Search results for: seismic prediction equations

Authors: Gabriel Malgaresi, Sara Borazjani, Hadi Madani, Pavel Bedrikovetsky

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Suspension-Colloidal flow in porous media occurs in numerous engineering fields, such as industrial water treatment, the disposal of industrial wastes into aquifers with the propagation of contaminants and low salinity water injection into petroleum reservoirs. The main effects are particle mobilization and captured by the porous rock, which can cause pore plugging and permeability reduction which is known as formation damage. Various factors such as fluid salinity, pH, temperature, and rock properties affect particle detachment. Formation damage is unfavorable specifically near injection and production wells. One way to control formation damage is pre-treatment of the rock with nanoparticles. Adsorption of nanoparticles on fines and rock surfaces alters zeta-potential of the surfaces and enhances the attachment force between the rock and fine particles. The main objective of this study is to develop a two-stage mathematical model for (1) flow and adsorption of nanoparticles on the rock in the pre-treatment stage and (2) fines migration and permeability reduction during the water production after the pre-treatment. The model accounts for adsorption and desorption of nanoparticles, fines migration, and kinetics of particle capture. The system of equations allows for the exact solution. The non-self-similar wave-interaction problem was solved by the Method of Characteristics. The analytical model is new in two ways: First, it accounts for the specific boundary and initial condition describing the injection of nanoparticle and production from the pre-treated porous media; second, it contains the effect of nanoparticle sorption hysteresis. The derived analytical model contains explicit formulae for the concentration fronts along with pressure drop. The solution is used to determine the optimal injection concentration of nanoparticle to avoid formation damage. The mathematical model was validated via an innovative laboratory program. The laboratory study includes two sets of core-flood experiments: (1) production of water without nanoparticle pre-treatment; (2) pre-treatment of a similar core with nanoparticles followed by water production. Positively-charged Alumina nanoparticles with the average particle size of 100 nm were used for the rock pre-treatment. The core was saturated with the nanoparticles and then flushed with low salinity water; pressure drop across the core and the outlet fine concentration was monitored and used for model validation. The results of the analytical modeling showed a significant reduction in the fine outlet concentration and formation damage. This observation was in great agreement with the results of core-flood data. The exact solution accurately describes fines particle breakthroughs and evaluates the positive effect of nanoparticles in formation damage. We show that the adsorbed concentration of nanoparticle highly affects the permeability of the porous media. For the laboratory case presented, the reduction of permeability after 1 PVI production in the pre-treated scenario is 50% lower than the reference case. The main outcome of this study is to provide a validated mathematical model to evaluate the effect of nanoparticles on formation damage.

Keywords: nano-particles, formation damage, permeability, fines migration

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Authors: S. Sabatino, V. Calderaro, V. Galdi, G. Graber, L. Ippolito

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Climate change is a rapidly growing global threat caused mainly by increased emissions of carbon dioxide (CO₂) into the atmosphere. These emissions come from multiple sources, including industry, power generation, and the transport sector. The need to tackle climate change and reduce CO₂ emissions is indisputable. A crucial solution to achieving decarbonization in the transport sector is the adoption of electric vehicles (EVs). These vehicles use lithium (Li-Ion) batteries as an energy source, making them extremely efficient and with low direct emissions. However, Li-Ion batteries are not without problems, including the risk of overheating and performance degradation. To ensure its safety and longevity, it is essential to use a battery management system (BMS). The BMS constantly monitors battery status, adjusts temperature and cell balance, ensuring optimal performance and preventing dangerous situations. From the monitoring carried out, it is also able to optimally manage the battery to increase its life. Among the parameters monitored by the BMS, the main ones are State of Charge (SoC), State of Health (SoH), and State of Power (SoP). The evaluation of these parameters can be carried out in two ways: offline, using benchtop batteries tested in the laboratory, or online, using batteries installed in moving vehicles. Online estimation is the preferred approach, as it relies on capturing real-time data from batteries while operating in real-life situations, such as in everyday EV use. Actual battery usage conditions are highly variable. Moving vehicles are exposed to a wide range of factors, including temperature variations, different driving styles, and complex charge/discharge cycles. This variability is difficult to replicate in a controlled laboratory environment and can greatly affect performance and battery life. Online estimation captures this variety of conditions, providing a more accurate assessment of battery behavior in real-world situations. In this article, a hybrid approach based on a neural network and a statistical method for real-time estimation of SoC, SoH, and SoP parameters of interest is proposed. These parameters are estimated from the analysis of a one-day driving profile of an electric vehicle, assumed to be divided into the following four phases: (i) Partial discharge (SoC 100% - SoC 50%), (ii) Partial discharge (SoC 50% - SoC 80%), (iii) Deep Discharge (SoC 80% - SoC 30%) (iv) Full charge (SoC 30% - SoC 100%). The neural network predicts the values of ohmic resistance and incremental capacity, while the statistical method is used to estimate the parameters of interest. This reduces the complexity of the model and improves its prediction accuracy. The effectiveness of the proposed model is evaluated by analyzing its performance in terms of square mean error (RMSE) and percentage error (MAPE) and comparing it with the reference method found in the literature.

Keywords: electric vehicle, Li-Ion battery, BMS, state-of-charge, state-of-health, state-of-power, artificial neural networks

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Authors: Gerd Wilsch, Tobias Guenther, Tobias Voelker

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In view of the ageing of vital infrastructure facilities, a reliable condition assessment of concrete structures is becoming of increasing interest for asset owners to plan timely and appropriate maintenance and repair interventions. For concrete structures, reinforcement corrosion induced by penetrating chlorides is the dominant deterioration mechanism affecting the serviceability and, eventually, structural performance. The determination of the quantitative chloride ingress is required not only to provide valuable information on the present condition of a structure, but the data obtained can also be used for the prediction of its future development and associated risks. At present, wet chemical analysis of ground concrete samples by a laboratory is the most common test procedure for the determination of the chloride content. As the chloride content is expressed by the mass of the binder, the analysis should involve determination of both the amount of binder and the amount of chloride contained in a concrete sample. This procedure is laborious, time-consuming, and costly. The chloride profile obtained is based on depth intervals of 10 mm. LIBS is an economically viable alternative providing chloride contents at depth intervals of 1 mm or less. It provides two-dimensional maps of quantitative element distributions and can locate spots of higher concentrations like in a crack. The results are correlated directly to the mass of the binder, and it can be applied on-site to deliver instantaneous results for the evaluation of the structure. Examples for the application of the method in the laboratory for the investigation of diffusion and migration of chlorides, sulfates, and alkalis are presented. An example for the visualization of the Li transport in concrete is also shown. These examples show the potential of the method for a fast, reliable, and automated two-dimensional investigation of transport processes. Due to the better spatial resolution, more accurate input parameters for model calculations are determined. By the simultaneous detection of elements such as carbon, chlorine, sodium, and potassium, the mutual influence of the different processes can be determined in only one measurement. Furthermore, the application of a mobile LIBS system in a parking garage is demonstrated. It uses a diode-pumped low energy laser (3 mJ, 1.5 ns, 100 Hz) and a compact NIR spectrometer. A portable scanner allows a two-dimensional quantitative element mapping. Results show the quantitative chloride analysis on wall and floor surfaces. To determine the 2-D distribution of harmful elements (Cl, C), concrete cores were drilled, split, and analyzed directly on-site. Results obtained were compared and verified with laboratory measurements. The results presented show that the LIBS method is a valuable addition to the standard procedures - the wet chemical analysis of ground concrete samples. Currently, work is underway to develop a technical code of practice for the application of the method for the determination of chloride concentration in concrete.

Keywords: chemical analysis, concrete, LIBS, spectroscopy

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Authors: Casta Guillaume, Robert Jagers, Deborah Rivas-Drake

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Civically engaged youth are critical to maintaining and/or improving the functioning of local, national and global communities and their institutions. The present study investigated how school climate and academic beliefs (academic self-efficacy and school belonging) may inform emergent civic behaviors (emergent citizenship) among self-identified middle school youth of color (African American, Multiracial or Mixed, Latino, Asian American or Pacific Islander, Native American, and other). Study aims: 1) Understand whether and how school climate is associated with civic engagement behaviors, directly and indirectly, by fostering a positive sense of connection to the school and/or engendering feelings of self-efficacy in the academic domain. Accordingly, we examined 2) The association of youths’ sense of school connection and academic self-efficacy with their personally responsible and participatory civic behaviors in school and community contexts—both concurrently and longitudinally. Data from two subsamples of a larger study of social/emotional development among middle school students were used for longitudinal and cross sectional analysis. The cross-sectional sample included 324 6th-8th grade students, of which 43% identified as African American, 20% identified as Multiracial or Mixed, 18% identified as Latino, 12% identified as Asian American or Pacific Islander, 6% identified as Other, and 1% identified as Native American. The age of the sample ranged from 11 – 15 (M = 12.33, SD = .97). For the longitudinal test of our mediation model, we drew on data from the 6th and 7th grade cohorts only (n =232); the ethnic and racial diversity of this longitudinal subsample was virtually identical to that of the cross-sectional sample. For both the cross-sectional and longitudinal analyses, full information maximum likelihood was used to deal with missing data. Fit indices were inspected to determine if they met the recommended thresholds of RMSEA below .05 and CFI and TLI values of at least .90. To determine if particular mediation pathways were significant, the bias-corrected bootstrap confidence intervals for each indirect pathway were inspected. Fit indices for the latent variable mediation model using the cross-sectional data suggest that the hypothesized model fit the observed data well (CFI = .93; TLI =. 92; RMSEA = .05, 90% CI = [.04, .06]). In the model, students’ perceptions of school climate were significantly and positively associated with greater feelings of school connectedness, which were in turn significantly and positively associated with civic engagement. In addition, school climate was significantly and positively associated with greater academic self-efficacy, but academic self-efficacy was not significantly associated with civic engagement. Tests of mediation indicated there was one significant indirect pathway between school climate and civic engagement behavior. There was an indirect association between school climate and civic engagement via its association with sense of school connectedness, indirect association estimate = .17 [95% CI: .08, .32]. The aforementioned indirect association via school connectedness accounted for 50% (.17/.34) of the total effect. Partial support was found for the prediction that students’ perceptions of a positive school climate are linked to civic engagement in part through their role in students’ sense of connection to school.

Keywords: civic engagement, early adolescence, school climate, school belonging, developmental niche

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Authors: Blaga Alexandra Cristina, Kloetzer Lenuta, Bompa Amalia Stela, Galaction Anca Irina, Cascaval Dan

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Vitamin B9 is an important member of vitamins B group, being a growth factor, important for making genetic material as DNA and RNA, red blood cells, for building muscle tissues, especially during periods of infancy, adolescence and pregnancy. Its production by biosynthesis is based on the high metabolic potential of mutant Bacillus subtilis, due to a superior biodisponibility compared to that obtained by chemical pathways. Pertraction, defined as the extraction and transport through liquid membranes consists in the transfer of a solute between two aqueous phases of different pH-values, phases that are separated by a solvent layer of various sizes. The pertraction efficiency and selectivity could be significantly enhanced by adding a carrier in the liquid membrane, such as organophosphoric compounds, long chain amines or crown-ethers etc., the separation process being called facilitated pertraction. The aim of the work is to determine the impact of the presence of two extractants/carriers in the bulk liquid membrane, i.e. di(2-ethylhexyl) phosphoric acid (D2EHPA) and lauryltrialkylmetilamine (Amberlite LA2) on the transport kinetics of vitamin B9. The experiments have been carried out using two pertraction equipments for a free liquid membrane or bulk liquid membrane. One pertraction cell consists on a U-shaped glass pipe (used for the dichloromethane membrane) and the second one is an H-shaped glass pipe (used for h-heptane), having 45 mm inner diameter of the total volume of 450 mL, the volume of each compartment being of 150 mL. The aqueous solutions are independently mixed by means of double blade stirrers with 6 mm diameter and 3 mm height, having the rotation speed of 500 rpm. In order to reach high diffusional rates through the solvent layer, the organic phase has been mixed with a similar stirrer, at a similar rotation speed (500 rpm). The area of mass transfer surface, both for extraction and for reextraction, was of 1.59x10-³ m2. The study on facilitated pertraction with the mixture of two carriers, namely D2EHPA and Amberlite LA-2, dissolved in two solvents with different polarities: n-heptane and dichloromethane, indicated the possibility to obtain the synergic effect. The synergism has been analyzed by considering the vitamin initial and final mass flows, as well as the permeability factors through liquid membrane. The synergic effect has been observed at low D2EHPA concentrations and high Amberlite LA-2 concentrations, being more important for the low-polar solvent (n-heptane). The results suggest that the mechanism of synergic pertraction consists on the reaction between the organophosphoric carrier and vitamin B9 at the interface between the feed and membrane phases, while the aminic carrier enhances the hydrophobicity of this compound by solvation. However, the formation of this complex reduced the reextraction rate and, consequently, affects the synergism related to the final mass flows and permeability factor. For describing the influences of carriers concentrations on the synergistic coefficients, some equations have been proposed by taking into account the vitamin mass flows or permeability factors, with an average deviations between 4.85% and 10.73%.

Keywords: pertraction, synergism, vitamin B9, Amberlite LA-2, di(2-ethylhexyl) phosphoric acid

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Authors: Maryam Ziaee

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This research proposes a new approach and provides an opportunity for manufacturing companies to produce large amounts of products that meet their prospective customers’ tastes, needs, and expectations and simultaneously enable manufacturers to increase their profit. Mass customization is the production of products or services to meet each individual customer’s desires to the greatest possible extent in high quantities and at reasonable prices. This process takes place at different levels such as the customization of goods’ design, assembly, sale, and delivery status, and classifies in several categories. The main focus of this study is on one class of mass customization, called optional customization, in which companies try to provide their customers with as many options as possible to customize their products. These options could range from the design phase to the manufacturing phase, or even methods of delivery. Mass customization values customers’ tastes, but it is only one side of clients’ satisfaction; on the other side is companies’ fast responsiveness delivery. It brings the concept of agility, which is the ability of a company to respond rapidly to changes in volatile markets in terms of volume and variety. Indeed, mass customization is not effectively feasible without integrating the concept of agility. To gain the customers’ satisfaction, the companies need to be quick in responding to their customers’ demands, thus highlighting the significance of agility. This research offers a different method that successfully integrates mass customization and fast production in manufacturing industries. This research is built upon the hypothesis that the success key to being agile in mass customization is to forecast demand, cooperate with suppliers, and control inventory. Therefore, the significance of the supply chain (SC) is more pertinent when it comes to this stage. Since SC behavior is dynamic and its behavior changes constantly, companies have to apply one of the predicting techniques to identify the changes associated with SC behavior to be able to respond properly to any unwelcome events. System dynamics utilized in this research is a simulation approach to provide a mathematical model among different variables to understand, control, and forecast SC behavior. The final stage is delayed differentiation, the production strategy considered in this research. In this approach, the main platform of products is produced and stocked and when the company receives an order from a customer, a specific customized feature is assigned to this platform and the customized products will be created. The main research question is to what extent applying system dynamics for the prediction of SC behavior improves the agility of mass customization. This research is built upon a qualitative approach to bring about richer, deeper, and more revealing results. The data is collected through interviews and is analyzed through NVivo software. This proposed model offers numerous benefits such as reduction in the number of product inventories and their storage costs, improvement in the resilience of companies’ responses to their clients’ needs and tastes, the increase of profits, and the optimization of productivity with the minimum level of lost sales.

Keywords: agility, manufacturing, resilience, supply chain

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Authors: Valentina Dolmova

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What consumers choose to buy and who voters select on election days are two questions that have captivated the interest of both academics and practitioners for many decades. The importance of understanding what influences the behavior of those groups and whether or not we can predict or control it fuels a steady stream of research in a range of fields. By looking only at the past 40 years, more than 70 thousand scientific papers have been published in each field – consumer behavior and political psychology, respectively. From marketing, economics, and the science of persuasion to political and cognitive psychology - we have all remained heavily engaged. The ever-evolving technology, inevitable socio-cultural shifts, global economic conditions, and much more play an important role in choice-equations regardless of context. On one hand, this makes the research efforts always relevant and needed. On the other, the relatively low number of cross-field collaborations, which seem to be picking up only in more in recent years, makes the existing findings isolated into framed bubbles. By performing systematic research across both areas of psychology and building a parallel between theories and factors of influence, however, we find that there is not only a definitive common ground between the behaviors of consumers and voters but that we are moving towards a global model of choice. This means that the lines between contexts are fading which has a direct implication on what we should focus on when predicting or navigating buyers and voters’ behavior. Internal and external factors in four main categories determine the choices we make as consumers and as voters. Together, personal, psychological, social, and cultural create a holistic framework through which all stimuli in relation to a particular product or a political party get filtered. The analogy “consumer-voter” solidifies further. Leading academics suggest that this fundamental parallel is the key to managing successfully political and consumer brands alike. However, we distinguish additional four key stimuli that relate to those factor categories (1/ opportunity costs; 2/the memory of the past; 3/recognisable figures/faces and 4/conflict) arguing that the level of expertise a person has determines the prevalence of factors or specific stimuli. Our efforts take into account global trends such as the establishment of “celebrity politics” and the image of “ethically concerned consumer brands” which bridge the gap between contexts to an even greater extent. Scientists and practitioners are pushed to accept the transformative nature of both fields in social psychology. Existing blind spots as well as the limited number of research conducted outside the American and European societies open up space for more collaborative efforts in this highly demanding and lucrative field. A mixed method of research tests three main hypotheses, the first two of which are focused on the level of irrelevance of context when comparing voting or consumer behavior – both from the factors and stimuli lenses, the third on determining whether or not the level of expertise in any field skews the weight of what prism we are more likely to choose when evaluating options.

Keywords: buyers’ behaviour, decision-making, voters’ behaviour, social psychology

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Authors: Quan Wen, Tianxiao Chang, Shaolu Shi, Yushi Wang, Guangyu Wang

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As a kind of working environment of the fuze, the spin rate decaying law of projectile in exterior trajectory is of great value in the design of the rotation count fixed distance fuze. In addition, it is significant in the field of devices for simulation tests of fuze exterior ballistic environment, flight stability, and dispersion accuracy of gun projectile and opening and scattering design of submunition and illuminating cartridges. Besides, the self-destroying mechanism of the fuze in small-caliber projectile often works by utilizing the attenuation of centrifugal force. In the theory of projectile aerodynamics and fuze design, there are many formulas describing the change law of projectile angular velocity in external ballistic such as Roggla formula, exponential function formula, and power function formula. However, these formulas are mostly semi-empirical due to the poor test conditions and insufficient test data at that time. These formulas are difficult to meet the design requirements of modern fuze because they are not accurate enough and have a narrow range of applications now. In order to provide more accurate ballistic environment parameters for the design of a hemispherical head projectile fuze, the projectile’s spin rate decaying law in exterior trajectory under the effect of air resistance was studied. In the analysis, the projectile shape was simplified as hemisphere head, cylindrical part, rotating band part, and anti-truncated conical tail. The main assumptions are as follows: a) The shape and mass are symmetrical about the longitudinal axis, b) There is a smooth transition between the ball hea, c) The air flow on the outer surface is set as a flat plate flow with the same area as the expanded outer surface of the projectile, and the boundary layer is turbulent, d) The polar damping moment attributed to the wrench hole and rifling mark on the projectile is not considered, e) The groove of the rifle on the rotating band is uniform, smooth and regular. The impacts of the four parts on aerodynamic moment of the projectile rotation were obtained by aerodynamic theory. The surface friction stress of the projectile, the polar damping moment formed by the head of the projectile, the surface friction moment formed by the cylindrical part, the rotating band, and the anti-truncated conical tail were obtained by mathematical derivation. After that, the mathematical model of angular spin rate attenuation was established. In the whole trajectory with the maximum range angle (38°), the absolute error of the polar damping torque coefficient obtained by simulation and the coefficient calculated by the mathematical model established in this paper is not more than 7%. Therefore, the credibility of the mathematical model was verified. The mathematical model can be described as a first-order nonlinear differential equation, which has no analytical solution. The solution can be only gained as a numerical solution by connecting the model with projectile mass motion equations in exterior ballistics.

Keywords: ammunition engineering, fuze technology, spin rate, numerical simulation

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Authors: Monika Saha, A. T. M. Hasan Zobeyer, Nasreen Jahan

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El Nino-Southern Oscillation (ENSO) is the interaction between atmosphere and ocean in tropical Pacific which causes inconsistent warm/cold weather in tropical central and eastern Pacific Ocean. Due to the impact of climate change, ENSO events are becoming stronger in recent times, and therefore it is very important to study the influence of ENSO in climate studies. Bangladesh, being in the low-lying deltaic floodplain, experiences the worst consequences due to flooding every year. To reduce the catastrophe of severe flooding events, non-structural measures such as flood forecasting can be helpful in taking adequate precautions and steps. Forecasting seasonal flood with a longer lead time of several months is a key component of flood damage control and water management. The objective of this research is to identify the possible strength of teleconnection between ENSO and river flow of Surma and examine the potential possibility of long lead flood forecasting in the wet season. Surma is one of the major rivers of Bangladesh and is a part of the Surma-Meghna river system. In this research, sea surface temperature (SST) has been considered as the ENSO index and the lead time is at least a few months which is greater than the basin response time. The teleconnection has been assessed by the correlation analysis between July-August-September (JAS) flow of Surma and SST of Nino 4 region of the corresponding months. Cumulative frequency distribution of standardized JAS flow of Surma has also been determined as part of assessing the possible teleconnection. Discharge data of Surma river from 1975 to 2015 is used in this analysis, and remarkable increased value of correlation coefficient between flow and ENSO has been observed from 1985. From the cumulative frequency distribution of the standardized JAS flow, it has been marked that in any year the JAS flow has approximately 50% probability of exceeding the long-term average JAS flow. During El Nino year (warm episode of ENSO) this probability of exceedance drops to 23% and while in La Nina year (cold episode of ENSO) it increases to 78%. Discriminant analysis which is known as 'Categoric Prediction' has been performed to identify the possibilities of long lead flood forecasting. It has helped to categorize the flow data (high, average and low) based on the classification of predicted SST (warm, normal and cold). From the discriminant analysis, it has been found that for Surma river, the probability of a high flood in the cold period is 75% and the probability of a low flood in the warm period is 33%. A synoptic parameter, forecasting index (FI) has also been calculated here to judge the forecast skill and to compare different forecasts. This study will help the concerned authorities and the stakeholders to take long-term water resources decisions and formulate policies on river basin management which will reduce possible damage of life, agriculture, and property.

Keywords: El Nino-Southern Oscillation, sea surface temperature, surma river, teleconnection, cumulative frequency distribution, discriminant analysis, forecasting index

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Authors: Jyoti Sahu, Vinay A. Juvekar

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Electrolytes are often used to bring about salting-in and salting-out of organic molecules and polymers (e.g. polyethylene glycols/proteins) from the aqueous solutions. For quantification of these phenomena, a thermodynamic model which can accurately predict activity coefficient of electrolyte as a function of temperature is needed. The thermodynamics models available in the literature contain a large number of empirical parameters. These parameters are estimated using lower/upper critical solution temperature of the solution in the electrolyte/organic molecule at different temperatures. Since the number of parameters is large, inaccuracy can bethe creep in during their estimation, which can affect the reliability of prediction beyond the range in which these parameters are estimated. Cloud point of solution is related to its free energy through temperature and composition derivative. Hence, the Cloud point measurement can be used for accurate estimation of the temperature and composition dependence of parameters in the model for free energy. Hence, if we use a two pronged procedure in which we first use cloud point of solution to estimate some of the parameters of the thermodynamic model and determine the rest using osmotic coefficient data, we gain on two counts. First, since the parameters, estimated in each of the two steps, are fewer, we achieve higher accuracy of estimation. The second and more important gain is that the resulting model parameters are more sensitive to temperature. This is crucial when we wish to use the model outside temperatures window within which the parameter estimation is sought. The focus of the present work is to prove this proposition. We have used electrolyte (NaCl/Na2CO3)-water-organic molecule (Iso-propanol/ethanol) as the model system. The model of Robinson-Stokes-Glukauf is modified by incorporating the temperature dependent Flory-Huggins interaction parameters. The Helmholtz free energy expression contains, in addition to electrostatic and translational entropic contributions, three Flory-Huggins pairwise interaction contributions viz., and (w-water, p-polymer, s-salt). These parameters depend both on temperature and concentrations. The concentration dependence is expressed in the form of a quadratic expression involving the volume fractions of the interacting species. The temperature dependence is expressed in the form .To obtain the temperature-dependent interaction parameters for organic molecule-water and electrolyte-water systems, Critical solution temperature of electrolyte -water-organic molecules is measured using cloud point measuring apparatus The temperature and composition dependent interaction parameters for electrolyte-water-organic molecule are estimated through measurement of cloud point of solution. The model is used to estimate critical solution temperature (CST) of electrolyte water-organic molecules solution. We have experimentally determined the critical solution temperature of different compositions of electrolyte-water-organic molecule solution and compared the results with the estimates based on our model. The two sets of values show good agreement. On the other hand when only osmotic coefficients are used for estimation of the free energy model, CST predicted using the resulting model show poor agreement with the experiments. Thus, the importance of the CST data in the estimation of parameters of the thermodynamic model is confirmed through this work.

Keywords: concentrated electrolytes, Debye-Hückel theory, interaction parameters, Robinson-Stokes-Glueckauf model, Flory-Huggins model, critical solution temperature

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Authors: Sumeet Rai, Anuj Tyagi, B. T. Naveen Kumar, Shubhkaramjeet Kaur, Niraj K. Singh

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Since their discovery, indiscriminate use of antibiotics in human, veterinary and aquaculture systems has resulted in global emergence/spread of multidrug-resistant bacterial pathogens. Thus, the need for alternative approaches to control bacterial infections has become utmost important. High selectivity/specificity of bacteriophages (phages) permits the targeting of specific bacteria without affecting the desirable flora. In this study, a lytic phage (Ahp1) specific to Aeromonas hydrophila subsp. hydrophila was isolated from finfish aquaculture pond. The host range of Ahp1 range was tested against 10 isolates of A. hydrophila, 7 isolates of A. veronii, 25 Vibrio cholerae isolates, 4 V. parahaemolyticus isolates and one isolate each of V. harveyi and Salmonella enterica collected previously. Except the host A. hydrophila subsp. hydrophila strain, no lytic activity against any other bacterial was detected. During the adsorption rate and one-step growth curve analysis, 69.7% of phage particles were able to get adsorbed on host cell followed by the release of 93 ± 6 phage progenies per host cell after a latent period of ~30 min. Phage nucleic acid was extracted by column purification methods. After determining the nature of phage nucleic acid as dsDNA, phage genome was subjected to next-generation sequencing by generating paired-end (PE, 2 x 300bp) reads on Illumina MiSeq system. De novo assembly of sequencing reads generated circular phage genome of 42,439 bp with G+C content of 58.95%. During open read frame (ORF) prediction and annotation, 22 ORFs (out of 49 total predicted ORFs) were functionally annotated and rest encoded for hypothetical proteins. Proteins involved in major functions such as phage structure formation and packaging, DNA replication and repair, DNA transcription and host cell lysis were encoded by the phage genome. The complete genome sequence of Ahp1 along with gene annotation was submitted to NCBI GenBank (accession number MF683623). Stability of Ahp1 preparations at storage temperatures of 4 °C, 30 °C, and 40 °C was studied over a period of 9 months. At 40 °C storage, phage counts declined by 4 log units within one month; with a total loss of viability after 2 months. At 30 °C temperature, phage preparation was stable for < 5 months. On the other hand, phage counts decreased by only 2 log units over a period of 9 during storage at 4 °C. As some of the phages have also been reported as glycerol sensitive, the stability of Ahp1 preparations in (0%, 15%, 30% and 45%) glycerol stocks were also studied during storage at -80 °C over a period of 9 months. The phage counts decreased only by 2 log units during storage, and no significant difference in phage counts was observed at different concentrations of glycerol. The Ahp1 phage discovered in our study had a very narrow host range and it may be useful for phage typing applications. Moreover, the endolysin and holin genes in Ahp1 genome could be ideal candidates for recombinant cloning and expression of antimicrobial proteins.

Keywords: Aeromonas hydrophila, endolysin, phage, narrow host range

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Authors: Madushani Rodrigo, Banuka Athuraliya

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In today's world of medical diagnosis and prediction, machine learning stands out as a strong tool, transforming old ways of caring for health. This study analyzes the use of machine learning in the specialized domain of sports medicine, with a focus on the timely and accurate detection of bone fractures in cricket athletes. Failure to identify bone fractures in real time can result in malunion or non-union conditions. To ensure proper treatment and enhance the bone healing process, accurately identifying fracture locations and types is necessary. When interpreting X-ray images, it relies on the expertise and experience of medical professionals in the identification process. Sometimes, radiographic images are of low quality, leading to potential issues. Therefore, it is necessary to have a proper approach to accurately localize and classify fractures in real time. The research has revealed that the optimal approach needs to address the stated problem and employ appropriate radiographic image processing techniques and object detection algorithms. These algorithms should effectively localize and accurately classify all types of fractures with high precision and in a timely manner. In order to overcome the challenges of misidentifying fractures, a distinct model for fracture localization and classification has been implemented. The research also incorporates radiographic image enhancement and preprocessing techniques to overcome the limitations posed by low-quality images. A classification ensemble model has been implemented using ResNet18 and VGG16. In parallel, a fracture segmentation model has been implemented using the enhanced U-Net architecture. Combining the results of these two implemented models, the FracXpert system can accurately localize exact fracture locations along with fracture types from the available 12 different types of fracture patterns, which include avulsion, comminuted, compressed, dislocation, greenstick, hairline, impacted, intraarticular, longitudinal, oblique, pathological, and spiral. This system will generate a confidence score level indicating the degree of confidence in the predicted result. Using ResNet18 and VGG16 architectures, the implemented fracture segmentation model, based on the U-Net architecture, achieved a high accuracy level of 99.94%, demonstrating its precision in identifying fracture locations. Simultaneously, the classification ensemble model achieved an accuracy of 81.0%, showcasing its ability to categorize various fracture patterns, which is instrumental in the fracture treatment process. In conclusion, FracXpert has become a promising ML application in sports medicine, demonstrating its potential to revolutionize fracture detection processes. By leveraging the power of ML algorithms, this study contributes to the advancement of diagnostic capabilities in cricket athlete healthcare, ensuring timely and accurate identification of bone fractures for the best treatment outcomes.

Keywords: multiclass classification, object detection, ResNet18, U-Net, VGG16

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Authors: Kotaro Sasai, Daijiro Mizutani, Kiyoyuki Kaito

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Expressways in Japan have been built in an accelerating manner since the 1960s with the aid of rapid economic growth. About 40 percent in length of expressways in Japan is now 30 years and older and has become superannuated. Time-related deterioration has therefore reached to a degree that administrators, from a standpoint of operation and maintenance, are forced to take prompt measures on a large scale aiming at repairing inner damage deep in pavements. These measures have already been performed for bridge management in Japan and are also expected to be embodied for pavement management. Thus, planning methods for the measures are increasingly demanded. Deterioration of layers around road surface such as surface course and binder course is brought about at the early stages of whole pavement deterioration process, around 10 to 30 years after construction. These layers have been repaired primarily because inner damage usually becomes significant after outer damage, and because surveys for measuring inner damage such as Falling Weight Deflectometer (FWD) survey and open-cut survey are costly and time-consuming process, which has made it difficult for administrators to focus on inner damage as much as they have been supposed to. As expressways today have serious time-related deterioration within them deriving from the long time span since they started to be used, it is obvious the idea of repairing layers deep in pavements such as base course and subgrade must be taken into consideration when planning maintenance on a large scale. This sort of maintenance requires precisely predicting degrees of deterioration as well as grasping the present situations of pavements. Methods for predicting deterioration are determined to be either mechanical or statistical. While few mechanical models have been presented, as far as the authors know of, previous studies have presented statistical methods for predicting deterioration in pavements. One describes deterioration process by estimating Markov deterioration hazard model, while another study illustrates it by estimating Proportional deterioration hazard model. Both of the studies analyze deflection data obtained from FWD surveys and present statistical methods for predicting deterioration process of layers around road surface. However, layers of base course and subgrade remain unanalyzed. In this study, data collected from FWD surveys are analyzed to predict deterioration process of layers deep in pavements in addition to surface layers by a means of estimating a deterioration hazard model using continuous indexes. This model can prevent the loss of information of data when setting rating categories in Markov deterioration hazard model when evaluating degrees of deterioration in roadbeds and subgrades. As a result of portraying continuous indexes, the model can predict deterioration in each layer of pavements and evaluate it quantitatively. Additionally, as the model can also depict probability distribution of the indexes at an arbitrary point and establish a risk control level arbitrarily, it is expected that this study will provide knowledge like life cycle cost and informative content during decision making process referring to where to do maintenance on as well as when.

Keywords: deterioration hazard model, falling weight deflectometer, inner damage, load bearing capacity, pavement

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Authors: Vanessa Do, Lauren Smith, Leslie Carver

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It is widely known that individuals with autism spectrum disorder (ASD) may exhibit sensitivity to stimuli. Even at a young age, they tend to display stimuli-related discomfort in their behavior during play. Play serves a crucial role in a child’s early years as it helps support healthy brain development, socio-emotional skills, and adaptation to their environment There is research dedicated to studying infant preferences for toys, especially in regard to: gender preferences, the advantages of promoting play, and the caregiver’s role in their child’s play routines. However, there is a disproportionate amount of literature examining how play patterns may differ in children with sensory sensitivity, such as children diagnosed with ASD. Prior literature has studied and found supporting evidence that individuals with ASD have deficits in social communication and have increased presence of repetitive behaviors and/or restricted interests, which also display in early childhood play patterns. This study aims to examine potential differences in toy preference between infants with (FH+) and without (FH-) a familial history of ASD ages 6. 9, and 12 months old. More specifically, this study will address the question, “do FH+ infants tend to play more with toys that require less social engagement compared to FH- infants?” Infants and their caregivers were recruited and asked to engage in a free-play session in their homes that lasted approximately 5 minutes. The sessions were recorded and later coded offline for engagement behaviors categorized by toy; each toy that the infants interacted with was coded as belonging to one of 6 categories: sensory (designed to stimulate one or more senses such as light-up toys or musical toys) , construction (e.g., building blocks, rubber suction cups), vehicles (e.g., toy cars), instructional (require steps to accomplish a goal such as flip phones or books), imaginative (e.g., dolls, stuffed animals), and miscellaneous (toys that do not fit into these categories). Toy engagement was defined as the infant looking and touching the toy (ILT) or looking at the toy while their caregiver was holding it (IL-CT). Results reported include/will include the proportion of time the infant was actively engaged with the toy out of the total usable video time per subject — distractions observed during the session were excluded from analysis. Data collection is still ongoing; however, the prediction is that FH+ infants will have higher engagement with sensory and construction toys as they require the least amount of social effort. Furthermore, FH+ infants will have the least engagement with the imaginative toys as prior literature has supported the claim that individuals with ASD have a decreased likelihood to engage in play that requires pretend play and other social skills. Looking at what toys are more or less engaging to FH+ infants is important as it provides significant contributions to their healthy cognitive, social, and emotional development. As play is one of the first ways for a child to understand the complexities of the larger world, the findings of this study may help guide further research into encouraging play with toys that are more engaging and sensory-sensitive for children with ASD.

Keywords: autism engagement, children’s play, early development, free-play, infants, toy

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Authors: Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Diego Santibañez Oyarce, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Streptococcus pyogenes is a gram-positive bacteria involved in a wide range of diseases and is a major-human-specific bacterial pathogen. In Chile, this year the 'Ministerio de Salud' declared an alert due to the increase in strains throughout the year. This increase can be attributed to the multitude of factors including antimicrobial resistance (AMR) and Virulence Factors (VF). Understanding these VF and AMR is crucial for developing effective strategies and improving public health responses. Moreover, experimental identification and characterization of these pathogenic mechanisms are labor-intensive and time-consuming. Therefore, new computational methods are required to provide robust techniques for accelerating this identification. Advances in Machine Learning (ML) algorithms represent the opportunity to refine and accelerate the discovery of VF associated with Streptococcus pyogenes. In this work, we evaluate the accuracy of various machine learning models in predicting the virulence factors and antimicrobial resistance of Streptococcus pyogenes, with the objective of providing new methods for identifying the pathogenic mechanisms of this organism.Our comprehensive approach involved the download of 32,798 genbank files of S. pyogenes from NCBI dataset, coupled with the incorporation of data from Virulence Factor Database (VFDB) and Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. These datasets provided labeled examples of both virulent and non-virulent genes, enabling a robust foundation for feature extraction and model training. We employed preprocessing, characterization and feature extraction techniques on primary nucleotide/amino acid sequences and selected the optimal more for model training. The feature set was constructed using sequence-based descriptors (e.g., k-mers and One-hot encoding), and functional annotations based on database prediction. The ML models compared are logistic regression, decision trees, support vector machines, neural networks among others. The results of this work show some differences in accuracy between the algorithms, these differences allow us to identify different aspects that represent unique opportunities for a more precise and efficient characterization and identification of VF and AMR. This comparative analysis underscores the value of integrating machine learning techniques in predicting S. pyogenes virulence and AMR, offering potential pathways for more effective diagnostic and therapeutic strategies. Future work will focus on incorporating additional omics data, such as transcriptomics, and exploring advanced deep learning models to further enhance predictive capabilities.

Keywords: antibiotic resistance, streptococcus pyogenes, virulence factors., machine learning

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Authors: Khachen Kongpakwattana, Nathorn Chaiyakunapruk

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Background: Decision-analytic models for Alzheimer’s disease (AD) have been advanced to discrete-event simulation (DES), in which individual-level modelling of disease progression across continuous severity spectra and incorporation of key parameters such as treatment persistence into the model become feasible. This study aimed to apply the DES to perform a cost-effectiveness analysis of treatment for AD in Thailand. Methods: A dataset of Thai patients with AD, representing unique demographic and clinical characteristics, was bootstrapped to generate a baseline cohort of patients. Each patient was cloned and assigned to donepezil, galantamine, rivastigmine, memantine or no treatment. Throughout the simulation period, the model randomly assigned each patient to discrete events including hospital visits, treatment discontinuation and death. Correlated changes in cognitive and behavioral status over time were developed using patient-level data. Treatment effects were obtained from the most recent network meta-analysis. Treatment persistence, mortality and predictive equations for functional status, costs (Thai baht (THB) in 2017) and quality-adjusted life year (QALY) were derived from country-specific real-world data. The time horizon was 10 years, with a discount rate of 3% per annum. Cost-effectiveness was evaluated based on the willingness-to-pay (WTP) threshold of 160,000 THB/QALY gained (4,994 US$/QALY gained) in Thailand. Results: Under a societal perspective, only was the prescription of donepezil to AD patients with all disease-severity levels found to be cost-effective. Compared to untreated patients, although the patients receiving donepezil incurred a discounted additional costs of 2,161 THB, they experienced a discounted gain in QALY of 0.021, resulting in an incremental cost-effectiveness ratio (ICER) of 138,524 THB/QALY (4,062 US$/QALY). Besides, providing early treatment with donepezil to mild AD patients further reduced the ICER to 61,652 THB/QALY (1,808 US$/QALY). However, the dominance of donepezil appeared to wane when delayed treatment was given to a subgroup of moderate and severe AD patients [ICER: 284,388 THB/QALY (8,340 US$/QALY)]. Introduction of a treatment stopping rule when the Mini-Mental State Exam (MMSE) score goes below 10 to a mild AD cohort did not deteriorate the cost-effectiveness of donepezil at the current treatment persistence level. On the other hand, none of the AD medications was cost-effective when being considered under a healthcare perspective. Conclusions: The DES greatly enhances real-world representativeness of decision-analytic models for AD. Under a societal perspective, treatment with donepezil improves patient’s quality of life and is considered cost-effective when used to treat AD patients with all disease-severity levels in Thailand. The optimal treatment benefits are observed when donepezil is prescribed since the early course of AD. With healthcare budget constraints in Thailand, the implementation of donepezil coverage may be most likely possible when being considered starting with mild AD patients, along with the stopping rule introduced.

Keywords: Alzheimer's disease, cost-effectiveness analysis, discrete event simulation, health technology assessment

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Authors: Meitham Amereh, Mohsen Akbari, Ben Nadler

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Cancer has been recognized as one of the most challenging problems in biology and medicine. Aggressive tumors are a lethal type of cancers characterized by high genomic instability, rapid progression, invasiveness, and therapeutic resistance. Their behavior involves complicated molecular biology and consequential dynamics. Although tremendous effort has been devoted to developing therapeutic approaches, there is still a huge need for new insights into the dark aspects of tumors. As one of the key requirements in better understanding the complex behavior of tumors, mathematical modeling and continuum physics, in particular, play a pivotal role. Mathematical modeling can provide a quantitative prediction on biological processes and help interpret complicated physiological interactions in tumors microenvironment. The pathophysiology of aggressive tumors is strongly affected by the extracellular cues such as stresses produced by mechanical forces between the tumor and the host tissue. During the tumor progression, the growing mass displaces the surrounding extracellular matrix (ECM), and due to the level of tissue stiffness, stress accumulates inside the tumor. The produced stress can influence the tumor by breaking adherent junctions. During this process, the tumor stops the rapid proliferation and begins to remodel its shape to preserve the homeostatic equilibrium state. To reach this, the tumor, in turn, upregulates epithelial to mesenchymal transit-inducing transcription factors (EMT-TFs). These EMT-TFs are involved in various signaling cascades, which are often associated with tumor invasiveness and malignancy. In this work, we modeled the tumor as a growing hyperplastic mass and investigated the effects of mechanical stress from surrounding ECM on tumor invasion. The invasion is modeled as volume-preserving inelastic evolution. In this framework, principal balance laws are considered for tumor mass, linear momentum, and diffusion of nutrients. Also, mechanical interactions between the tumor and ECM is modeled using Ciarlet constitutive strain energy function, and dissipation inequality is utilized to model the volumetric growth rate. System parameters, such as rate of nutrient uptake and cell proliferation, are obtained experimentally. To validate the model, human Glioblastoma multiforme (hGBM) tumor spheroids were incorporated inside Matrigel/Alginate composite hydrogel and was injected into a microfluidic chip to mimic the tumor’s natural microenvironment. The invasion structure was analyzed by imaging the spheroid over time. Also, the expression of transcriptional factors involved in invasion was measured by immune-staining the tumor. The volumetric growth, stress distribution, and inelastic evolution of tumors were predicted by the model. Results showed that the level of invasion is in direct correlation with the level of predicted stress within the tumor. Moreover, the invasion length measured by fluorescent imaging was shown to be related to the inelastic evolution of tumors obtained by the model.

Keywords: cancer, invasion, mathematical modeling, microfluidic chip, tumor spheroids

Procedia PDF Downloads 111

Authors: Jungyeol Hong, Dongjoo Park

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The urban transit system is a critical part of a solution to the economic, energy, and environmental challenges. Furthermore, it ultimately contributes the improvement of people’s quality of lives. For taking these kinds of advantages, the city of Seoul has tried to construct an integrated transit system including both subway and buses. The effort led to the fact that approximately 6.9 million citizens use the integrated transit system every day for their trips. Diagnosing the current transit network is a significant task to provide more convenient and pleasant transit environment. Therefore, the critical objective of this study is to establish a methodological framework for the analysis of an integrated bus-subway network and to examine the relationship between subway ridership and parameters such as network topology measures, bus demand, and a variety of commercial business facilities. Regarding a statistical approach to estimate subway ridership at a station level, many previous studies relied on Ordinary Least Square regression, but there was lack of studies considering the endogeneity issues which might show in the subway ridership prediction model. This study focused on both discovering the impacts of integrated transit network topology measures and endogenous effect of bus demand on subway ridership. It could ultimately contribute to developing more accurate subway ridership estimation accounting for its statistical bias. The spatial scope of the study covers Seoul city in South Korea, and it includes 243 subway stations and 10,120 bus stops with the temporal scope set during twenty-four hours with one-hour interval time panels each. The subway and bus ridership information in detail was collected from the Seoul Smart Card data in 2015 and 2016. First, integrated subway-bus network topology measures which have characteristics regarding connectivity, centrality, transitivity, and reciprocity were estimated based on the complex network theory. The results of integrated transit network topology analysis were compared to subway-only network topology. Also, the non-recursive approach which is Three-Stage Least Square was applied to develop the daily subway ridership model as capturing the endogeneity between bus and subway demands. Independent variables included roadway geometry, commercial business characteristics, social-economic characteristics, safety index, transit facility attributes, and dummies for seasons and time zone. Consequently, it was found that network topology measures were significant size effect. Especially, centrality measures showed that the elasticity was a change of 4.88% for closeness centrality, 24.48% for betweenness centrality while the elasticity of bus ridership was 8.85%. Moreover, it was proved that bus demand and subway ridership were endogenous in a non-recursive manner as showing that predicted bus ridership and predicted subway ridership is statistically significant in OLS regression models. Therefore, it shows that three-stage least square model appears to be a plausible model for efficient subway ridership estimation. It is expected that the proposed approach provides a reliable guideline that can be used as part of the spectrum of tools for evaluating a city-wide integrated transit network.

Keywords: integrated transit system, network topology measures, three-stage least squared, endogeneity, subway ridership

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Authors: Dila Ram Bhandari

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Throughout the 20th century, new governments were created where identities such as ethnic, religious, linguistic, caste, communal, tribal, and others played a part in the development of constitutions and the legal system of victim and criminal justice. Acute issues with extremism, poverty, environmental degradation, cybercrimes, human rights violations, crime against, and victimization of both individuals and groups have recently plagued South Asian nations. Everyday massive number of crimes are steadfast, these frequent crimes have made the lives of common citizens restless. Crimes are one of the major threats to society and also for civilization. Crime is a bone of contention that can create a societal disturbance. The old-style crime solving practices are unable to live up to the requirement of existing crime situations. Crime analysis is one of the most important activities of the majority of intelligent and law enforcement organizations all over the world. The South Asia region lacks such a regional coordination mechanism, unlike central Asia of Asia Pacific regions, to facilitate criminal intelligence sharing and operational coordination related to organized crime, including illicit drug trafficking and money laundering. There have been numerous conversations in recent years about using data mining technology to combat crime and terrorism. The Data Detective program from Sentient as a software company, uses data mining techniques to support the police (Sentient, 2017). The goals of this internship are to test out several predictive model solutions and choose the most effective and promising one. First, extensive literature reviews on data mining, crime analysis, and crime data mining were conducted. Sentient offered a 7-year archive of crime statistics that were daily aggregated to produce a univariate dataset. Moreover, a daily incidence type aggregation was performed to produce a multivariate dataset. Each solution's forecast period lasted seven days. Statistical models and neural network models were the two main groups into which the experiments were split. For the crime data, neural networks fared better than statistical models. This study gives a general review of the applied statistics and neural network models. A detailed image of each model's performance on the available data and generalizability is provided by a comparative analysis of all the models on a comparable dataset. Obviously, the studies demonstrated that, in comparison to other models, Gated Recurrent Units (GRU) produced greater prediction. The crime records of 2005-2019 which was collected from Nepal Police headquarter and analysed by R programming. In conclusion, gated recurrent unit implementation could give benefit to police in predicting crime. Hence, time series analysis using GRU could be a prospective additional feature in Data Detective.

Keywords: time series analysis, forecasting, ARIMA, machine learning

Procedia PDF Downloads 164

Authors: Ntandazo Dlatu, Benjamin Longo-Mbenza, Andre Renzaho, Ruffin Appalata, Yolande Yvonne Valeria Matoumona Mavoungou, Mbenza Ben Longo, Kenneth Ekoru, Blaise Makoso, Gedeon Longo Longo

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Background: The gap between complexities related to the integration of Tuberculosis /HIV control and evidence-based knowledge motivated the initiation of the study. Therefore, the objective of this study was to explore correlations between national TB management guidelines, multiple deprivation indexes, quantiles, components and levels of Tuberculosis control programme using mathematical modeling in rural O.R. Tambo District Health Facilities, South Africa. Methods: The study design used mixed secondary data analysis and cross-sectional analysis between 2009 and 2013 across O.R Tambo District, Eastern Cape, South Africa using univariate/ bivariate analysis, linear multiple regression models, and multivariate discriminant analysis. Health inequalities indicators and component of an impediment to the tuberculosis control programme were evaluated. Results: In total, 62 400 records for TB notification were analyzed for the period 2009-2013. There was a significant but negative between Financial Year Expenditure (r= -0.894; P= 0.041) Seropositive HIV status(r= -0.979; P= 0.004), Population Density (r = -0.881; P= 0.048) and the number of TB defaulter in all TB cases. It was shown unsuccessful control of TB management program through correlations between numbers of new PTB smear positive, TB defaulter new smear-positive, TB failure all TB, Pulmonary Tuberculosis case finding index and deprivation-concentration-dispersion index. It was shown successful TB program control through significant and negative associations between declining numbers of death in co-infection of HIV and TB, TB deaths all TB and SMIAD gradient/ deprivation-concentration-dispersion index. The multivariate linear model was summarized by unadjusted r of 96%, adjusted R2 of 95 %, Standard Error of estimate of 0.110, R2 changed of 0.959 and significance for variance change for P=0.004 to explain the prediction of TB defaulter in all TB with equation y= 8.558-0.979 x number of HIV seropositive. After adjusting for confounding factors (PTB case finding the index, TB defaulter new smear-positive, TB death in all TB, TB defaulter all TB, and TB failure in all TB). The HIV and TB death, as well as new PTB smear positive, were identified as the most important, significant, and independent indicator to discriminate most deprived deprivation index far from other deprivation quintiles 2-5 using discriminant analysis. Conclusion: Elimination of poverty such as overcrowding, lack of sanitation and environment of highest burden of HIV might end the TB threat in O.R Tambo District, Eastern Cape, South Africa. Furthermore, ongoing adequate budget comprehensive, holistic and collaborative initiative towards Sustainable Developmental Goals (SDGs) is necessary for complete elimination of TB in poor O.R Tambo District.

Keywords: tuberculosis, HIV/AIDS, success, failure, control program, health inequalities, South Africa

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Authors: Masood Otarod, Ronald M. Supkowski

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This abstract discusses a method that reduces the general dispersion model in cylindrical coordinates to a second order linear ordinary differential equation with constant coefficients so that it can be utilized to conduct kinetic studies in packed bed tubular catalytic reactors at a broad range of Reynolds numbers. The model was tested by 13CO isotope transient tracing of the CO adsorption of Boudouard reaction in a differential reactor at an average Reynolds number of 0.2 over Pd-Al2O3 catalyst. Detailed experimental results have provided evidence for the validity of the theoretical framing of the model and the estimated parameters are consistent with the literature. The solution of the general dispersion model requires the knowledge of the radial distribution of axial velocity. This is not always known. Hence, up until now, the implementation of the dispersion model has been largely restricted to the plug-flow regime. But, ideal plug-flow is impossible to achieve and flow regimes approximating plug-flow leave much room for debate as to the validity of the results. The reduction of the general dispersion model transpires as a result of the application of a factorization theorem. Factorization theorem is derived from the observation that a cross section of a catalytic bed consists of a solid phase across which the reaction takes place and a void or porous phase across which no significant measure of reaction occurs. The disparity in flow and the heterogeneity of the catalytic bed cause the concentration of reacting compounds to fluctuate radially. These variabilities signify the existence of radial positions at which the radial gradient of concentration is zero. Succinctly, factorization theorem states that a concentration function of axial and radial coordinates in a catalytic bed is factorable as the product of the mean radial cup-mixing function and a contingent dimensionless function. The concentration of adsorbed compounds are also factorable since they are piecewise continuous functions and suffer the same variability but in the reverse order of the concentration of mobile phase compounds. Factorability is a property of packed beds which transforms the general dispersion model to an equation in terms of the measurable mean radial cup-mixing concentration of the mobile phase compounds and mean cross-sectional concentration of adsorbed species. The reduced model does not require the knowledge of the radial distribution of the axial velocity. Instead, it is characterized by new transport parameters so denoted by Ωc, Ωa, Ωc, and which are respectively denominated convection coefficient cofactor, axial dispersion coefficient cofactor, and radial dispersion coefficient cofactor. These cofactors adjust the dispersion equation as compensation for the unavailability of the radial distribution of the axial velocity. Together with the rest of the kinetic parameters they can be determined from experimental data via an optimization procedure. Our data showed that the estimated parameters Ωc, Ωa Ωr, are monotonically correlated with the Reynolds number. This is expected to be the case based on the theoretical construct of the model. Computer generated simulations of methanation reaction on nickel provide additional support for the utility of the newly conceptualized dispersion model.

Keywords: factorization, general dispersion model, isotope transient kinetic, partial differential equations

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Authors: Pau Rodriguez, Jordi Gonzalez, Josep M. Gonfaus, Xavier Roca

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Image sharing in social networks has increased exponentially in the past years. Officially, there are 600 million Instagrammers uploading around 100 million photos and videos per day. Consequently, there is a need for developing new tools to understand the content expressed in shared images, which will greatly benefit social media communication and will enable broad and promising applications in education, advertisement, entertainment, and also psychology. Following these trends, our work aims to take advantage of the existing relationship between text and personality, already demonstrated by multiple researchers, so that we can prove that there exists a relationship between images and personality as well. To achieve this goal, we consider that images posted on social networks are typically conditioned on specific words, or hashtags, therefore any relationship between text and personality can also be observed with those posted images. Our proposal makes use of the most recent image understanding models based on neural networks to process the vast amount of data generated by social users to determine those images most correlated with personality traits. The final aim is to train a weakly-supervised image-based model for personality assessment that can be used even when textual data is not available, which is an increasing trend. The procedure is described next: we explore the images directly publicly shared by users based on those accompanying texts or hashtags most strongly related to personality traits as described by the OCEAN model. These images will be used for personality prediction since they have the potential to convey more complex ideas, concepts, and emotions. As a result, the use of images in personality questionnaires will provide a deeper understanding of respondents than through words alone. In other words, from the images posted with specific tags, we train a deep learning model based on neural networks, that learns to extract a personality representation from a picture and use it to automatically find the personality that best explains such a picture. Subsequently, a deep neural network model is learned from thousands of images associated with hashtags correlated to OCEAN traits. We then analyze the network activations to identify those pictures that maximally activate the neurons: the most characteristic visual features per personality trait will thus emerge since the filters of the convolutional layers of the neural model are learned to be optimally activated depending on each personality trait. For example, among the pictures that maximally activate the high Openness trait, we can see pictures of books, the moon, and the sky. For high Conscientiousness, most of the images are photographs of food, especially healthy food. The high Extraversion output is mostly activated by pictures of a lot of people. In high Agreeableness images, we mostly see flower pictures. Lastly, in the Neuroticism trait, we observe that the high score is maximally activated by animal pets like cats or dogs. In summary, despite the huge intra-class and inter-class variabilities of the images associated to each OCEAN traits, we found that there are consistencies between visual patterns of those images whose hashtags are most correlated to each trait.

Keywords: emotions and effects of mood, social impact theory in social psychology, social influence, social structure and social networks

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Authors: Rishana Bilimoria, Selina Tang, Sajni Shah, Marianne Henien, Christopher Sproat

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Temporomandibular disorders (TMD) may affect up to a third of the general population, and there is evidence demonstrating the majority of Myofascial TMD cases improve after education and conservative measures. In 2015 our department implemented a modified care pathway for myofascial TMD patients in an attempt to improve the patient journey. This involved the use of an interactive group therapy approach to deliver education, reinforce conservative measures and promote self-management. Patient reported experience measures from the new group clinic revealed 71% patient satisfaction. This service is efficient in improving aspects of health status while reducing health-care costs and redistributing clinical time. Since its’ establishment, 52 hours of clinical time, resources and funding have been redirected effectively. This Quality Improvement Project was initiated because it was felt that this new service was being underutilised by our surgical teams. The ‘Plan-Do-Study-Act cycle’ (PDSA) framework was employed to analyse utilisation of the service: The ‘plan’ stage involved outlining our aims: to raise awareness amongst clinicians of the unified care pathway and to increase referral to this clinic. The ‘do’ stage involved collecting data from a sample of 96 patients over 4 month period to ascertain the proportion of Myofascial TMD patients who were correctly referred to the designated clinic. ‘Suitable’ patients who weren’t referred were identified. The ‘Study’ phase involved analysis of results, which revealed that 77% of suitable patients weren’t referred to the designated clinic. They were reviewed on other clinics, which are often overbooked, or managed by junior staff members. This correlated with our original prediction. Barriers to referral included: lack of awareness of the clinic, individual consultant treatment preferences and patient, reluctance to be referred to a ‘group’ clinic. The ‘Act’ stage involved presenting our findings to the team at a clinical governance meeting. This included demonstration of the clinical effectiveness of the care-pathway and explaining the referral route and criteria. In light of the evaluation results, it was decided to keep the group clinic and maximize utilisation. The second cycle of data collection following these changes revealed that of 66 Myofascial TMD patients over a 4 month period, only 9% of suitable patients were not seen via the designated pathway; therefore this QIP was successful in meeting the set objectives. Overall, employing the PDSA cycle in this QIP resulted in appropriate utilisation of the modified care pathway for patients with myofascial TMD in Guy’s Oral Surgery Department. In turn, this leads to high patient satisfaction with the service and effectively redirected 52 hours of clinical time. It permitted adoption of a collaborative working style with oral surgery colleagues to investigate problems, identify solutions, and collectively raise standards of clinical care to ensure we adopt a unified care pathway in secondary care management of Myofascial TMD patients.

Keywords: myofascial, quality Improvement, PDSA, TMD

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Authors: Sylvana A. Vega, Cesar E. Huilinir, Carlos J. Gonzalez

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Co-digestion in anaerobic biodigesters is a technology improving hydrolysis by increasing methane generation. In the present study, the dimensional computational fluid dynamics (CFD) is numerically analyzed using Ansys Fluent software for agitation in a full-scale Continuous Stirred Tank Reactor (CSTR) biodigester during the co-digestion process. For this, a rheological study of the substrate is carried out, establishing rotation speeds of the stirrers depending on the microbial activity and energy ranges. The substrate is organic waste from industrial sources of sanitary water, butcher, fishmonger, and dairy. Once the rheological behavior curves have been obtained, it is obtained that it is a non-Newtonian fluid of the pseudoplastic type, with a solids rate of 12%. In the simulation, the rheological results of the fluid are considered, and the full-scale CSTR biodigester is modeled. It was coupling the second-order continuity differential equations, the three-dimensional Navier Stokes, the power-law model for non-Newtonian fluids, and three turbulence models: k-ε RNG, k-ε Realizable, and RMS (Reynolds Stress Model), for a 45° tilt vane impeller. It is simulated for three minutes since it is desired to study an intermittent mixture with a saving benefit of energy consumed. The results show that the absolute errors of the power number associated with the k-ε RNG, k-ε Realizable, and RMS models were 7.62%, 1.85%, and 5.05%, respectively, the numbers of power obtained from the analytical-experimental equation of Nagata. The results of the generalized Reynolds number show that the fluid dynamics have a transition-turbulent flow regime. Concerning the Froude number, the result indicates there is no need to implement baffles in the biodigester design, and the power number provides a steady trend close to 1.5. It is observed that the levels of design speeds within the biodigester are approximately 0.1 m/s, which are speeds suitable for the microbial community, where they can coexist and feed on the substrate in co-digestion. It is concluded that the model that more accurately predicts the behavior of fluid dynamics within the reactor is the k-ε Realizable model. The flow paths obtained are consistent with what is stated in the referenced literature, where the 45° inclination PBT impeller is the right type of agitator to keep particles in suspension and, in turn, increase the dispersion of gas in the liquid phase. If a 24/7 complete mix is considered under stirred agitation, with a plant factor of 80%, 51,840 kWh/year are estimated. On the contrary, if intermittent agitations of 3 min every 15 min are used under the same design conditions, reduce almost 80% of energy costs. It is a feasible solution to predict the energy expenditure of an anaerobic biodigester CSTR. It is recommended to use high mixing intensities, at the beginning and end of the joint phase acetogenesis/methanogenesis. This high intensity of mixing, in the beginning, produces the activation of the bacteria, and once reaching the end of the Hydraulic Retention Time period, it produces another increase in the mixing agitations, favoring the final dispersion of the biogas that may be trapped in the biodigester bottom.

Keywords: anaerobic co-digestion, computational fluid dynamics, CFD, net power, organic waste

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Authors: Almas Rajguru

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The Oligocene succession of the Tapti- Daman area is one of the established petroleum plays in Tapti-Daman block of the Mumbai Offshore Basin. Despite good control and production history, the sand geometry and continuity of reservoir character of these sediments are less understood as most reservoirs are thin and fall below seismic resolution. The present work focuses on a detailed analysis of the Early Oligocene Mahuva Formation at the reservoir scale through laboratory studies (sedimentology and biostratigraphy) of core and sidewall cores in integration with electro logs for firming up facies’ distribution, micro-depositional environment and sequence stratigraphy, diagenesis and reservoir characterization from seventeen wells from North Tapti-C-37 area in Tapti Daman Block, WOB. The thick shale/claystone with thin interbeds of sandstone and siltstones of deeper marine in the lower part of Mahuva Fm represents deposition in a transgressive regime. The overlying interbedded sandstone, glauconitic-siltstone/fine-grained sandstone, and thin beds of packstone/grainstone within highly fissile shale were deposited in a prograding tide-dominated delta during late-rise normal regression. Nine litho facies (F1-F9) representing deposition in various microenvironments of the tide-dominated delta are identified based on their characteristic sediment texture, structure and microfacies. Massive, gritty sandstone (F1) with poorly sorted sands lithic fragments with calcareous and Fe-rich matrix represents channel fill sediments. High-angle cross-stratified sandstone (F2) deposited in rapidly shifting/migrating bars under strong tidal currents. F3 records the laterally accreted tidal-channel point bars. F3 (low-angle cross-stratified to parallel bedded sandstone) and F4 (Clean sandstone) are often associated with F2 in a tidal bar complex. F5 (interbedded thin sand and mud) and F6 (bioturbated sandstone) represent tidal flat deposits. High energy open marine carbonate shoals (F8) and fossiliferous sandstone in offshore bars (F7) represent deepening up facies. Shallow marine standstill conditions facilitated the deposition of thick shale (F9) beds. The reservoir facies (F1-F6) are commonly poorly to moderately sorted; bimodal, immature sandstone represented by quartz-wacke. The framework grains are sub-angular to sub-rounded, medium to coarse-grained (occasionally gritty) embedded within argillaceous (kaolinite/chlorite/chamosite) to highly Fe-rich matrix (sideritic). The facies F7 and F8, representing the sandy packstone and grainstone facies, respectively, exhibit poor reservoir characteristics due to sanitization, diagenetic compaction and matrix-filled intergranular spaces. The various diagenetic features such as the presence of authigenic clays (kaolinite/dickite/smectite); ferruginous minerals like siderite, pyrite, hematite and other iron oxides; bioturbations; glauconite; calcite and quartz cementation, precipitation of gypsum, pressure solution and other compaction effects are identified. These diagenetic features, wherever present, have reduced porosity and permeability thereby adversely affecting reservoir quality. Tidal bar sandstones possess good reservoir characteristics such as moderate to good sorting, fair to good porosity and geometry that facilitates efficient lateral extension and vertical thickness of reservoir. The sand bodies of F2, F3 and F4 facies of Well L, M and Q deposited in a tidal bar complex exhibit good reservoir quality represented by relatively cleaner, poorly burrowed, loose, friable sandstone with good porosity. Sandstone facies around these wells could prove a potential hydrocarbon reservoir and could be considered for further exploration.

Keywords: reservoir sedimentology, facies analysis, HST, tide dominated delta, tidal bars

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Authors: Berengere Vire, Nicolas Salvetat, Yoann Lannay, Guillaume Marcellin, Siem Van Der Laan, Franck Molina, Dinah Weissmann

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Predicting suicidal behaviors is one of the most complex challenges of daily psychiatric practices. Today, suicide risk prediction using biological tools is not validated and is only based on subjective clinical reports of the at-risk individual. Therefore, there is a great need to identify biomarkers that would allow early identification of individuals at risk of suicide. Alterations of adenosine-to-inosine (A-to-I) RNA editing of neurotransmitter receptors and other proteins have been shown to be involved in etiology of different psychiatric disorders and linked to suicidal behavior. RNA editing is a co- or post-transcriptional process leading to a site-specific alteration in RNA sequences. It plays an important role in the epi transcriptomic regulation of RNA metabolism. On postmortem human brain tissue (prefrontal cortex) of depressed suicide victims, Alcediag found specific alterations of RNA editing activity on the mRNA coding for the serotonin 2C receptor (5-HT2cR). Additionally, an increase in expression levels of ADARs, the RNA editing enzymes, and modifications of RNA editing profiles of prime targets, such as phosphodiesterase 8A (PDE8A) mRNA, have also been observed. Interestingly, the PDE8A gene is located on chromosome 15q25.3, a genomic region that has recurrently been associated with the early-onset major depressive disorder (MDD). In the current study, we examined whether modifications in RNA editing profile of prime targets allow identifying disease-relevant blood biomarkers and evaluating suicide risk in patients. To address this question, we performed a clinical study to identify an RNA editing signature in blood of depressed patients with and without the history of suicide attempts. Patient’s samples were drawn in PAXgene tubes and analyzed on Alcediag’s proprietary RNA editing platform using next generation sequencing technology. In addition, gene expression analysis by quantitative PCR was performed. We generated a multivariate algorithm comprising various selected biomarkers to detect patients with a high risk to attempt suicide. We evaluated the diagnostic performance using the relative proportion of PDE8A mRNA editing at different sites and/or isoforms as well as the expression of PDE8A and the ADARs. The significance of these biomarkers for suicidality was evaluated using the area under the receiver-operating characteristic curve (AUC). The generated algorithm comprising the biomarkers was found to have strong diagnostic performances with high specificity and sensitivity. In conclusion, we developed tools to measure disease-specific biomarkers in blood samples of patients for identifying individuals at the greatest risk for future suicide attempts. This technology not only fosters patient management but is also suitable to predict the risk of drug-induced psychiatric side effects such as iatrogenic increase of suicidal ideas/behaviors.

Keywords: blood biomarker, next-generation-sequencing, RNA editing, suicide

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Authors: Ola Hall, Ibrahim Wahab, Thorsteinn Rognvaldsson, Mattias Ohlsson

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The subfield of poverty and welfare estimation that applies machine learning tools and methods on satellite imagery is a nascent but rapidly growing one. This is in part driven by the sustainable development goal, whose overarching principle is that no region is left behind. Among other things, this requires that welfare levels can be accurately and rapidly estimated at different spatial scales and resolutions. Conventional tools of household surveys and interviews do not suffice in this regard. While they are useful for gaining a longitudinal understanding of the welfare levels of populations, they do not offer adequate spatial coverage for the accuracy that is needed, nor are their implementation sufficiently swift to gain an accurate insight into people and places. It is this void that satellite imagery fills. Previously, this was near-impossible to implement due to the sheer volume of data that needed processing. Recent advances in machine learning, especially the deep learning subtype, such as deep neural networks, have made this a rapidly growing area of scholarship. Despite their unprecedented levels of performance, such models lack transparency and explainability and thus have seen limited downstream applications as humans generally are apprehensive of techniques that are not inherently interpretable and trustworthy. While several studies have demonstrated the superhuman performance of AI models, none has directly compared the performance of such models and human readers in the domain of poverty studies. In the present study, we directly compare the performance of human readers and a DL model using different resolutions of satellite imagery to estimate the welfare levels of demographic and health survey clusters in Tanzania, using the wealth quintile ratings from the same survey as the ground truth data. The cluster-level imagery covers all 608 cluster locations, of which 428 were classified as rural. The imagery for the human readers was sourced from the Google Maps Platform at an ultra-high resolution of 0.6m per pixel at zoom level 18, while that of the machine learning model was sourced from the comparatively lower resolution Sentinel-2 10m per pixel data for the same cluster locations. Rank correlation coefficients of between 0.31 and 0.32 achieved by the human readers were much lower when compared to those attained by the machine learning model – 0.69-0.79. This superhuman performance by the model is even more significant given that it was trained on the relatively lower 10-meter resolution satellite data while the human readers estimated welfare levels from the higher 0.6m spatial resolution data from which key markers of poverty and slums – roofing and road quality – are discernible. It is important to note, however, that the human readers did not receive any training before ratings, and had this been done, their performance might have improved. The stellar performance of the model also comes with the inevitable shortfall relating to limited transparency and explainability. The findings have significant implications for attaining the objective of the current frontier of deep learning models in this domain of scholarship – eXplainable Artificial Intelligence through a collaborative rather than a comparative framework.

Keywords: poverty prediction, satellite imagery, human readers, machine learning, Tanzania

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Authors: Scott M. Black, Craig Maclean, Pauline Hall Barrientos, Konstantinos Ritos, Asimina Kazakidi

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Aortic aneurysms and dissections are leading causes of death in cardiovascular disease. Both inevitably lead to hemodynamic instability without surgical intervention in the form of vascular stent-graft deployment. An accurate description of the aortic geometry and blood flow in patient-specific cases is vital for treatment planning and long-term success of such grafts, as they must generate physiological branch perfusion and in-stent hemodynamics. The aim of this study was to create patient-specific computational fluid dynamics (CFD) models through a multi-modality, multi-dimensional approach with boundary condition optimization to predict branch flow rates and in-stent hemodynamics in custom stent-graft configurations. Three-dimensional (3D) thoracoabdominal aortae were reconstructed from four-dimensional flow-magnetic resonance imaging (4D Flow-MRI) and computed tomography (CT) medical images. The former employed a novel approach to generate and enhance vessel lumen contrast via through-plane velocity at discrete, user defined cardiac time steps post-hoc. To produce patient-specific boundary conditions (BCs), the aortic geometry was reduced to a one-dimensional (1D) model. Thereafter, a zero-dimensional (0D) 3-Element Windkessel model (3EWM) was coupled to each terminal branch to represent the distal vasculature. In this coupled 0D-1D model, the 3EWM parameters were optimized to yield branch flow waveforms which are representative of the 4D Flow-MRI-derived in-vivo data. Thereafter, a 0D-3D CFD model was created, utilizing the optimized 3EWM BCs and a 4D Flow-MRI-obtained inlet velocity profile. A sensitivity analysis on the effects of stent-graft configuration and BC parameters was then undertaken using multiple stent-graft configurations and a range of distal vasculature conditions. 4D Flow-MRI granted unparalleled visualization of blood flow throughout the cardiac cycle in both the pre- and postsurgical states. Segmentation and reconstruction of healthy and stented regions from retrospective 4D Flow-MRI images also generated 3D models with geometries which were successfully validated against their CT-derived counterparts. 0D-1D coupling efficiently captured branch flow and pressure waveforms, while 0D-3D models also enabled 3D flow visualization and quantification of clinically relevant hemodynamic parameters for in-stent thrombosis and graft limb occlusion. It was apparent that changes in 3EWM BC parameters had a pronounced effect on perfusion distribution and near-wall hemodynamics. Results show that the 3EWM parameters could be iteratively changed to simulate a range of graft limb diameters and distal vasculature conditions for a given stent-graft to determine the optimal configuration prior to surgery. To conclude, this study outlined a methodology to aid in the prediction post-surgical branch perfusion and in-stent hemodynamics in patient specific cases for the implementation of custom stent-grafts.

Keywords: 4D flow-MRI, computational fluid dynamics, vascular stent-grafts, windkessel

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Authors: Weili Jiang, Simon Cadavid Lopera, Klaus Drechsler

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Fuel cell vehicle has become the most competitive solution for the transportation sector in the hydrogen economy. Type IV pressure vessel is currently the most popular and widely developed technology for the on-board storage, based on their high reliability and relatively low cost. Due to the stringent requirement on mechanical performance, the pressure vessel is subject to great amount of composite material, a major cost driver for the hydrogen tanks. Evidently, the optimization of composite layup design shows great potential in reducing the overall material usage, yet requires comprehensive understanding on underlying mechanisms as well as the influence of different design parameters on mechanical performance. Given the type of materials and manufacturing processes by which the type IV pressure vessels are manufactured, the design and optimization are a nuanced subject. The manifold of stacking sequence and fiber orientation variation possibilities have an out-standing effect on vessel strength due to the anisotropic property of carbon fiber composites, which make the design space high dimensional. Each variation of design parameters requires computational resources. Using finite element analysis to evaluate different designs is the most common method, however, the model-ing, setup and simulation process can be very time consuming and result in high computational cost. For this reason, it is necessary to build a reliable automation scheme to set up and analyze the di-verse composite layups. In this research, the simulation process of different tank designs regarding various parameters is conducted and automatized in a commercial finite element analysis framework Abaqus. Worth mentioning, the modeling of the composite overwrap is automatically generated using an Abaqus-Python scripting interface. The prediction of the winding angle of each layer and corresponding thickness variation on dome region is the most crucial step of the modeling, which is calculated and implemented using analytical methods. Subsequently, these different composites layups are simulated as axisymmetric models to facilitate the computational complexity and reduce the calculation time. Finally, the results are evaluated and compared regarding the ultimate tank strength. By automatically modeling, evaluating and comparing various composites layups, this system is applicable for the optimization of the tanks structures. As mentioned above, the mechanical property of the pressure vessel is highly dependent on composites layup, which requires big amount of simulations. Consequently, to automatize the simulation process gains a rapid way to compare the various designs and provide an indication of the optimum one. Moreover, this automation process can also be operated for creating a data bank of layups and corresponding mechanical properties with few preliminary configuration steps for the further case analysis. Subsequently, using e.g. machine learning to gather the optimum by the data pool directly without the simulation process.

Keywords: type IV pressure vessels, carbon composites, finite element analy-sis, automation of simulation process

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Authors: Sabrina Neururer, Marco Schweitzer, Werner Hackl, Bernhard Tilg, Patrick Raudaschl, Andreas Huber, Bernhard Pfeifer

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History and the current outbreak of Covid-19 have shown the deadly potential of infectious diseases. However, infectious diseases also have a serious impact on areas other than health and healthcare, such as the economy or social life. These areas are strongly codependent. Therefore, disease control measures, such as social distancing, quarantines, curfews, or lockdowns, have to be adopted in a very considerate manner. Infectious disease modeling can support policy and decision-makers with adequate information regarding the dynamics of the pandemic and therefore assist in planning and enforcing appropriate measures that will prevent the healthcare system from collapsing. In this work, an agent-based simulation package named “survival” for simulating infectious diseases is presented. A special focus is put on SARS-Cov-2. The presented simulation package was used in Austria to model the SARS-Cov-2 outbreak from the beginning of 2020. Agent-based modeling is a relatively recent modeling approach. Since our world is getting more and more complex, the complexity of the underlying systems is also increasing. The development of tools and frameworks and increasing computational power advance the application of agent-based models. For parametrizing the presented model, different data sources, such as known infections, wastewater virus load, blood donor antibodies, circulating virus variants and the used capacity for hospitalization, as well as the availability of medical materials like ventilators, were integrated with a database system and used. The simulation result of the model was used for predicting the dynamics and the possible outcomes and was used by the health authorities to decide on the measures to be taken in order to control the pandemic situation. The survival package was implemented in the programming language Java and the analytics were performed with R Studio. During the first run in March 2020, the simulation showed that without measures other than individual personal behavior and appropriate medication, the death toll would have been about 27 million people worldwide within the first year. The model predicted the hospitalization rates (standard and intensive care) for Tyrol and South Tyrol with an accuracy of about 1.5% average error. They were calculated to provide 10-days forecasts. The state government and the hospitals were provided with the 10-days models to support their decision-making. This ensured that standard care was maintained for as long as possible without restrictions. Furthermore, various measures were estimated and thereafter enforced. Among other things, communities were quarantined based on the calculations while, in accordance with the calculations, the curfews for the entire population were reduced. With this framework, which is used in the national crisis team of the Austrian province of Tyrol, a very accurate model could be created on the federal state level as well as on the district and municipal level, which was able to provide decision-makers with a solid information basis. This framework can be transferred to various infectious diseases and thus can be used as a basis for future monitoring.

Keywords: modelling, simulation, agent-based, SARS-Cov-2, COVID-19

Procedia PDF Downloads 174