Search results for: equation modeling methods

Authors: Hyun-Woo Cho

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For ensuring the safety of a manufacturing process one should quickly identify an assignable cause of a fault in an on-line basis. To this end, many statistical techniques including linear and nonlinear methods have been frequently utilized. However, such methods possessed a major problem of small sample size, which is mostly attributed to the characteristics of empirical models used for reference models. This work presents a new method to overcome the insufficiency of measurement data in the monitoring and diagnosis tasks. Some quasi-measurement data are generated from existing data based on the two indices of similarity and importance. The performance of the method is demonstrated using a real data set. The results turn out that the presented methods are able to handle the insufficiency problem successfully. In addition, it is shown to be quite efficient in terms of computational speed and memory usage, and thus on-line implementation of the method is straightforward for monitoring and diagnosis purposes.

Keywords: data analysis, diagnosis, monitoring, process data, quality control

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Janet C. Read

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UClan set collaboration with Lingokids to assess the Lingokids learning app's impact on learning outcomes in classrooms in the UK for children with ages ranging from 3 to 5 years. Data gathered during the controlled study with 69 children includes attitudinal data, engagement, and learning scores. Data shows that children enjoyment while learning was higher among those children using the game-based app compared to those children using other traditional methods. It’s worth pointing out that engagement when using the learning app was significantly higher than other traditional methods among older children. According to existing literature, there is a direct correlation between engagement, motivation, and learning. Therefore, this study provides relevant data points to conclude that Lingokids learning app serves its purpose of encouraging learning through playful and interactive content. That being said, we believe that learning outcomes should be assessed with a wider range of methods in further studies. Likewise, it would be beneficial to assess the level of usability and playability of the app in order to evaluate the learning app from other angles.

Keywords: learning app, learning outcomes, rapid test activity, Smileyometer, early childhood education, innovative pedagogy

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Authors: Anaissia Franca, Julian Fernandez, Curran Crawford, Ned Djilali

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A methodology to estimate the state-of-charge (SOC) of battery electric buses, including degradation effects, for a given driving cycle is presented to support long-term techno-economic analysis integrating electric buses and charging infrastructure. The degradation mechanisms, characterized by both capacity and power fade with time, have been modeled using an electrochemical model for Li-ion batteries. Iterative changes in the negative electrode film resistance and decrease in available lithium as a function of utilization is simulated for every cycle. The cycles are formulated to follow typical transit bus driving patterns. The power and capacity decay resulting from the degradation model are introduced as inputs to a longitudinal chassis dynamic analysis that calculates the power consumption of the bus for a given driving cycle to find the state-of-charge of the battery as a function of time. The method is applied to an in-depot charging scenario, for which the bus is charged exclusively at the depot, overnight and to its full capacity. This scenario is run both with and without including degradation effects over time to illustrate the significant impact of degradation mechanisms on bus performance when doing feasibility studies for a fleet of electric buses. The impact of battery degradation on battery lifetime is also assessed. The modeling tool can be further used to optimize component sizing and charging locations for electric bus deployment projects.

Keywords: battery electric bus, E-bus, in-depot charging, lithium-ion battery, battery degradation, capacity fade, power fade, electric vehicle, SEI, electrochemical models

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

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In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

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Authors: William Brasier, Nicolas Rabin, Celeste Joly

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Japanese knotweed (Fallopia japonica) is very present on the banks of the Rhone, colonizing more and more areas along the river. The Compagnie Nationale du Rhone (C.N.R.), which manages the river, has experimented with several control techniques in recent years. Since 2015, 15 experimental plots have been monitored on the banks of a restored river appendix to measure the effect of three control methods: confinement by felt, repeated mowing and the planting of competing species and/or species with allelopathic power: Viburnum opulus, Rhamnus frangula, Sambucus ebulus and Juglans regia. Each year, the number of stems, the number of elderberry plants, the height of the plants and photographs were collected. After six years of monitoring, the results showed that the density of knotweed stems decreased by 50 to 90% on all plots. The control methods are sustainable and are gradually gaining in efficiency. The establishment of native plants coupled with regular manual maintenance can reduce the development of Japanese knotweed. Continued monitoring over the next few years will determine the kinetics of the total eradication (i.e. 0 stem/plot) of the Japanese knotweed by these methods.

Keywords: fallopia japonica, interspecific plant competition , Rhone river, riparian trees

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Authors: S. Rostami, S. H. Hosseini, A. A. Torabi, M. Bekhradi

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Introduction: Interest in the use of caffeine as an ergogenic aid has increased since the International Olympic Committee lifted the partial ban on its use. Caffeine has beneficial effects on various aspects of athletic performance, but its effects on training have been neglected. The purpose of this study was to investigate the acute effect of caffeine on testosterone and cortisole in young futsal players. Methods: Twenty-four professional futsal players with 18.3± 1.9 years ingested caffeine doses of 0, 200 and 800 mg in random order 1 hr before an anaerobic-exercise session (RAST test). Samples were taken at the time of caffeine ingestion and 30 min after the session. Data were log-transformed to estimate percent effects with mixed modeling, and effects were standardized to assess magnitudes. fects on training have been neglected. Results: Testosterone concentration showed a small increase of 15% (90% confidence limits, ± 19%) during exercise. Caffeine raised this concentration in a dose-dependent manner by a further small 21% (± 24%) at the highest dose. The 800-mg dose also produced a moderate 52% (± 44%) increase in cortisol. The effect of caffeine on the testosterone: cortisol ratio was a small decline (14%; ± 21%). Discussion and Conclusion: Caffeine has some potential to benefit training outcomes via the anabolic effects of the increase in testosterone concentration, but this benefit might be counteracted by the opposing catabolic effects of the increase in cortisol and resultant decline in the testosterone: cortisol ratio.

Keywords: anabolic, catabolic, performance, testosterone, cortisol ratio, RAST test

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Authors: E. Feizi Ashtiani, S. Jamshidi, M.H Niksokhan, A. Feizi Ashtiani

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Waste load allocation (WLA) policies may use multi-objective optimization methods to find the most appropriate and sustainable solutions. These usually intend to simultaneously minimize two criteria, total abatement costs (TC) and environmental violations (EV). If other criteria, such as inequity, need for minimization as well, it requires introducing more binary optimizations through different scenarios. In order to reduce the calculation steps, this study presents value index as an innovative decision making approach. Since the value index contains both the environmental violation and treatment costs, it can be maximized simultaneously with the equity index. It implies that the definition of different scenarios for environmental violations is no longer required. Furthermore, the solution is not necessarily the point with minimized total costs or environmental violations. This idea is testified for Haraz River, in north of Iran. Here, the dissolved oxygen (DO) level of river is simulated by Streeter-Phelps equation in MATLAB software. The WLA is determined for fish farms using multi-objective particle swarm optimization (MOPSO) in two scenarios. At first, the trade-off curves of TC-EV and TC-Inequity are plotted separately as the conventional approach. In the second, the Value-Equity curve is derived. The comparative results show that the solutions are in a similar range of inequity with lower total costs. This is due to the freedom of environmental violation attained in value index. As a result, the conventional approach can well be replaced by the value index particularly for problems optimizing these objectives. This reduces the process to achieve the best solutions and may find better classification for scenario definition. It is also concluded that decision makers are better to focus on value index and weighting its contents to find the most sustainable alternatives based on their requirements.

Keywords: waste load allocation (WLA), value index, multi objective particle swarm optimization (MOPSO), Haraz River, equity

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Authors: Ajith Kumar S., Sankaran Namboothiri, Sankrish J., SarathKumar S., S. Anil Lal

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A numerical analysis of flow over a heated circular cylinder is done in this paper. The governing equations, Navier-Stokes, and energy equation within the Boussinesq approximation along with continuity equation are solved using hybrid FEM-FVM technique. The density gradient created due to the heating of the cylinder will induce buoyancy force, opposite to the direction of action of acceleration due to gravity, g. In the present work, the flow direction and the direction of buoyancy force are taken as same (vertical flow configuration), so that the buoyancy force accelerates the mean flow past the cylinder. The relative dominance of the buoyancy force over the inertia force is characterized by the Richardson number (Ri), which is one of the parameter that governs the flow dynamics and heat transfer in this analysis. It is well known that above a certain value of Reynolds number, Re (ratio of inertia force over the viscous forces), the unsteady Von Karman vortices can be seen shedding behind the cylinder. The shedding wake patterns could be seriously altered by heating/cooling the cylinder. The non-dimensional shedding frequency called the Strouhal number is found to be increasing as Ri increases. The aerodynamic force coefficients CL and CD are observed to change its value. In the present vertical configuration of flow over the cylinder, as Ri increases, shedding frequency gets increased and suddenly drops down to zero at a critical value of Richardson number. The unsteady vortices turn to steady standing recirculation bubbles behind the cylinder after this critical Richardson number. This phenomenon is well known in literature as "Breakdown of the Karman Vortex Street". It is interesting to see the flow structures on further increase in the Richardson number. On further heating of the cylinder surface, the size of the recirculation bubble decreases without loosing its symmetry about the horizontal axis passing through the center of the cylinder. The separation angle is found to be decreasing with Ri. Finally, we observed a second critical Richardson number, after which the the flow will be attached to the cylinder surface without any wake behind it. The flow structures will be symmetrical not only about the horizontal axis, but also with the vertical axis passing through the center of the cylinder. At this stage, there will be a "single plume" emanating from the rear stagnation point of the cylinder. We also observed the transition of the plume is a strong function of the Richardson number.

Keywords: drag reduction, flow over circular cylinder, flow control, mixed convection flow, vortex shedding, vortex breakdown

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Authors: Yilin Liao, Hewen Li, Paula McConvey

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Speed skaters often face a risk of concussion when they fall on the ice floor and impact crash mats during practices and competitive races. Several variables, including those related to the skater, the crash mat, and the impact position (body side/head/feet impact), are believed to influence the severity of the skater's concussion. While computer simulation modeling can be employed to analyze these accidents, the simulation process is time-consuming and does not provide rapid information for coaches and teams to assess the skater's injury risk in competitive events. This research paper promotes the exploration of the feasibility of using AI techniques for evaluating skater’s potential concussion severity, and to develop a fast concussion prediction tool using artificial neural networks to reduce the risk of treatment delays for injured skaters. The primary data is collected through virtual tests and physical experiments designed to simulate skater-mat impact. It is then analyzed to identify patterns and correlations; finally, it is used to train and fine-tune the artificial neural networks for accurate prediction. The development of the prediction tool by employing machine learning strategies contributes to the application of AI methods in sports science and has theoretical involvements for using AI techniques in predicting and preventing sports-related injuries.

Keywords: artificial neural networks, concussion, machine learning, impact, speed skater

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Authors: Emmanuelle Maria, Pierre Crançon, Gaëtane Lespes

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Colloids are ubiquitous in the environment and are known to play a major role in enhancing the transport of trace elements, thus being an important vector for contaminants dispersion. Colloids study and characterization are necessary to improve our understanding of the fate of pollutants in the environment. However, in stream water and groundwater, colloids are often very poorly concentrated. It is therefore necessary to pre-concentrate colloids in order to get enough material for analysis, while preserving their initial structure. Many techniques are used to extract and/or pre-concentrate the colloidal phase from bulk aqueous phase, but yet there is neither reference method nor estimation of the impact of these different techniques on the colloids structure, as well as the bias introduced by the separation method. In the present work, we have tested and compared several methods of colloidal phase extraction/pre-concentration, and their impact on colloids properties, particularly their size distribution and their elementary composition. Ultrafiltration methods (frontal, tangential and centrifugal) have been considered since they are widely used for the extraction of colloids in natural waters. To compare these methods, a ‘synthetic groundwater’ was used as a reference. The size distribution (obtained by Field-Flow Fractionation (FFF)) and the chemical composition of the colloidal phase (obtained by Inductively Coupled Plasma Mass Spectrometry (ICPMS) and Total Organic Carbon analysis (TOC)) were chosen as comparison factors. In this way, it is possible to estimate the pre-concentration impact on the colloidal phase preservation. It appears that some of these methods preserve in a more efficient manner the colloidal phase composition while others are easier/faster to use. The choice of the extraction/pre-concentration method is therefore a compromise between efficiency (including speed and ease of use) and impact on the structural and chemical composition of the colloidal phase. In perspective, the use of these methods should enhance the consideration of colloidal phase in the transport of pollutants in environmental assessment studies and forensics.

Keywords: chemical composition, colloids, extraction, preconcentration methods, size distribution

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Authors: Kin On Kwok, Vivian Wei, Benjamin Cowling, Steven Riley, Jonathan Read

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Background: The role of social contact behavior measured in different contact constructs in the transmission of respiratory pathogens with acute respiratory illness (ARI) remains unclear. We, therefore, aim to depict the individual pattern of ARI in the community and investigate the association between different contact dimensions and ARI in Hong Kong. Methods: Between June 2013 and September 2013, 620 subjects participated in the last two waves of recruitment of the population based longitudinal phone social contact survey. Some of the subjects in this study are from the same household. They are also provided with the symptom diaries to self-report any acute respiratory illness related symptoms between the two days of phone recruitment. Data from 491 individuals who were not infected on the day of phone recruitment and returned the symptom diaries after the last phone recruitment were used for analysis. Results: After adjusting different follow-up periods among individuals, the overall incidence rate of ARI was 1.77 per 100 person-weeks. Over 75% ARI episodes involve running nose, cough, sore throat, which are followed by headache (55%), malagia (35%) and fever (18%). Using a generalized estimating equation framework accounting for the cluster effect of subjects living in the same household, we showed that both daily number of locations visited with contacts and the number of contacts, explained the ARI incidence rate better than only one single contact construct. Conclusion: Our result suggests that it is the intertwining property of contact quantity (number of contacts) and contact intensity (ratio of subject-to-contact) that governs the infection risk by a collective set of respiratory pathogens. Our results provide empirical evidence that multiple contact constructs should be incorporated in the mathematical transmission models to feature a more realistic dynamics of respiratory disease.

Keywords: acute respiratory illness, longitudinal study, social contact, symptom diaries

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Authors: E. Arcos, E. Bautista, F. Méndez

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In this work, a scaling analysis of the liquefaction phenomena is presented. The characteristic scales are obtained by balancing term by term of the well-known partial dynamics governing equations, (U − P). From the above, the order of magnitude of the horizontal displacement is very smaller compared with the vertical displacement and therefore the governing equation is only a function of the dependent vertical variables. The U − P approximation is reduced and presented in its dimensionless version. This scaling analysis can be used to obtain analytical solutions of the liquefaction phenomena under the action of the water waves.

Keywords: approximation U-P, porous seabed, scaling analysis, water waves

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Authors: Kunal Shah, Swati Patel

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Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. Classification is used to predict class of unseen instance as accurate as possible. Multi label classification is a variant of single label classification where set of labels associated with single instance. Multi label classification is used by modern applications, such as text classification, functional genomics, image classification, music categorization etc. This paper introduces the task of multi-label classification, methods for multi-label classification and evolution measure for multi-label classification. Also, comparative analysis of multi label classification methods on the basis of theoretical study, and then on the basis of simulation was done on various data sets.

Keywords: binary relevance, concept drift, data stream mining, MLSC, multiple window with buffer

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Authors: Debasis Sengupta, Sudipta Das

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The class of life distributions with decreasing mean residual life (DMRL) is well known in the field of reliability modeling. It contains the IFR class of distributions and is contained in the NBUE class of distributions. While upper and lower bounds of the reliability distribution function of aging classes such as IFR, IFRA, NBU, NBUE, and HNBUE have discussed in the literature for a long time, there is no analogous result available for the DMRL class. We obtain the upper and lower bounds for the reliability function of the DMRL class in terms of first order finite moment. The lower bound is obtained by showing that for any fixed time, the minimization of the reliability function over the class of all DMRL distributions with a fixed mean is equivalent to its minimization over a smaller class of distribution with a special form. Optimization over this restricted set can be made algebraically. Likewise, the maximization of the reliability function over the class of all DMRL distributions with a fixed mean turns out to be a parametric optimization problem over the class of DMRL distributions of a special form. The constructive proofs also establish that both the upper and lower bounds are sharp. Further, the DMRL upper bound coincides with the HNBUE upper bound and the lower bound coincides with the IFR lower bound. We also prove that a pair of sharp upper and lower bounds for the reliability function when the distribution is increasing mean residual life (IMRL) with a fixed mean. This result is proved in a similar way. These inequalities fill a long-standing void in the literature of the life distribution modeling.

Keywords: DMRL, IMRL, reliability bounds, hazard functions

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Authors: Liwiusz Wojciechowski

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Research background: The current knowledge does not give a clear answer to the question of the impact of FDI on productivity. Results of the empirical studies are still inconclusive, no matter how extensive and diverse in terms of research approaches or groups of countries analyzed they are. It should also take into account the possibility that FDI and productivity are linked and that there is a bidirectional relationship between them. This issue is particularly important because on one hand FDI can contribute to changes in productivity in the host country, but on the other hand its level and dynamics may imply that FDI should be undertaken in a given country. As already mentioned, a two-way relationship between the presence of foreign capital and productivity in the host country should be assumed, taking into consideration the endogenous nature of FDI. Purpose of the article: The overall objective of this study is to determine the causality between foreign direct investment and total factor productivity in host county in terms of different relative absorptive capacity across countries. In the classic sense causality among variables is not always obvious and requires for testing, which would facilitate proper specification of FDI models. The aim of this article is to study endogeneity of selected macroeconomic variables commonly being used in FDI models in case of Visegrad countries: main recipients of FDI in CEE. The findings may be helpful in determining the structure of the actual relationship between variables, in appropriate models estimation and in forecasting as well as economic policymaking. Methodology/methods: Panel and time-series data techniques including GMM estimator, VEC models and causality tests were utilized in this study. Findings & Value added: The obtained results allow to confirm the hypothesis states the bi-directional causality between FDI and total factor productivity. Although results differ from among countries and data level of aggregation implications may be useful for policymakers in case of providing foreign capital attracting policy.

Keywords: endogeneity, foreign direct investment, multi-equation models, total factor productivity

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Authors: Cecile Meier, Drago Diaz Aleman, Itahisa Perez Conesa, Jose Luis Saorin Perez, Jorge De La Torre Cantero

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In this work, two ways of creating small-sized metal sculptures are proposed: the first by means of microcasting and the second by electroforming from models printed in 3D using an FDM (Fused Deposition Modeling‎) printer or using a DLP (Digital Light Processing) printer. It is viable to replace the wax in the processes of the artistic foundry with 3D printed objects. In this technique, the digital models are manufactured with resin using a low-cost 3D FDM printer in polylactic acid (PLA). This material is used, because its properties make it a viable substitute to wax, within the processes of artistic casting with the technique of lost wax through Ceramic Shell casting. This technique consists of covering a sculpture of wax or in this case PLA with several layers of thermoresistant material. This material is heated to melt the PLA, obtaining an empty mold that is later filled with the molten metal. It is verified that the PLA models reduce the cost and time compared with the hand modeling of the wax. In addition, one can manufacture parts with 3D printing that are not possible to create with manual techniques. However, the sculptures created with this technique have a size limit. The problem is that when printed pieces with PLA are very small, they lose detail, and the laminar texture hides the shape of the piece. DLP type printer allows obtaining more detailed and smaller pieces than the FDM. Such small models are quite difficult and complex to melt using the lost wax technique of Ceramic Shell casting. But, as an alternative, there are microcasting and electroforming, which are specialized in creating small metal pieces such as jewelry ones. The microcasting is a variant of the lost wax that consists of introducing the model in a cylinder in which the refractory material is also poured. The molds are heated in an oven to melt the model and cook them. Finally, the metal is poured into the still hot cylinders that rotate in a machine at high speed to properly distribute all the metal. Because microcasting requires expensive material and machinery to melt a piece of metal, electroforming is an alternative for this process. The electroforming uses models in different materials; for this study, micro-sculptures printed in 3D are used. These are subjected to an electroforming bath that covers the pieces with a very thin layer of metal. This work will investigate the recommended size to use 3D printers, both with PLA and resin and first tests are being done to validate use the electroforming process of microsculptures, which are printed in resin using a DLP printer.

Keywords: sculptures, DLP 3D printer, microcasting, electroforming, fused deposition modeling

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Authors: Siqi Qiu, Yijian Zheng, Xin Guo Ming

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In many reliability and risk analysis, failures of components are supposed to be independent. However, in reality, the ignorance of failure dependencies among components may render the results of reliability and risk analysis incorrect. There are two principal ways to incorporate failure dependencies in system reliability and risk analysis: implicit and explicit methods. In the implicit method, failure dependencies can be modeled by joint probabilities, correlation values or conditional probabilities. In the explicit method, certain types of dependencies can be modeled in a fault tree as mutually independent basic events for specific component failures. In this paper, explicit and implicit methods based on BDD will be proposed to evaluate the reliability of systems considering failure dependencies. The obtained results prove the equivalence of the proposed implicit and explicit methods. It is found that the consideration of failure dependencies decreases the reliability of systems. This observation is intuitive, because more components fail due to failure dependencies. The consideration of failure dependencies helps designers to reduce the dependencies between components during the design phase to make the system more reliable.

Keywords: reliability assessment, risk assessment, failure dependencies, binary decision diagram

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Authors: Jyoti Srivastava, Sandhya S. Kaushik, Mallika Tewari, Hari S. Shukla

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Little information is available about the development of psychological well being over time among women who have been undergoing treatment for breast cancer. The aim of this study was to identify the trajectories of depression anxiety and stress among women with early-stage breast cancer. Of the 48 Indian women with newly diagnosed early-stage breast cancer recruited from surgical oncology unit, 39 completed an interview and were assessed for depression anxiety and stress (Depression Anxiety Stress Scale-DASS 21) before their first course of chemotherapy (baseline) and follow up interviews at 3, 6 and 9 months thereafter. Growth mixture modeling was used to identify distinct trajectories of Depression Anxiety and Stress symptoms. Logistic Regression analysis was used to evaluate the characteristics of women in distinct groups. Most women showed mild to moderate level of depression and anxiety (68%) while normal to mild level of stress (71%). But one in 11 women was chronically anxious (9%) and depressed (9%). Young age, having a partner, shorter education and receiving chemotherapy but not radiotherapy might characterize women whose psychological symptoms remain strong nine months after diagnosis. By looking beyond the mean, it was found that several socio-demographic and treatment factors characterized the women whose depression, anxiety and stress level remained severe even nine months after diagnosis. The results suggest that support provided to cancer patients should have a special focus on a relatively small group of patient most in need.

Keywords: psychological well being, growth mixture modeling, logistic regression analysis, socio-demographic factors

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Authors: Avinoam Rabinovich

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CO₂ geological storage is a proven technology for reducing anthropogenic carbon emissions, which is paramount for achieving the ambitious net zero emissions goal. Core flooding experiments are an important step in any CO₂ storage project, allowing us to gain information on the flow of CO₂ and brine in the porous rock extracted from the reservoir. This information is important for understanding basic mechanisms related to CO₂ geological storage as well as for reservoir modeling, which is an integral part of a field project. In this work, a different method for constructing accurate models of CO₂-brine core flooding will be presented. Results for synthetic cases and real experiments will be shown and compared with numerical models to exhibit their predictive capabilities. Furthermore, the various mechanisms which impact the CO₂ distribution and trapping in the rock samples will be discussed, and examples from models and experiments will be provided. The new method entails solving an inverse problem to obtain a three-dimensional permeability distribution which, along with the relative permeability and capillary pressure functions, constitutes a model of the flow experiments. The model is more accurate when data from a number of experiments are combined to solve the inverse problem. This model can then be used to test various other injection flow rates and fluid fractions which have not been tested in experiments. The models can also be used to bridge the gap between small-scale capillary heterogeneity effects (sub-core and core scale) and large-scale (reservoir scale) effects, known as the upscaling problem.

Keywords: CO₂ geological storage, residual trapping, capillary heterogeneity, core flooding, CO₂-brine flow

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Authors: N. A. Masevhe, M. Nemukula, S. S. Gololo, K. G. Kgosana

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Background and study significance: Legume seeds are important in agriculture as they are used for feed formulations due to their nutrient-dense, low-cost and easy accessibility. Although they are important sources of energy, proteins, carbohydrates, vitamins, and minerals, they contain abundant quantities of anti-nutritive factors that reduce the bioavailability of nutrients, digestibility of proteins and mineral absorption in the livestock. However, the removal of these factors is too costly as it requires expensive state-of-the-art techniques such as high pressure and thermal processing. Basic methodologies: The aim of the study was to investigate cost-effective methods that can be used to reduce the inherent phytates as putative antinutrients in the legume seeds. The seeds of Arachis hypogaea, Pisum sativum and Vigna radiata L. were subjected to the single processing methods viz raw seeds plus dehulling (R+D), soaking plus dehulling (S+D), ordinary cooking plus dehulling (C+D), infusion plus dehulling (I+D), autoclave plus dehulling (A+D), microwave plus dehulling (M+D) and five combined methods (S+I+D; S+A+D; I+M+D; S+C+D; S+M+D). All the processed seeds were dried, ground into powder, extracted and analyzed on a microplate reader to determine the percentage of phytates per dry mass of the legume seeds. Phytic acid was used as a positive control, and one-way ANOVA was used to determine the significant differences between the means of the processing methods at a threshold of 0.05. Major findings: The results of the processing methods showed the percentage yield ranges of 39.1-96%, 67.4-88.8%, and 70.2-93.8% for V. radiata, A. hypogaea and P. sativum, respectively. Though the raw seeds contained the highest contents of phytates that ranged between 0.508 and 0.527%, as expected, the R+D resulted in a slightly lower phytate percentage range of 0.469-0.485%. At the same time, other processing methods resulted in phytate contents that were below 0.35%. The M+D and S+M+D methods showed low phytate percentage ranges of 0.276-0.296% and 0.272-0.294%, respectively, where the lowest percentage yield was determined in S+M+D of P. sativum. Furthermore, these results were found to be significantly different (p<0.05). Concluding statement: Despite the nutritive aspects of the processed legume seeds which is still in progress, the M+D and S+M+D methods which significantly reduced the phytates in the investigated legume seeds may be recommended to the local farmers and feed producing industries so as to enhance animal health and production at an affordable cost.

Keywords: anti-nutritive factors, extraction, legume seeds, phytate

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Authors: Hamza Nejib, Okba Taouali

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This paper presents two of the most knowing kernel adaptive filtering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing filters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to figure the performance of the approaches presented and finally to result which of them is the adapted one.

Keywords: online prediction, KAF, signal processing, RKHS, Kernel methods, KRLS, KLMS

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Authors: Omid Tavakol, Mahnaz Gholami

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Introduction : In this oral presentation the main goal is discussing immediate loading in dental implants , from treatment planning and surgical guide designing to delivery , follow up and occlusal consideration . Methods and materials : first of all systematic reviews about immediate loading will be considered . besides , a comparison will be made between immediate loading and conventional loading in terms of success rate and complications . After that different methods , prosthetic options and materials best used in immediate loading will be explained. Particularly multi unit abutments and their mechanism of function will be explained .Digital impressions and designing the temporaries is the next topic we are to explicate .Next issue is the differences between single unit , multiple unit and full arch implantation in immediate loading .Following we are going to describe methods for tissue engineering and papilla formation after extraction . Last slides are about a full mouth rehabilitation via immediate loading technique from surgical designing to follow up .At the end we would talk about potential complications , how to prevent from occurrence and what to do if we face up with .

Keywords: guided surgery, digital implantology, immediate loading, digital dentistry

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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

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Authors: Samaneh Yavari, Christiane Pferrer, Elisabeth Engelke, Alexander Starke, Juergen Rehage

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Background: Three nociceptive thresholds of thermal, electrical, and mechanical thresholds commonly use to evaluate the local anesthesia in many species, for instance, cow, horse, cat, dog, rabbit, and so on. Due to the lack of investigations to evaluate and/or validate such those nociceptive thresholds, our plan was the comparison of two-foot local anesthesia methods of Intravenous Regional Anesthesia (IVRA) and our modified four-point Nerve Block Anesthesia (NBA). Materials and Methods: Eight healthy nonpregnant nondairy Holstein Frisian cows in a cross-over study design were selected for this study. All cows divided into two different groups to receive two local anesthesia techniques of IVRA and our modified four-point NBA. Three thermal, electrical, and mechanical force and pinpricks were applied to evaluate the quality of local anesthesia methods before and after local anesthesia application. Results: The statistical evaluation demonstrated that our four-point NBA has a qualification to select as a standard foot local anesthesia. However, the recorded results of our study revealed no significant difference between two groups of local anesthesia techniques of IVRA and modified four-point NBA related to quality and duration of anesthesia stimulated by electrical, mechanical and thermal nociceptive stimuli. Conclusion and discussion: All three nociceptive threshold stimuli of electrical, mechanical and heat nociceptive thresholds can be applied to measure and evaluate the efficacy of foot local anesthesia of dairy cows. However, our study revealed no superiority of those three nociceptive methods to evaluate the duration and quality of bovine foot local anesthesia methods. Veterinarians to investigate the duration and quality of their selected anesthesia method can use any of those heat, mechanical, and electrical methods.

Keywords: mechanical, thermal, electrical threshold, IVRA, NBA, hind limb, dairy cow

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Authors: Thang Minh Le, Hadi Khabbaz

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The coefficient of consolidation is a parameter governing the rate at which saturated soil particularly clay undergoes consolidation when subjected to an increase in pressure. The rate and amount of compression in soil varies with the rate that pore water is lost; and hence depends on soil permeability. Over many years, various methods have been proposed to determine the coefficient of consolidation, cv, which is an indication of the rate of foundation settlement on soft ground. However, defining this parameter is often problematic and heavily relies on graphical techniques, which are subject to some uncertainties. This paper initially presents an overview of many well-established methods to determine the vertical coefficient of consolidation from the incremental loading consolidation tests. An array of consolidation tests was conducted on the undisturbed clay samples, collected at various depths from a site in Nakdong river delta, Busan, South Korea. The consolidation test results on these soft sensitive clay samples were employed to evaluate the targeted methods to predict the settlement rate of Busan clay. In relationship of time-displacement-velocity, a total of 3 method groups from 10 common procedures were classified and compared together. Discussions on study results will be also provided.

Keywords: Busan clay, coefficient of consolidation, constant rate of strain, incremental loading

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Authors: Vinod Nair, C. Sadasivan

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Beta-lactam antibiotics are the most frequently prescribed medications in modern medicine. The antibiotic resistance by the production of enzyme beta-lactamase is an important mechanism seen in microorganisms. Resistance to beta-lactams mediated by beta-lactamases can be overcome successfully with the use of beta-lactamase inhibitors. New generations of the antibiotics contain mostly synthetic compounds, and many side effects have been reported for them. Combinations of beta-lactam and beta-lactamase inhibitors have become one of the most successful antimicrobial strategies in the current scenario of bacterial infections. Plant-based drugs are very cheap and having lesser adverse effect than synthetic compounds. The synergistic effect of eugenol acetate with beta-lactams restores the activity of beta-lactams, allowing their continued clinical use. It is reported here the enhanced inhibitory effect of phytochemical, eugenol acetate, isolated from the plant Syzygium aromaticum with beta-lactams on beta-lactamase. The compound was found to have synergistic effect with the antibiotic amoxicillin against antibiotic-resistant strain of S.aureus. The enzyme was purified from the organism and incubated with the compound. The assay showed that the compound could inhibit the enzymatic activity of beta-lactamase. Modeling and molecular docking studies indicated that the compound can fit into the active site of beta-lactamase and can mask the important residue for hydrolysis of beta-lactams. The synergistic effects of eugenol acetate with beta-lactam antibiotics may justify, the use of these plant compounds for the preparation of β-lactamase inhibitors against β-lactam resistant S.aureus.

Keywords: betalactamase, eugenol acetate, synergistic effect, molecular modeling

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Authors: Saeed Delara, Kendra MacKay

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Varying methodologies are considered for pile design for both Russian and Western approaches. Although both approaches rely on toe and side frictional resistances, different calculation methods are proposed to estimate pile capacity. The Western approach relies on compactness (internal friction angle) of soil for cohesionless soils and undrained shear strength for cohesive soils. The Russian approach relies on grain size for cohesionless soils and liquidity index for cohesive soils. Though most recommended methods in the Western approaches are relatively simple methods to predict pile settlement, the Russian approach provides a detailed method to estimate single pile and pile group settlement. Details to calculate pile axial capacity and settlement using the Russian and Western approaches are discussed and compared against field test results.

Keywords: pile capacity, pile settlement, Russian approach, western approach

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Authors: Mostafa Mjahed

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This paper concerns the control of a nonlinear system using two different methods, reference model and genetic algorithm. The quadcopter is a nonlinear unstable system, which is a part of aerial robots. It is constituted by four rotors placed at the end of a cross. The center of this cross is occupied by the control circuit. Its motions are governed by six degrees of freedom: three rotations around 3 axes (roll, pitch and yaw) and the three spatial translations. The control of such system is complex, because of nonlinearity of its dynamic representation and the number of parameters, which it involves. Numerous studies have been developed to model and stabilize such systems. The classical PID and LQ correction methods are widely used. If the latter represent the advantage to be simple because they are linear, they reveal the drawback to require the presence of a linear model to synthesize. It also implies the complexity of the established laws of command because the latter must be widened on all the domain of flight of these quadcopter. Note that, if the classical design methods are widely used to control aeronautical systems, the Artificial Intelligence methods as genetic algorithms technique receives little attention. In this paper, we suggest comparing two PID design methods. Firstly, the parameters of the PID are calculated according to the reference model. In a second phase, these parameters are established using genetic algorithms. By reference model, we mean that the corrected system behaves according to a reference system, imposed by some specifications: settling time, zero overshoot etc. Inspired from the natural evolution of Darwin's theory advocating the survival of the best, John Holland developed this evolutionary algorithm. Genetic algorithm (GA) possesses three basic operators: selection, crossover and mutation. We start iterations with an initial population. Each member of this population is evaluated through a fitness function. Our purpose is to correct the behavior of the quadcopter around three axes (roll, pitch and yaw) with 3 PD controllers. For the altitude, we adopt a PID controller.

Keywords: quadcopter, genetic algorithm, PID, fitness, model, control, nonlinear system

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Authors: Sadeq Al Yaari, Fayza Alhammadi, Ayman Al Yaari, Montaha Al Yaari, Aayah Al Yaari, Adham Al Yaari, Sajedah Al Yaari, Saleh Al Yami

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Background: There has been a debate in the literature over the years as to whether or not MossTalk Words program fits Arab Broca’s aphasics (BAs) due to that language differences and also the fact that the technique has not yet been used for aphasics with semantic dementia (SD aphasics). Aims: To oversimplify the above mentioned debate slightly for purposes of exposition, the purpose of the present study is to investigate the “usability” of this program as well as pictures and community as therapeutic techniques for both Arab BAs and SD aphasics. Method: The subjects of this study are two Saudi aphasics (53 and 57 years old, respectively). The former suffers from Broca’s aphasia due to a stroke, while the latter suffers from semantic dementia. Both aphasics can speak English and have used the Moss Talk Words program in addition to intensive picture-naming therapeutic sessions for two years. They were tested by one of the researchers four times (a time per six months). The families of the two subjects, in addition to their relatives and friends, played a major part in all therapeutic sessions. Conclusion: Results show that in averages across the entire therapeutic sessions, MossTalk Words program was clearly found more effective in modeling BAs’ pronunciation than that of SD aphasic. Furthermore, picture-naming intensive exercises in addition to the positive role of the community members played a major role in the progress of the two subjects’ performance.

Keywords: moss talk words, program, technique, Broca’s aphasia, semantic dementia, subjects, picture, community

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