Amot was specifically knocked out in blood vessels. In vitro evidence showed that Amot binded to beta-actin, Integrin beta 1 (ITGB1), Fibronectin, FAK, Vinculin, major focal adhesion molecules, and ITGB1 and stress fibers were distinctly induced by Amot transfection. Via traction force microscopy, the total energy (force indicater) was found significantly decreased in Amot knockdown cells. Taken together, we propose that Amot is a novel partner of the ITGB1/Fibronectin protein complex at focal adhesion and required for exerting force transition between endothelial cell and extracellular matrix.

Keywords: angiogenesis, angiomotin, endothelial cell migration, focal adhesion, integrin beta 1

Procedia PDF Downloads 237

Authors: Rabbia Idrees, Ananda Maiti, Saurabh Garg, Muhammad Bilal Amin

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IoT devices are the basic building blocks of IoT network that generate enormous volume of real-time and high-speed data to help organizations and companies to take intelligent decisions. To integrate this enormous data from multisource and transfer it to the appropriate client is the fundamental of IoT development. The handling of this huge quantity of devices along with the huge volume of data is very challenging. The IoT devices are battery-powered and resource-constrained and to provide energy efficient communication, these IoT devices go sleep or online/wakeup periodically and a-periodically depending on the traffic loads to reduce energy consumption. Sometime these devices get disconnected due to device battery depletion. If the node is not available in the network, then the IoT network provides incomplete, missing, and inaccurate data. Moreover, many IoT applications, like vehicle tracking and patient tracking require the IoT devices to be mobile. Due to this mobility, If the distance of the device from the sink node become greater than required, the connection is lost. Due to this disconnection other devices join the network for replacing the broken-down and left devices. This make IoT devices dynamic in nature which brings uncertainty and unreliability in the IoT network and hence produce bad quality of data. Due to this dynamic nature of IoT devices we do not know the actual reason of abnormal data. If data are of poor-quality decisions are likely to be unsound. It is highly important to process data and estimate data quality before bringing it to use in IoT applications. In the past many researchers tried to estimate data quality and provided several Machine Learning (ML), stochastic and statistical methods to perform analysis on stored data in the data processing layer, without focusing the challenges and issues arises from the dynamic nature of IoT devices and how it is impacting data quality. A comprehensive review on determining the impact of dynamic nature of IoT devices on data quality is done in this research and presented a data quality model that can deal with this challenge and produce good quality of data. This research presents the data quality model for the sensors monitoring water quality. DBSCAN clustering and weather sensors are used in this research to make data quality model for the sensors monitoring water quality. An extensive study has been done in this research on finding the relationship between the data of weather sensors and sensors monitoring water quality of the lakes and beaches. The detailed theoretical analysis has been presented in this research mentioning correlation between independent data streams of the two sets of sensors. With the help of the analysis and DBSCAN, a data quality model is prepared. This model encompasses five dimensions of data quality: outliers’ detection and removal, completeness, patterns of missing values and checks the accuracy of the data with the help of cluster’s position. At the end, the statistical analysis has been done on the clusters formed as the result of DBSCAN, and consistency is evaluated through Coefficient of Variation (CoV).

Keywords: clustering, data quality, DBSCAN, and Internet of things (IoT)

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Authors: Mohamed Malek Khenissi, Mohamed Montassar Ben Slama, Anis Belhaj Mohamed, Moncef Saidi

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Northern Tunisia is well known by its different and complex structural and geological zones that have been the result of a geodynamic history that extends from the early Mesozoic era to the actual period. One of these zones is the salt province, where the Halokinesis process is manifested by a number of NE/SW salt structures such as Jebel Ech-Cheid which represents masses of materials characterized by a high plasticity and low density. The salt masses extrusions that have been developed due to an extension that started from the late Triassic to late Cretaceous. The evolution of salt bodies within sedimentary basins have not only contributed to modify the architecture of the basin, but it also has certain geochemical effects which touch mainly source rocks that surround it. It has been demonstrated that the presence of salt structures within sedimentary basins can influence its temperature distribution and thermal history. Moreover, it has been creating heat flux anomalies that may affect the maturity of organic matter and the timing of hydrocarbon generation. Field samples of the Bahloul source rock (Cenomanan-Tunonian) were collected from different sights from all around Ech Cheid salt structure and evaluated using Rock-eval pyrolysis and GC/MS techniques in order to assess the degree of maturity evolution and the heat flux anomalies in the different zones analyze. The Total organic Carbon (TOC) values range between 1 to 9% and the (Tmax) ranges between 424 and 445°C, also the distribution of the source rock biomarkers both saturated and aromatic changes in a regular fashions with increasing maturity and this are shown in the chromatography results such as Ts/(Ts+Tm) ratios, 22S/(22S+22R) values for C31 homohopanes, ββ/(ββ+αα)20R and 20S/(20S+20R) ratios for C29 steranes which gives a consistent maturity indications and assessment of the field samples. These analyses are carried to interpret the maturity evolution and the heat flux around Ech Cheid salt structure through the geological history. These analyses also aim to demonstrate that the salt structure can have a direct effect on the geothermal gradient of the basin and on the maturity of the Bahloul Formation source rock. The organic matter has reached different stages of thermal maturity, but delineate a general increasing maturity trend. Our study confirms that the J. Ech Cheid salt body have on the first hand: a huge influence on the local distribution of anoxic depocentre at least within Cenomanian-Turonian time. In the second hand, the thermal anomaly near the salt mass has affected the maturity of Bahloul Formation.

Keywords: Bahloul formation, depocentre, GC/MS, rock-eval

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Authors: Guntur Bagus Pamungkas

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As part of a tropical country that is normally rich in forest land areas, Indonesia has always been in the world's spotlight due to its significantly increasing process of deforestation. In one hand, it is related to the mainstay for maintaining the sustainability of the earth's ecosystem functions. On the other hand, they also cover the various potential sources of the global economy. Therefore, it can always be the target of different scale of investors to excessively exploit them. No wonder the emergence of disasters in various characteristics always comes up. In fact, the deforestation phenomenon does not only occur in various forest land areas in the main islands of Indonesia but also includes Java Island, the most densely populated areas in the world. This island only remains the forest land of about 9.8% of the total forest land in Indonesia due to its long history of it, especially in Central Java Province, the most densely populated area in Java. Again, not surprisingly, this province belongs to the area with the highest frequency of disasters because of it, landslides in particular. One of the areas that often experience it is Banjarnegara District, especially in mountainous areas that lies in the range from 1000 to 3000 meters above sea level, where the remains of land forest area can easyly still be found. Even among them still leaves less untouchable tropical rain forest whose area also covers part of a neighboring district, Pekalongan, which is considered to be the rest of the world's little paradise on Earth. The district's landscape is indeed beautiful, especially in the Dieng area, a major tourist destination in Central Java Province after Borobudur Temple. However, annually hazardous always threatens this district due to this landslide disaster. Even, there was a tragic event that was buried with its inhabitants a few decades ago. This research aims to find part of the concept of effective forest management through monitoring the presence of remaining forest areas in this area. The research implemented monitoring of deforestation rates using the Stochastic Cellular Automata-Markov Chain (SCA-MC) method, which serves to provide a spatial simulation of land use and cover changes (LULCC). This geospatial process uses the Landsat-8 OLI image product with Thermal Infra-Red Sensors (TIRS) Band 10 in 2020 and Landsat 5 TM with TIRS Band 6 in 2010. Then it is also integrated with physical and social geography issues using the QGIS 2.18.11 application with the Mollusce Plugin, which serves to clarify and calculate the area of land use and cover, especially in forest areas—using the LULCC method, which calculates the rate of forest area reduction in 2010-2020 in Banjarnegara District. Since the dependence of this area on the use of forest land is quite high, concepts and preventive actions are needed, such as rehabilitation and reforestation of critical lands through providing proper monitoring and targeted forest management to restore its ecosystem in the future.

Keywords: deforestation, populous area, LULCC method, proper control and effective forest management

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Authors: Anders Schou Simonsen, Kim Sorensen, Thomas Condra

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Wet scrubbers are used for flue gas desulfurization by injecting water directly into the flue gas stream from a set of sprayers. The water droplets will flow freely inside the scrubber, and flow down along the scrubber walls as a thin wall film while reacting with the gas phase to remove SO₂. This complex multiphase phenomenon can be divided into three main contributions: the continuous gas phase, the liquid droplet phase, and the liquid wall film phase. This study proposes a complete model, where all three main contributions are taken into account and resolved using OpenFOAM for the continuous gas phase, and MATLAB for the liquid droplet and wall film phases. The 3D continuous gas phase is composed of five species: CO₂, H₂O, O₂, SO₂, and N₂, which are resolved along with momentum, energy, and turbulence. Source terms are present for four species, energy and momentum, which are affecting the steady-state solution. The liquid droplet phase experiences breakup, collisions, dynamics, internal chemistry, evaporation and condensation, species mass transfer, energy transfer and wall film interactions. Numerous sub-models have been implemented and coupled to realise the above-mentioned phenomena. The liquid wall film experiences impingement, acceleration, atomization, separation, internal chemistry, evaporation and condensation, species mass transfer, and energy transfer, which have all been resolved using numerous sub-models as well. The continuous gas phase has been coupled with the liquid phases using source terms by an approach, where the two software packages are couples using a link-structure. The complete CFD model has been verified using 16 experimental tests from an existing scrubber installation, where a gradient-based pattern search optimization algorithm has been used to tune numerous model parameters to match the experimental results. The CFD model needed to be fast for evaluation in order to apply this optimization routine, where approximately 1000 simulations were needed. The results show that the complex multiphase phenomena governing wet scrubbers can be resolved in a single model. The optimization routine was able to tune the model to accurately predict the performance of an existing installation. Furthermore, the study shows that a coupling between OpenFOAM and MATLAB is realizable, where the data and source term exchange increases the computational requirements by approximately 5%. This allows for exploiting the benefits of both software programs.

Keywords: desulfurization, discrete phase, scrubber, wall film

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Authors: Rodrigo Fernandes da Silva, Daniela Maira Cardozo, Paulo Cesar Martins Alves, Sophie Françoise Derchain, Fernando Guimarães

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T-reg lymphocytes are important for the control of peripheral tolerance. They control the adaptive immune system and prevent autoimmunity through its suppressive action on CD4+ and CD8+ lymphocytes. The suppressive action also includes B lymphocytes, dendritic cells, monocytes/macrophages and recently, studies have shown that T-reg are also able to inhibit NK cells, therefore they exert their control of the immune response from innate to adaptive response. Most tumors express self-ligands, therefore it is believed that T-reg cells induce tolerance of the immune system, hindering the development of successful immunotherapies. T-reg cells have been linked to the suppression mechanisms of the immune response against tumors, including ovarian cancer. The goal of this study was to disclose the sub-population of the expanded CD3+ lymphocytes reported by previous studies, using the long-term culture model designed by Carlens et al 2001, to generate effector cell suspensions enriched with cytotoxic CD3-CD56+ NK cells, from PBMC of ovarian neoplasia patients. Methods and Results: Blood was collected from 12 patients with ovarian neoplasia after signed consent: 7 benign (Bng) and 5 malignant (Mlg). Mononuclear cells were separated by Ficoll-Paque gradient. Long-term culture was conducted by a 21 day culturing process with SCGM CellGro medium supplemented with anti-CD3 (10ng/ml, first 5 days), IL-2 (1000UI/ml) and FBS (10%). After 21 days of expansion, there was an increase in the population of CD3+ lymphocytes in the benign and malignant group. Within CD3+ population, there was a significant decrease in the population of CD4+ lymphocytes in the benign (median Bgn D-0=73.68%, D-21=21.05%) (p<0.05) and malignant (median Mlg D-0=64.00%, D-21=11.97%) (p < 0.01) group. Inversely, after 21 days of expansion, there was an increase in the population of CD8+ lymphocytes within the CD3+ population in the benign (median Bgn D-0=16.80%, D-21=38.56%) and malignant (median Mlg D-0=27.12%, D-21=72.58%) group. However, this increase was only significant on the malignant group (p<0.01). Within the CD3+CD4+ population, there was a significant increase (p < 0.05) in the population of T-reg lymphocytes in the benign (median Bgn D-0=9.84%, D-21=39.47%) and malignant (median Mlg D-0=3.56%, D-21=16.18%) group. Statistical analysis inter groups was performed by Kruskal-Wallis test and intra groups by Mann Whitney test. Conclusion: The CD4+ and CD8+ sub-population of CD3+ lymphocytes shifts with the culturing process. This might be due to the process of the immune system to produce a cytotoxic response. At the same time, T-reg lymphocytes increased within the CD4+ population, suggesting a modulation of the immune response towards cells of the immune system. The expansion of the T-reg population can hinder an immune response against cancer. Therefore, an immunotherapy using this expansion procedure should aim to halt the expansion of T-reg or its immunosuppresion capability.

Keywords: regulatory T cells, CD8+ T cells, CD4+ T cells, NK cell expansion

Procedia PDF Downloads 451

Authors: Roberto M. G. Velásquez, Jonas R. Gazoli, Nelson Ponce Jr, Valério L. Borges, Alessandro Sete, Fernanda M. C. Tomé, Julian D. Hunt, Heitor C. Lira, Cristiano L. de Souza, Fabio T. Bindemann, Wilmar Wounnsoscky

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Nowadays, society is working toward reducing energy losses and greenhouse gas emissions, as well as seeking clean energy sources, as a result of the constant increase in energy demand and emissions. Energy loss occurs in the gas pressure reduction stations at the delivery points in natural gas distribution systems (city gates). Installing pressure reduction turbines (PRT) parallel to the static reduction valves at the city gates enhances the energy efficiency of the system by recovering the enthalpy of the pressurized natural gas, obtaining in the pressure-lowering process shaft work and generating electrical power. Currently, the Brazilian natural gas transportation network has 9,409 km in extension, while the system has 16 national and 3 international natural gas processing plants, including more than 143 delivery points to final consumers. Thus, the potential of installing PRT in Brazil is 66 MW of power, which could yearly avoid the emission of 235,800 tons of CO2 and generate 333 GWh/year of electricity. On the other hand, an economic viability analysis of these energy efficiency projects is commonly carried out based on estimates of the project's cash flow obtained from several variables forecast. Usually, the cash flow analysis is performed using representative values of these variables, obtaining a deterministic set of financial indicators associated with the project. However, in most cases, these variables cannot be predicted with sufficient accuracy, resulting in the need to consider, to a greater or lesser degree, the risk associated with the calculated financial return. This paper presents an approach applied to the technical-economic viability analysis of PRTs projects that explicitly considers the uncertainties associated with the input parameters for the financial model, such as gas pressure at the delivery point, amount of energy generated by TRP, the future price of energy, among others, using sensitivity analysis techniques, scenario analysis, and Monte Carlo methods. In the latter case, estimates of several financial risk indicators, as well as their empirical probability distributions, can be obtained. This is a methodology for the financial risk analysis of PRT projects. The results of this paper allow a more accurate assessment of the potential PRT project's financial feasibility in Brazil. This methodology will be tested at the Cuiabá thermoelectric plant, located in the state of Mato Grosso, Brazil, and can be applied to study the potential in other countries.

Keywords: pressure reduction turbine, natural gas pressure drop station, energy efficiency, electricity generation, monte carlo methods

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Authors: Merve Uzuner Sahin, Kumru D. Atalay, Berna Dengiz

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In today’s competitive market, there are many alternative products that can be used in similar manner and purpose. Therefore, the utility of the product is an important issue for the preferability of the brand. This utility could be measured in terms of its functionality, durability, reliability. These all are affected by the system capabilities. Reliability is an important system design criteria for the manufacturers to be able to have high availability. Availability is the probability that a system (or a component) is operating properly to its function at a specific point in time or a specific period of times. System availability provides valuable input to estimate the production rate for the company to realize the production plan. When considering only the corrective maintenance downtime of the system, mean time between failure (MTBF) and mean time to repair (MTTR) are used to obtain system availability. Also, the MTBF and MTTR values are important measures to improve system performance by adopting suitable maintenance strategies for reliability engineers and practitioners working in a system. Failure and repair time probability distributions of each component in the system should be known for the conventional availability analysis. However, generally, companies do not have statistics or quality control departments to store such a large amount of data. Real events or situations are defined deterministically instead of using stochastic data for the complete description of real systems. A fuzzy set is an alternative theory which is used to analyze the uncertainty and vagueness in real systems. The aim of this study is to present a novel approach to compute system availability using representation of MTBF and MTTR in fuzzy numbers. Based on the experience in the system, it is decided to choose 3 different spread of MTBF and MTTR such as 15%, 20% and 25% to obtain lower and upper limits of the fuzzy numbers. To the best of our knowledge, the proposed method is the first application that is used fuzzy MTBF and fuzzy MTTR for fuzzy system availability estimation. This method is easy to apply in any repairable production system by practitioners working in industry. It is provided that the reliability engineers/managers/practitioners could analyze the system performance in a more consistent and logical manner based on fuzzy availability. This paper presents a real case study of a repairable multi-stage production line in lead-acid battery production factory in Turkey. The following is focusing on the considered wet-charging battery process which has a higher production level than the other types of battery. In this system, system components could exist only in two states, working or failed, and it is assumed that when a component in the system fails, it becomes as good as new after repair. Instead of classical methods, using fuzzy set theory and obtaining intervals for these measures would be very useful for system managers, practitioners to analyze system qualifications to find better results for their working conditions. Thus, much more detailed information about system characteristics is obtained.

Keywords: availability analysis, battery production system, fuzzy sets, triangular fuzzy numbers (TFNs)

Procedia PDF Downloads 224

Authors: Fabrizio Ascione, Martina Borrelli, Rosa Francesca De Masi, Silvia Ruggiero, Giuseppe Peter Vanoli

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The refurbishment of public buildings is one of the key factors of energy efficiency policy of European States. Educational buildings account for the largest share of the oldest edifice with interesting potentialities for demonstrating best practice with regards to high performance and low and zero-carbon design and for becoming exemplar cases within the community. In this context, this paper discusses the critical issue of dealing the energy refurbishment of a university building in heating dominated climate of South Italy. More in detail, the importance of using validated models will be examined exhaustively by proposing an analysis on uncertainties due to modelling assumptions mainly referring to the adoption of stochastic schedules for occupant behavior and equipment or lighting usage. Indeed, today, the great part of commercial tools provides to designers a library of possible schedules with which thermal zones can be described. Very often, the users do not pay close attention to diversify thermal zones and to modify or to adapt predefined profiles, and results of designing are affected positively or negatively without any alarm about it. Data such as occupancy schedules, internal loads and the interaction between people and windows or plant systems, represent some of the largest variables during the energy modelling and to understand calibration results. This is mainly due to the adoption of discrete standardized and conventional schedules with important consequences on the prevision of the energy consumptions. The problem is surely difficult to examine and to solve. In this paper, a sensitivity analysis is presented, to understand what is the order of magnitude of error that is committed by varying the deterministic schedules used for occupation, internal load, and lighting system. This could be a typical uncertainty for a case study as the presented one where there is not a regulation system for the HVAC system thus the occupant cannot interact with it. More in detail, starting from adopted schedules, created according to questioner’ s responses and that has allowed a good calibration of energy simulation model, several different scenarios are tested. Two type of analysis are presented: the reference building is compared with these scenarios in term of percentage difference on the projected total electric energy need and natural gas request. Then the different entries of consumption are analyzed and for more interesting cases also the comparison between calibration indexes. Moreover, for the optimal refurbishment solution, the same simulations are done. The variation on the provision of energy saving and global cost reduction is evidenced. This parametric study wants to underline the effect on performance indexes evaluation of the modelling assumptions during the description of thermal zones.

Keywords: energy simulation, modelling calibration, occupant behavior, university building

Procedia PDF Downloads 141

Authors: Saif Alomari

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The paper aims at giving physical and mathematical descriptions of how the structural parameters of a resonant tunnelling diode (RTD) affect its output characteristics. Specifically, the value of the peak voltage, peak current, peak to valley current ratio (PVCR), and the difference between peak and valley voltages and currents ΔV and ΔI. A simulation-based approach using the Non-Equilibrium Green Function (NEGF) formalism based on the Silvaco ATLAS simulator is employed to conduct a series of designed experiments. These experiments show how the doping concentration in the emitter and collector layers, their thicknesses, and the width of the barriers and the quantum well influence the above-mentioned output characteristics. Each of these parameters was systematically changed while holding others fixed in each set of experiments. Factorial experiments are outside the scope of this work and will be investigated in future. The physics involved in the operation of the device is thoroughly explained and mathematical models based on curve fitting and underlaying physical principles are deduced. The models can be used to design devices with predictable output characteristics. These models were found absent in the literature that the author acanned. Results show that the doping concentration in each region has an effect on the value of the peak voltage. It is found that increasing the carrier concentration in the collector region shifts the peak to lower values, whereas increasing it in the emitter shifts the peak to higher values. In the collector’s case, the shift is either controlled by the built-in potential resulting from the concentration gradient or the conductivity enhancement in the collector. The shift to higher voltages is found to be also related to the location of the Fermi-level. The thicknesses of these layers play a role in the location of the peak as well. It was found that increasing the thickness of each region shifts the peak to higher values until a specific characteristic length, afterwards the peak becomes independent of the thickness. Finally, it is shown that the thickness of the barriers can be optimized for a particular well width to produce the highest PVCR or the highest ΔV and ΔI. The location of the peak voltage is important in optoelectronic applications of RTDs where the operating point of the device is usually the peak voltage point. Furthermore, the PVCR, ΔV, and ΔI are of great importance for building RTD-based oscillators as they affect the frequency response and output power of the oscillator.

Keywords: peak to valley ratio, peak voltage shift, resonant tunneling diodes, structural parameters

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Authors: Valentin Père, Mathieu Milhé, Fabien Baillon, Jean-Louis Dirion

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Microgrids offer potential solutions to meet the need for local grid stability and increase isolated networks autonomy with the integration of intermittent renewable energy production and storage facilities. In such a context, sizing production and storage for a given network is a complex task, highly depending on input data such as power load profile and renewable resource availability. This work aims at developing an operating cost computation methodology for different microgrid designs based on the use of deep reinforcement learning (RL) algorithms to tackle the optimal operation problem in stochastic environments. RL is a data-based sequential decision control method based on Markov decision processes that enable the consideration of random variables for control at a chosen time scale. Agents trained via RL constitute a promising class of Energy Management Systems (EMS) for the operation of microgrids with energy storage. Microgrid sizing (or design) is generally performed by minimizing investment costs and operational costs arising from the EMS behavior. The latter might include economic aspects (power purchase, facilities aging), social aspects (load curtailment), and ecological aspects (carbon emissions). Sizing variables are related to major constraints on the optimal operation of the network by the EMS. In this work, an islanded mode microgrid is considered. Renewable generation is done with photovoltaic panels; an electrochemical battery ensures short-term electricity storage. The controllable unit is a hydrogen tank that is used as a long-term storage unit. The proposed approach focus on the transfer of agent learning for the near-optimal operating cost approximation with deep RL for each microgrid size. Like most data-based algorithms, the training step in RL leads to important computer time. The objective of this work is thus to study the potential of Batch-Constrained Q-learning (BCQ) for the optimal sizing of microgrids and especially to reduce the computation time of operating cost estimation in several microgrid configurations. BCQ is an off-line RL algorithm that is known to be data efficient and can learn better policies than on-line RL algorithms on the same buffer. The general idea is to use the learned policy of agents trained in similar environments to constitute a buffer. The latter is used to train BCQ, and thus the agent learning can be performed without update during interaction sampling. A comparison between online RL and the presented method is performed based on the score by environment and on the computation time.

Keywords: batch-constrained reinforcement learning, control, design, optimal

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Authors: Ahmed O. Apampa, Yinusa A. Jimoh

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The potential economic viability and environmental benefits of using a biomass waste, such as corn cob ash (CCA) as pozzolan in stabilizing soils for road pavement construction in a sub-tropical region was investigated. Corn cob was obtained from Maya in South West Nigeria and processed to ash of characteristics similar to Class C Fly Ash pozzolan as specified in ASTM C618-12. This was then blended with ordinary Portland cement in the CCA:OPC ratios of 1:1, 1:2 and 2:1. Each of these blends was then mixed with lateritic soil of ASHTO classification A-2-6(3) in varying percentages from 0 – 7.5% at 1.5% intervals. The soil-CCA-Cement mixtures were thereafter tested for geotechnical index properties including the BS Proctor Compaction, California Bearing Ratio (CBR) and the Unconfined Compression Strength Test. The tests were repeated for soil-cement mix without any CCA blending. The cost of the binder inputs and optimal blends of CCA:OPC in the stabilized soil were thereafter analyzed by developing algorithms that relate the experimental data on strength parameters (Unconfined Compression Strength, UCS and California Bearing Ratio, CBR) with the bivariate independent variables CCA and OPC content, using Matlab R2011b. An optimization problem was then set up minimizing the cost of chemical stabilization of laterite with CCA and OPC, subject to the constraints of minimum strength specifications. The Evolutionary Engine as well as the Generalized Reduced Gradient option of the Solver of MS Excel 2010 were used separately on the cells to obtain the optimal blend of CCA:OPC. The optimal blend attaining the required strength of 1800 kN/m2 was determined for the 1:2 CCA:OPC as 5.4% mix (OPC content 3.6%) compared with 4.2% for the OPC only option; and as 6.2% mix for the 1:1 blend (OPC content 3%). The 2:1 blend did not attain the required strength, though over a 100% gain in UCS value was obtained over the control sample with 0% binder. Upon the fact that 0.97 tonne of CO2 is released for every tonne of cement used (OEE, 2001), the reduced OPC requirement to attain the same result indicates the possibility of reducing the net CO2 contribution of the construction industry to the environment ranging from 14 – 28.5% if CCA:OPC blends are widely used in soil stabilization, going by the results of this study. The paper concludes by recommending that Nigeria and other developing countries in the sub-tropics with abundant stock of biomass waste should look in the direction of intensifying the use of biomass waste as fuel and the derived ash for the production of pozzolans for road-works, thereby reducing overall green house gas emissions and in compliance with the objectives of the United Nations Framework on Climate Change.

Keywords: corn cob ash, biomass waste, lateritic soil, unconfined compression strength, CO2 emission

Procedia PDF Downloads 373

Authors: Gabriel Wosiak, Felipe Staciaki, Eryka Nobrega, Ernesto Pereira

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Hydrogen is an energy carrier with potential applications in various industries. Alkaline electrolysis is a commonly used method for hydrogen production; however, its energy cost remains relatively high compared to other methods. This is due in part to interfacial pH changes that occur during the electrolysis process. Interfacial pH changes refer to the changes in pH that occur at the interface between the cathode electrode and the electrolyte solution. These changes are caused by the electrochemical reactions at both electrodes, which consume or produces hydroxide ions (OH-) from the electrolyte solution. This results in an important change in the local pH at the electrode surface, which can have several impacts on the energy consumption and durability of electrolysers. One impact of interfacial pH changes is an increase in the overpotential required for hydrogen production. Overpotential is the difference between the theoretical potential required for a reaction to occur and the actual potential that is applied to the electrodes. In the case of water electrolysis, the overpotential is caused by a number of factors, including the mass transport of reactants and products to and from the electrodes, the kinetics of the electrochemical reactions, and the interfacial pH. An increase in the interfacial pH at the anode surface in alkaline conditions can lead to an increase in the overpotential for hydrogen production. This is because the lower local pH makes it more difficult for the hydroxide ions to be oxidized. As a result, there is an increase in the required energy to the process occur. In addition to increasing the overpotential, interfacial pH changes can also lead to the degradation of the electrodes. This is because the lower pH can make the electrode more susceptible to corrosion. As a result, the electrodes may need to be replaced more frequently, which can increase the overall cost of water electrolysis. The method presented in the paper addresses the issue of interfacial pH changes by using a cell design with a different cell design, introducing the electrode asymmetry. This design helps to mitigate the pH gradient at the anode/electrolyte interface, which reduces the overpotential and improves the energy efficiency of the electrolyser. The method was tested using a multivariate approach in both laboratory and industrial current density conditions and validated the results with numerical simulations. The results demonstrated a clear improvement (11.6%) in energy efficiency, providing an important contribution to the field of sustainable energy production. The findings of the paper have important implications for the development of cost-effective and sustainable hydrogen production methods. By mitigating interfacial pH changes, it is possible to improve the energy efficiency of alkaline electrolysis and make it a more competitive option for hydrogen production.

Keywords: electrolyser, interfacial pH, numerical simulation, optimization, asymmetric cell

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Authors: Engy Shokry, Giancarlo La Marca, Maria Luisa Della Bona

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Newborn screening for Congenital Adrenal Hyperplasia (CAH) relies on quantification of 17α-hydroxyprogesterone using enzyme immunoassays. These assays, in spite of being rapid, readily available and easy to perform, its reliability was found questionable due to lack of selectivity and specificity resulting in large number of false-positives, consequently family anxiety and associated hospitalization costs. To improve specificity of conventional 17α-hydroxyprogesterone screening which may experience false transient elevation in preterm, low birth weight or acutely ill neonates, steroid profiling by LC-MS/MS as a second-tier test was implemented. Unlike the previously applied LC-MS/MS methods, with the disadvantage of requiring a relatively high number of blood drops. Since newborn screening tests are increasing, it is necessary to minimize the sample volume requirement to make the maximum use of blood samples collected on filter paper. The proposed new method requires just one 3.2 mm dried blood spot (DBS) punch. Extraction was done using methanol: water: formic acid (90:10:0.1, v/v/v) containing deuterium labelled internal standards. Extracts were evaporated and reconstituted in 10 % acetone in water. Column switching strategy for on-line sample clean-up was applied to improve the chromatographic run. The first separative step retained the investigated steroids and passed through the majority of high molecular weight impurities. After the valve switching, the investigated steroids are back flushed from the POROS® column onto the analytical column and separated using gradient elution. Found quantitation limits were 5, 10 and 50 nmol/L for 17α-hydroxyprogesterone, androstenedione and cortisol respectively with mean recoveries of between 98.31-103.24 % and intra-/ inter-assay CV% < 10 % except at LLOQ. The method was validated using standard addition calibration and isotope dilution strategies. Reference ranges were determined by analysing samples from 896 infants of various ages at the time of sample collection. The method was also applied on patients with confirmed CAH. Our method represents an attractive combination of low sample volume requirement, minimal sample preparation time without derivatization and quick chromatography (5 min). The three steroid profile and the concentration ratios (17OHP + androstenedione/cortisol) allowed better screening outcomes of CAH reducing false positives, associated costs and anxiety.

Keywords: congenital adrenal hyperplasia (CAH), 17α-hydroxyprogesterone, androstenedione, cortisol, LC-MS/MS

Procedia PDF Downloads 439

Authors: Panagiotis Lemonakis, Theodoros Αlimonakis, George Kaliabetsos, Nikos Eliou

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It is very well known that certain vertical and longitudinal slopes have to be assured in order to achieve adequate rainwater runoff from the pavement. The selection of longitudinal slopes, between the turning points of the vertical curves that meet the afore-mentioned requirement does not ensure adequate drainage because the same condition must also be applied at the transition curves. In this way none of the pavement edges’ slopes (as well as any other spot that lie on the pavement) will be opposite to the longitudinal slope of the rotation axis. Horizontal and vertical alignment must be properly combined in order to form a road which resultant slope does not take small values and hence, checks must be performed in every cross section and every chainage of the road. The present research investigates the rain water runoff from the road surface in order to identify the conditions under which, areas of inadequate drainage are being created, to analyze the rainwater behavior in such areas, to provide design examples of good and bad drainage zones and to track down certain motorcycle types which might encounter hazardous situations due to the presence of water film between the pavement and both of their tires resulting loss of traction. Moreover, it investigates the combination of longitudinal and cross slope values in critical pavement areas. It should be pointed out that the drainage gradient is analytically calculated for the whole road width and not just for an oblique slope per chainage (combination of longitudinal grade and cross slope). Lastly, various combinations of horizontal and vertical design are presented, indicating the crucial zones of bad pavement drainage. The key conclusion of the study is that any type of motorcycle will travel for some time inside the area of improper runoff for a certain time frame which depends on the speed and the trajectory that the rider chooses along the transition curve. Taking into account that on this section the rider will have to lean his motorcycle and hence reduce the contact area of his tire with the pavement it is apparent that any variations on the friction value due to the presence of a water film may lead to serious problems regarding his safety. The water runoff from the road pavement is improved when between reverse longitudinal slopes, crest instead of sag curve is chosen and particularly when its edges coincide with the edges of the horizontal curve. Lastly, the results of the investigation have shown that the variation of the longitudinal slope involves the vertical shift of the center of the poor water runoff area. The magnitude of this area increases as the length of the transition curve increases.

Keywords: drainage, motorcycle safety, superelevation, transition curves, vertical grade

Procedia PDF Downloads 100

Authors: Stephen L. Green, Alexander N. Gorban, Ivan Y. Tyukin

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Within the past decade, using Convolutional Neural Networks (CNN)’s to create Deep Learning systems capable of translating Sign Language into text has been a breakthrough in breaking the communication barrier for deaf-mute people. Conventional research on this subject has been concerned with training the network to recognize the fingerspelling gestures of a given language and produce their corresponding alphanumerics. One of the problems with the current developing technology is that images are scarce, with little variations in the gestures being presented to the recognition program, often skewed towards single skin tones and hand sizes that makes a percentage of the population’s fingerspelling harder to detect. Along with this, current gesture detection programs are only trained on one finger spelling language despite there being one hundred and forty-two known variants so far. All of this presents a limitation for traditional exploitation for the state of current technologies such as CNN’s, due to their large number of required parameters. This work aims to present a technology that aims to resolve this issue by combining a pretrained legacy AI system for a generic object recognition task with a corrector method to uptrain the legacy network. This is a computationally efficient procedure that does not require large volumes of data even when covering a broad range of sign languages such as American Sign Language, British Sign Language and Chinese Sign Language (Pinyin). Implementing recent results on method concentration, namely the stochastic separation theorem, an AI system is supposed as an operate mapping an input present in the set of images u ∈ U to an output that exists in a set of predicted class labels q ∈ Q of the alphanumeric that q represents and the language it comes from. These inputs and outputs, along with the interval variables z ∈ Z represent the system’s current state which implies a mapping that assigns an element x ∈ ℝⁿ to the triple (u, z, q). As all xi are i.i.d vectors drawn from a product mean distribution, over a period of time the AI generates a large set of measurements xi called S that are grouped into two categories: the correct predictions M and the incorrect predictions Y. Once the network has made its predictions, a corrector can then be applied through centering S and Y by subtracting their means. The data is then regularized by applying the Kaiser rule to the resulting eigenmatrix and then whitened before being split into pairwise, positively correlated clusters. Each of these clusters produces a unique hyperplane and if any element x falls outside the region bounded by these lines then it is reported as an error. As a result of this methodology, a self-correcting recognition process is created that can identify fingerspelling from a variety of sign language and successfully identify the corresponding alphanumeric and what language the gesture originates from which no other neural network has been able to replicate.

Keywords: convolutional neural networks, deep learning, shallow correctors, sign language

Procedia PDF Downloads 100

Authors: Soheila Sadeghi

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In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

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Authors: Yildiz Stella Dak, Jale Tezcan

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Ground motion models that relate a strong motion parameter of interest to a set of predictive seismological variables describing the earthquake source, the propagation path of the seismic wave, and the local site conditions constitute a critical component of seismic hazard analyses. When a sufficient number of strong motion records are available, ground motion relations are developed using statistical analysis of the recorded ground motion data. In regions lacking a sufficient number of recordings, a synthetic database is developed using stochastic, theoretical or hybrid approaches. Regardless of the manner the database was developed, ground motion relations are developed using regression analysis. Development of a ground motion relation is a challenging process which inevitably requires the modeler to make subjective decisions regarding the inclusion criteria of the recordings, the functional form of the model and the set of seismological variables to be included in the model. Because these decisions are critically important to the validity and the applicability of the model, there is a continuous interest on procedures that will facilitate the development of ground motion models. This paper proposes the use of the Least Absolute Shrinkage and Selection Operator (LASSO) in selecting the set predictive seismological variables to be used in developing a ground motion relation. The LASSO can be described as a penalized regression technique with a built-in capability of variable selection. Similar to the ridge regression, the LASSO is based on the idea of shrinking the regression coefficients to reduce the variance of the model. Unlike ridge regression, where the coefficients are shrunk but never set equal to zero, the LASSO sets some of the coefficients exactly to zero, effectively performing variable selection. Given a set of candidate input variables and the output variable of interest, LASSO allows ranking the input variables in terms of their relative importance, thereby facilitating the selection of the set of variables to be included in the model. Because the risk of overfitting increases as the ratio of the number of predictors to the number of recordings increases, selection of a compact set of variables is important in cases where a small number of recordings are available. In addition, identification of a small set of variables can improve the interpretability of the resulting model, especially when there is a large number of candidate predictors. A practical application of the proposed approach is presented, using more than 600 recordings from the National Geospatial-Intelligence Agency (NGA) database, where the effect of a set of seismological predictors on the 5% damped maximum direction spectral acceleration is investigated. The set of candidate predictors considered are Magnitude, Rrup, Vs30. Using LASSO, the relative importance of the candidate predictors has been ranked. Regression models with increasing levels of complexity were constructed using one, two, three, and four best predictors, and the models’ ability to explain the observed variance in the target variable have been compared. The bias-variance trade-off in the context of model selection is discussed.

Keywords: ground motion modeling, least absolute shrinkage and selection operator, penalized regression, variable selection

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Authors: S. Mehrab Amiri, Nasser Talebbeydokhti

Abstract:

Modeling sediment transport processes by means of numerical approach often poses severe challenges. In this way, a number of techniques have been suggested to solve flow and sediment equations in decoupled, semi-coupled or fully coupled forms. Furthermore, in order to capture flow discontinuities, a number of techniques, like artificial viscosity and shock fitting, have been proposed for solving these equations which are mostly required careful calibration processes. In this research, a numerical scheme for solving shallow water and Exner equations in fully coupled form is presented. First-Order Centered scheme is applied for producing required numerical fluxes and the reconstruction process is carried out toward using Monotonic Upstream Scheme for Conservation Laws to achieve a high order scheme.  In order to satisfy C-property of the scheme in presence of bed topography, Surface Gradient Method is proposed. Combining the presented scheme with fourth order Runge-Kutta algorithm for time integration yields a competent numerical scheme. In addition, to handle non-prismatic channels problems, Cartesian Cut Cell Method is employed. A trained Multi-Layer Perceptron Artificial Neural Network which is of Feed Forward Back Propagation (FFBP) type estimates sediment flow discharge in the model rather than usual empirical formulas. Hydrodynamic part of the model is tested for showing its capability in simulation of flow discontinuities, transcritical flows, wetting/drying conditions and non-prismatic channel flows. In this end, dam-break flow onto a locally non-prismatic converging-diverging channel with initially dry bed conditions is modeled. The morphodynamic part of the model is verified simulating dam break on a dry movable bed and bed level variations in an alluvial junction. The results show that the model is capable in capturing the flow discontinuities, solving wetting/drying problems even in non-prismatic channels and presenting proper results for movable bed situations. It can also be deducted that applying Artificial Neural Network, instead of common empirical formulas for estimating sediment flow discharge, leads to more accurate results.

Keywords: artificial neural network, morphodynamic model, sediment continuity equation, shallow water equations

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Authors: Haseen Siddiqui, Sanjay M. Mahajani

Abstract:

Various important reactions in chemical and metallurgical industries fall in the category of gas-solid reactions. These reactions can be categorized as catalytic and non-catalytic gas-solid reactions. In gas-solid reaction systems, heat and mass transfer limitations put an appreciable influence on the rate of the reaction. The consequences can be unavoidable for overlooking such effects while collecting the reaction rate data for the design of the reactor. Pyrolysis reaction comes in this category that involves the production of gases due to the interaction of heat and solid substance. Pyrolysis is also an important step in the gasification process and therefore, the gasification reactivity majorly influenced by the pyrolysis process that produces the char, as a feed for the gasification process. Therefore, in the present study, a non-isothermal transient 1-D model is developed for a single biomass pellet to investigate the effect of heat and mass transfer limitations on the rate of pyrolysis reaction. The obtained set of partial differential equations are firstly discretized using the concept of ‘method of lines’ to obtain a set of ordinary differential equation with respect to time. These equations are solved, then, using MATLAB ode solver ode15s. The model is capable of incorporating structural changes, porosity variation, variation in various thermal properties and various pellet shapes. The model is used to analyze the effectiveness factor for different values of Lewis number and heat of reaction (G factor). Lewis number includes the effect of thermal conductivity of the solid pellet. Higher the Lewis number, the higher will be the thermal conductivity of the solid. The effectiveness factor was found to be decreasing with decreasing Lewis number due to the fact that smaller Lewis numbers retard the rate of heat transfer inside the pellet owing to a lower rate of pyrolysis reaction. G factor includes the effect of the heat of reaction. Since the pyrolysis reaction is endothermic in nature, the G factor takes negative values. The more the negative value higher will be endothermic nature of the pyrolysis reaction. The effectiveness factor was found to be decreasing with more negative values of the G factor. This behavior can be attributed to the fact that more negative value of G factor would result in more energy consumption by the reaction owing to a larger temperature gradient inside the pellet. Further, the analytical expressions are also derived for gas and solid concentrations and effectiveness factor for two limiting cases of the general model developed. The two limiting cases of the model are categorized as the homogeneous model and unreacted shrinking core model.

Keywords: effectiveness factor, G-factor, homogeneous model, lewis number, non-catalytic, shrinking core model

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Authors: M. Cardenas-Garcia, P. P. Gonzalez-Perez

Abstract:

Intercellular communication is a necessary condition for cellular functions and it allows a group of cells to survive as a population. Throughout this interaction, the cells work in a coordinated and collaborative way which facilitates their survival. In the case of cancerous cells, these take advantage of intercellular communication to preserve their malignancy, since through these physical unions they can send signs of malignancy. The Wnt/β-catenin signaling pathway plays an important role in the formation of intercellular communications, being also involved in a large number of cellular processes such as proliferation, differentiation, adhesion, cell survival, and cell death. The modeling and simulation of cellular signaling systems have found valuable support in a wide range of modeling approaches, which cover a wide spectrum ranging from mathematical models; e.g., ordinary differential equations, statistical methods, and numerical methods– to computational models; e.g., process algebra for modeling behavior and variation in molecular systems. Based on these models, different simulation tools have been developed from mathematical ones to computational ones. Regarding cellular and molecular processes in cancer, its study has also found a valuable support in different simulation tools that, covering a spectrum as mentioned above, have allowed the in silico experimentation of this phenomenon at the cellular and molecular level. In this work, we simulate and explore the complex interaction patterns of intercellular communication in cancer cells using the Cellulat bioinformatics tool, a computational simulation tool developed by us and motivated by two key elements: 1) a biochemically inspired model of self-organizing coordination in tuple spaces, and 2) the Gillespie’s algorithm, a stochastic simulation algorithm typically used to mimic systems of chemical/biochemical reactions in an efficient and accurate way. The main idea behind the Cellulat simulation tool is to provide an in silico experimentation environment that complements and guides in vitro experimentation in intra and intercellular signaling networks. Unlike most of the cell signaling simulation tools, such as E-Cell, BetaWB and Cell Illustrator which provides abstractions to model only intracellular behavior, Cellulat is appropriate for modeling both intracellular signaling and intercellular communication, providing the abstractions required to model –and as a result, simulate– the interaction mechanisms that involve two or more cells, that is essential in the scenario discussed in this work. During the development of this work we made evident the application of our computational simulation tool (Cellulat) for the modeling and simulation of intercellular communication between normal and cancerous cells, and in this way, propose key molecules that may prevent the arrival of malignant signals to the cells that surround the tumor cells. In this manner, we could identify the significant role that has the Wnt/β-catenin signaling pathway in cellular communication, and therefore, in the dissemination of cancer cells. We verified, using in silico experiments, how the inhibition of this signaling pathway prevents that the cells that surround a cancerous cell are transformed.

Keywords: cancer cells, in silico approach, intercellular communication, key molecules, modeling and simulation

Procedia PDF Downloads 249