Search results for: traffic measurement and modeling

Authors: Wardatutthoyyibah, Satyawan Pudyatmoko, Sena Adi Subrata, Muhammad Ali Imron

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The proboscis monkey is an endemic species to the island of Borneo with conservation status IUCN (The International Union for Conservation of Nature) of endangered. The population of the monkey has a specific habitat and sensitive to habitat disturbances. As a consequence of increasing rates of land-use change in the last four decades, its population was reported significantly decreased. We quantified the effect of land use change on the proboscis monkey’s habitat through the species distribution modeling (SDM) approach with Maxent Software. We collected presence data and environmental variables, i.e., land cover, topography, bioclimate, distance to the river, distance to the road, and distance to the anthropogenic disturbance to generate predictive distribution maps of the monkeys. We compared two prediction maps for 2000 and 2015 data to represent the current habitat of the monkey. We overlaid the monkey’s predictive distribution map with the existing protected areas to investigate whether the habitat of the monkey is protected under the protected areas networks. The results showed that almost 50% of the monkey’s habitat reduced as the effect of land use change. And only 9% of the current proboscis monkey’s habitat within protected areas. These results are important for the master plan of conservation of the endangered proboscis monkey and provide scientific guidance for the future development incorporating biodiversity issue.

Keywords: endemic species, land use change, maximum entropy, spatial distribution

Procedia PDF Downloads 159

Authors: Julius Steinkopf, Eric Rost, Aike Hessel, Sonia Lippke

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Objective: This study examined possible determinants of return to work (RTW) in individuals who participated in a medical rehabilitation program longitudinally over a time period of six months. Design/methodology/approach: N=1,044 rehabilitants were included in the baseline measurement in terms of completing a questionnaire during their medical rehabilitation. About 30% (n=350) have remained in the study in terms of participating in computer-assisted telephone interviewing (CATI) six months later. Frequency analyses and Regression analyses were run. Findings: About 70% of the rehabilitants returned to work six months after rehabilitation. Regression analyses revealed that the RTW rates were significantly predicted by gender (OR=0.12, men were more likely to return), perceived social support (OR=3.01) and current physical functioning (OR=1.25). Furthermore RTW motives, like expected monetary rewards (OR=25.2) and feelings of being needed (OR=0.18) same as motives for not returning to work (nRTW), like the wish to stop working in order to spend time with the spouse (OR=0.13) or a lack of enjoyment of work (OR=3.81), significantly predicted return to work rates. Life satisfaction, self-efficacy beliefs, mental health, current income, educational background or age did not significantly increase explained variance (all ps > .05). Practical implications: Taking theses predictors into account provides options to increase the effectiveness of interventions aiming at increasing RTW: Medical rehabilitations should not only aim at improving the physical functioning but also to enhance beneficial motives and social support as well as support women specifically in order to improve the effectiveness of medical rehabilitation and public health interventions. Originality/value: Illness-caused work absences are related to high financial costs and individual burden. Despite of the public health and societal implications, this is one of the very few studies investigating systematically fitness and health for the return to work.

Keywords: gender, fitness, health, physical functioning

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Authors: Brunno Vasques

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Due to the non-toxic nature and high performance in terms of vacuum specific impulse and density specific impulse, the combination of liquid oxygen and liquid methane have been identified as a promising option for future space vehicle systems. Applications requiring throttling capability include specific missions such as rendezvous, planetary landing and de-orbit as well as weapon systems. One key challenge in throttling liquid rocket engines is maintaining an adequate pressure drop across the injection elements, which is necessary to provide good propellant atomization and mixing as well as system stability. The potential scalability of pintle injectors, their great suitability to throttling and inherent combustion stability characteristics led to investigations using a variety of propellant combinations, including liquid oxygen and hydrogen and fluorine-oxygen and methane. Presented here are the preliminary performance and heat transfer information obtained during hot-fire testing of a pintle injector running on liquid oxygen and liquid methane propellants. The specific injector design selected for this purpose is a multi-configuration building block version with replaceable injection elements, providing flexibility to accommodate hardware modifications with minimum difficulty. On the basis of single point runs and the use of a copper/nickel segmented calorimetric combustion chamber and associated transient temperature measurement, the characteristic velocity efficiency, injector footprint and heat fluxes could be established for the first proposed pintle configuration as a function of injection velocity- and momentum-ratios. A description of the test-bench is presented as well as a discussion of irregularities encountered during testing, such as excessive heat flux into the pintle tip resulting from certain operating conditions.

Keywords: green propellants, hot-fire performance, rocket engine throttling, pintle injector

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Authors: Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Fillia Makedon, Glenn Wylie

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Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject. Given fMRI data, the problem of predicting the state of cognitive fatigue in a person has not been investigated to its full extent. This paper proposes tackling this issue as a multi-class classification problem by dividing the state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions. We built a spatio-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans. We also applied a self-supervised method called MoCo (Momentum Contrast) to pre-train our model on a public dataset BOLD5000 and fine-tuned it on our labeled dataset to predict cognitive fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem.

Keywords: fMRI, brain imaging, deep learning, self-supervised learning, contrastive learning, cognitive fatigue

Procedia PDF Downloads 191

Authors: Muqdad Al-Juboori, Bithin Datta

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Hydraulic structures such as gravity dams are classified as essential structures, and have the vital role in providing strong and safe water resource management. Three major aspects must be considered to achieve an effective design of such a structure: 1) The building cost, 2) safety, and 3) accurate analysis of seepage characteristics. Due to the complexity and non-linearity relationships of the seepage process, many approximation theories have been developed; however, the application of these theories results in noticeable errors. The analytical solution, which includes the difficult conformal mapping procedure, could be applied for a simple and symmetrical problem only. Therefore, the objectives of this paper are to: 1) develop a surrogate model based on numerical simulated data using SEEPW software to approximately simulate seepage process related to a hydraulic structure, 2) develop and solve a linked simulation-optimization model based on the developed surrogate model to describe the seepage occurring under a concrete gravity dam, in order to obtain optimum and safe design at minimum cost. The result shows that the linked simulation-optimization model provides an efficient and optimum design of concrete gravity dams.

Keywords: artificial neural network, concrete gravity dam, genetic algorithm, seepage analysis

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Authors: Asmae Tafraouti, Yasmina Layouni, Pascal Kleimann

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The growing market for Microsystems (MST) and Micro-Electromechanical Systems (MEMS) is driving the research for alternative manufacturing techniques to microelectronics-based technologies, which are generally expensive and time-consuming. Hot-embossing and micro-injection modeling of thermoplastics appear to be industrially viable processes. However, both require the use of master models, usually made in hard materials such as steel. These master models cannot be fabricated using standard microelectronics processes. Thus, other micromachining processes are used, as laser machining or micro-electrical discharge machining (µEDM). In this work, µEDM has been used. The principle of µEDM is based on the use of a thin cylindrical micro-tool that erodes the workpiece surface. The two electrodes are immersed in a dielectric with a distance of a few micrometers (gap). When an electrical voltage is applied between the two electrodes, electrical discharges are generated, which cause material machining. In order to produce master models with high resolution and smooth surfaces, it is necessary to well control the discharge mechanism. However, several problems are encountered, such as a random electrical discharge process, the fluctuation of the discharge energy, the electrodes' polarity inversion, and the wear of the micro-tool. The effect of different parameters, such as the applied voltage, the working capacitor, the micro-tool diameter, the initial gap, has been studied. This analysis helps to improve the machining performances, such: the workpiece surface condition and the lateral crater's gap.

Keywords: craters, electrical discharges, micro-electrical discharge machining (µEDM), microsystems

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Authors: Tawfiq Chekifi, Brahim Dennai, Rachid Khelfaoui

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Over the last few decades, modeling immiscible fluids such as oil and water have been a classical research topic. Droplet-based microfluidics presents a unique platform for mixing, reaction, separation, dispersion of drops, and numerous other functions. For this purpose, several devices were studied, as well as microfluidic oscillator. The latter was obtained from wall attachment microfluidic amplifiers using a feedback loop from the outputs to the control inputs, nevertheless this device have not well used for microdrops applications. In this paper, we suggest a numerical CFD study of a microfluidic oscillator with two different lengths of feedback loop. In order to produce simultaneous microdrops of gasoil on water, a typical geometry that includes double T-junction is connected to the fluidic oscillator. The generation of microdrops is computed by volume-of-fluid method (VOF). Flow oscillations of microdrops were triggered by the Coanda effect of jet flow. The aim of work is to obtain a high oscillation frequency in output of this passive device, the influence of hydrodynamics and physics parameters on the microdrops frequency in the output of our microsystem is also analyzed, The computational results show that, the length of feedback loop, applied pressure on T-junction and interfacial tension have a significant effect on the dispersion of microdrops and its oscillation frequency. Across the range of low Reynold number, the microdrops generation and its dynamics have been accurately controlled by adjusting applying pressure ratio of two phases.

Keywords: fluidic oscillator, microdrops manipulation, VOF (volume of fluid method), microfluidic oscillator

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Authors: Peter K. Galenko, Stefanie Koch, Markus Rettenmayr, Robert Wonneberger, Evgeny V. Kharanzhevskiy, Maria Zamoryanskaya, Vladimir Ankudinov

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We analyze the structure of undercooled melts, crystal growth kinetics and amorphous/crystalline microstructure of rapidly solidifying glass-forming Pd-based and CuZr-based alloys. A dendrite growth model is developed using a combination of the kinetic phase-field model and mesoscopic sharp interface model. The model predicts features of crystallization kinetics in alloys from thermodynamically controlled growth (governed by the Gibbs free energy change on solidification) to the kinetically limited regime (governed by atomic attachment-detachment processes at the solid/liquid interface). Comparing critical undercoolings observed in the crystallization kinetics with experimental data on melt viscosity, atomistic simulation's data on liquid microstructure and theoretically predicted dendrite growth velocity allows us to conclude that the dendrite growth kinetics strongly depends on the cluster structure changes of the melt. The obtained data of theoretical and experimental investigations are used for interpretation of microstructure of samples processed in electro-magnetic levitator on board International Space Station in the frame of the project "MULTIPHAS" (European Space Agency and German Aerospace Center, 50WM1941) and "KINETIKA" (ROSKOSMOS).

Keywords: dendrite, kinetics, model, solidification

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Authors: Ruangdech Sirikit

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The purposes of this research were to study the variables affecting the quality assessment of mathematics subject in Thailand by using value-added analysis on TIMSS 2011. The data used in this research is the secondary data from the 2011 Trends in International Mathematics and Science Study (TIMSS), collected from 6,124 students in 172 schools from Thailand, studying only mathematics subjects. The data were based on 14 assessment tests of knowledge in mathematics. There were 3 steps of data analysis: 1) To analyze descriptive statistics 2) To estimate competency of students from the assessment of their mathematics proficiency by using MULTILOG program; 3) analyze value added in the model of quality assessment using Value-Added Model with Hierarchical Linear Modeling (HLM) and 2 levels of analysis. The research results were as follows: 1. Student level variables that had significant effects on the competency of students at .01 levels were Parental care, Resources at home, Enjoyment of learning mathematics and Extrinsic motivation in learning mathematics. Variable that had significant effects on the competency of students at .05 levels were Education of parents and self-confident in learning mathematics. 2. School level variable that had significant effects on competency of students at .01 levels was Extra large school. Variable that had significant effects on competency of students at .05 levels was medium school.

Keywords: quality assessment, value-added model, TIMSS, mathematics, Thailand

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Authors: Mohamed Dhaoui, Hakim Gabtni

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The migration of hydrocarbons is a fairly complicated process that depends on several parameters, both structural and sedimentological. In this study, we will try to determine secondary migration paths which convey hydrocarbon from their main source rock to the largest reservoir of the Paleozoic petroleum system of the Tunisian part of Ghadames basin. In fact, The Silurian source rock is the main source rock of the Paleozoic petroleum system of the Ghadames basin. However, the most solicited reservoir in this area is the Triassic reservoir TAGI (Trias Argilo-Gréseux Inférieur). Several geochemical studies have confirmed that oil products TAGI come mainly from the Tannezuft Silurian source rock. That being said that secondary migration occurs through the fault system which affects the post-Silurian series. Our study is based on analysis and interpretation of gravity data. The gravity modeling was conducted in the northern part of Ghadames basin and the Telemzane uplift. We noted that there is a close relationship between the location of producing oil fields and gravity gradients which separate the positive and negative gravity anomalies. In fact, the analysis and transformation of the Bouguer anomaly map, and the residual gravity map allowed as understanding the architecture of the Precambrian in the study area, thereafter gravimetric models were established allowed to determine the probable migration path.

Keywords: basement, Ghadames, gravity, hydrocarbon, migration path

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Authors: Jiahui Song

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The application of an electric field can cause poration at cell membranes. This includes the outer plasma membrane, as well as the membranes of intracellular organelles. In order to analyze and predict such electroporation effects, it becomes necessary to first evaluate the electric fields and the transmembrane voltages. This information can then be used to assess changes in the pore formation energy that finally yields the pore distributions and their radii based on the Smolchowski equation. The dynamic pore model can be achieved by including a dynamic aspect and a dependence on the pore population density into the pore formation energy equation. These changes make the pore formation energy E(r) self-adjusting in response to pore formation without causing uncontrolled growth and expansion. By using dynamic membrane tension, membrane electroporation in response to a 180kV/cm trapezoidal pulse with a 10 ns on time and 1.5 ns rise- and fall-times is discussed. Poration is predicted to occur at times beyond the peak at around 9.2 ns. Modeling also yields time-dependent distributions of the membrane pore population after multiple pulses. It shows that the pore distribution shifts to larger values of the radius with multiple pulsing. Molecular dynamics (MD) simulations are also carried out for a fixed field of 0.5 V/nm to demonstrate nanopore formation from a microscopic point of view. The result shows that the pore is predicted to be about 0.9 nm in diameter and somewhat narrower at the central point.

Keywords: high-intensity, nanosecond, dynamics, electroporation

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Authors: Odenigbo Veronica Ngozi, Ukwuaba Loretta Chika, Ukamaka Eze

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This study delved into youth’s empowerment pathway to unlocking entrepreneurial skills and employability in Enugu state, Nigeria. The purpose of the study is to ascertain the effect of youth joblessness in Enugu State. Two research questions guided the study, and two null hypotheses, which were formulated and tested at a 0.05 level of significance, were used for the analysis. A descriptive survey research design was employed for the study. The population for the study consisted of 103 male and female youths in Enugu state, who were randomly sampled among youths as respondents. A structured questionnaire which was developed by researchers and titled ‘Youth Empowerment a Pathway to Unlocking Entrepreneurial Skills and Employability (YEPUESEQ)’, was used to elicit information from the respondents. The instrument was validated by three experts, one from the Department of Measurement and Evaluation and two from the Department of Continuing Education and Development Studies, all from Enugu State University of Science and Technology, Agbani (ESUT). Cronbach Alpha reliability estimate was used to determine the internal consistency of the instrument with a reliability index of 0.74, indicating that the instrument is highly reliable and suitable to elicit information from the respondent. Data collected were analyzed using mean and standard deviation to answer the two research questions, while the null hypotheses were tested at 0.05 level of significance using a t-test. The findings of the study indicated that when the youths are jobless, they tend to join bad gangs in as much as they can get money, but when they are empowered, they tend towards supporting themselves, the community, and the nation as a whole in bringing in human, economic and social development to society. Based on the findings, the researchers recommended, among others, that the government should establish skill acquisition centers while the youths can enroll for a particular skill of their choice with good facilities, infrastructures and qualified facilitators.

Keywords: youth, empowerment, entrepreneurial skill, employability skill

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Authors: Ka Ho Yau, Jan Frederick Engels, Kwok Kei Lai, Reinhard Renneberg

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Lateral flow test is a prevalent technology in various sectors such as food, pharmacology and biomedical sciences. Colloidal gold (CG) is widely used as the signalling molecule because of the ease of synthesis, bimolecular conjugation and its red colour due to intrinsic SPRE. However, the production of colloidal gold is costly and requires vigorous conditions. The stability of colloidal gold are easily affected by environmental factors such as pH, high salt content etc. Silica nanoparticles are well known for its ease of production and stability over a wide range of solvents. Using reverse micro-emulsion (w/o), silica nanoparticles with different sizes can be produced precisely by controlling the amount of water. By incorporating different water-soluble dyes, a rainbow colour of the silica nanoparticles could be produced. Conjugation with biomolecules such as antibodies can be achieved after surface modification of the silica nanoparticles with organosilane. The optimum amount of the antibodies to be labelled was determined by Bradford Assay. In this work, we have demonstrated the ability of the dye-doped silica nanoparticles as a signalling molecule in lateral flow test, which showed a semi-quantitative measurement of the analyte. The image was further analysed for the LOD=10 ng of the analyte. The working range and the linear range of the test were from 0 to 2.15μg/mL and from 0 to 1.07 μg/mL (R2=0.988) respectively. The performance of the tests was comparable to those using colloidal gold with the advantages of lower cost, enhanced stability and having a wide spectrum of colours. The positives lines can be imaged by naked eye or by using a mobile phone camera for a better quantification. Further research has been carried out in multicolour detection of different biomarkers simultaneously. The preliminary results were promising as there was little cross-reactivity being observed for an optimized system. This approach provides a platform for multicolour detection for a set of biomarkers that enhances the accuracy of diseases diagnostics.

Keywords: colorimetric detection, immunosensor, paper-based biosensor, silica

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Authors: Ajai Singh

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Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using Soil and Water Assessment Tool (SWAT), a watershed scale model and Radial Basis Neural Network (RBNN), an artificial neural network model. Both the models were calibrated and validated based on measured stream flow and quantification of the uncertainty in SWAT model output was assessed using ‘‘Sequential Uncertainty Fitting Algorithm’’ (SUFI-2). Though both the model predicted satisfactorily, but RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. Comparison of the results of the two models also indicates a wider prediction interval for the results of the SWAT model. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91% is higher than the P-factor in SWAT as 87%. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models.

Keywords: SWAT, RBNN, SUFI 2, bootstrap technique, stream flow, simulation

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Authors: Lisha Tan, Yunbo Nie, Mohammad Rahnama

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This study investigates the feasibility of harnessing train-induced airflows in underground metro stations as a source of renewable energy. Field measurements were conducted at multiple SkyTrain stations to assess wind speed distributions caused by passing trains. The data revealed significant airflow velocities with multimodal characteristics driven by varying train operations. These airflow velocities represent substantial kinetic energy that can be converted into usable power. Calculations showed that wind power densities within the underground tunnels ranged from 0.97 W/m² to 3.46 W/m², based on average cubed wind speeds, indicating considerable energy content available for harvesting. A Bayesian method was utilized to model these wind speed distributions, effectively capturing the complex airflow patterns. Further analysis using RETScreen evaluated the cost-benefit and environmental impact of implementing energy harvesting systems. Preliminary results suggest that the proposed system could result in substantial energy savings, reduce CO₂ emissions, and provide a favorable payback period, highlighting the economic and environmental viability of integrating wind turbines into metro stations.

Keywords: train-induced airflows, renewable energy generation, wind power density, RETScreen

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Authors: Sylvester S. Horvey, Jones Mensah

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The growing complexities and dynamics in the business environment have led to a new approach to risk management, known as enterprise risk management (ERM). ERM is a system and an approach to managing the risks of an organization in an integrated manner to achieve the corporate goals and strategic objectives. Regardless of the diversities in the business environment, ERM has become an essential factor in managing individual and business risks because ERM is believed to enhance shareholder value and firm growth. Despite the growing number of literature on ERM, the question about what factors drives ERM remains limited. This study provides a comprehensive literature review of the main factors that contribute to ERM implementation. Google Scholar was the leading search engine used to identify empirical literature, and the review spanned between 2000 and 2020. Articles published in Scimago journal ranking and Scopus were examined. Thirteen firm characteristics and sixteen articles were considered for the empirical review. Most empirical studies agreed that firm size, institutional ownership, industry type, auditor type, industrial diversification, earnings volatility, stock price volatility, and internal auditor had a positive relationship with ERM adoption, whereas firm size, institutional ownership, auditor type, and type of industry were mostly seen be statistically significant. Other factors such as financial leverage, profitability, asset opacity, international diversification, and firm complexity revealed an inconclusive result. The growing literature on ERM is not without limitations; hence, this study suggests that further research should examine ERM determinants within a new geographical context while considering a new and robust way of measuring ERM rather than relying on a simple proxy (dummy) for ERM measurement. Other firm characteristics such as organizational culture and context, corporate scandals and losses, and governance could be considered determinants of ERM adoption.

Keywords: enterprise risk management, determinants, ERM adoption, literature review

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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: 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: Bahar Tunctan, Sefika Pinar Kucukkavruk, Meryem Temiz-Resitoglu, Demet Sinem Guden, Ayse Nihal Sari, Seyhan Sahan-Firat

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We hypothesized that rexinoids such as bexarotene, a selective retinoid X receptor α (RXRα) agonist, may be beneficial for preventing mortality due to inflammation associated with increased expression/activity of inducible nitric oxide synthase (iNOS) induced by lipopolysaccharide (LPS). Therefore, we investigated effects of bexarotene on the changes in circulating protein levels of iNOS (an index for systemic iNOS expression), myeloperoxidase (MPO) (an index for systemic inflammation), and lactate dehydrogenase (LDH) (an index for systemic tissue injury) in LPS-induced systemic inflammation model resulting in septic shock in rats. Rats were injected with saline (4 ml/kg; i.p.), LPS (10 mg/kg; i.p.), dimethylsulphoxide (4 ml/kg, 0.1%; s.c.) at time 0. Mean arterial blood pressure and heart rate were measured using a tail-cuff device. Bexarotene (0.03, 0.1, 0.3, and 1 mg/kg; s.c.) was administered to separate groups of rats 1 h after injection of saline or LPS. The rats were sacrificed 4 h after saline or LPS injection and blood was collected for measurement of serum iNOS, MPO, and LDH protein levels. Blood pressure decreased by 31 mmHg and heart rate increased by 63 bpm in the LPS-treated rats. Bexarotene at 0.3 and 1 mg/kg doses caused 20% mortality 4 h after LPS injection. In the LPS-treated rats, serum iNOS, MPO, and LDH protein levels were increased. Bexarotene only at 0.1 mg/kg dose prevented the LPS-induced hypotension and increased in iNOS, MPO, and LDH protein levels. These data are consistent with the view that a decrease in systemic iNOS levels contributes to the beneficial effect of bexarotene to prevent the hypotension associated with inflammation and tissue injury during rat endotoxemia. [This work was financially supported by The Scientific and Technological Research Council of Turkey (SBAG-109S121)].

Keywords: bexarotene, inflammation, iNOS, lipopolisaccharide, RXRa

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Authors: Sibel Karacaoglu, Fatma Ç. Kayapinar

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The aim of the study is to investigate the effect of the core training program on physical fitness characteristics and body composition in male volleyball players. 26 male university volleyball team players aged between 19 to 24 years who had no health problems and injury participated in the study. Subjects were divided into training (TG) and control groups (CG) as randomly. Data from twenty-one players who completed all training sessions were used for statistical analysis (TG,n=11; CG,n=10). A core training program was applied to the training group three days a week for 10 weeks. On the other hand, the control group did not receive any training. Before and after the 10-week training program, pre- and post-testing comprised of body composition measurements (weight, BMI, bioelectrical impedance analysis) and physical fitness measurements including flexibility (sit and reach test), muscle strength (back, leg and grip strength by dynamometer), muscle endurance (sit-ups and push-ups tests), power (one-legged jump and vertical jump tests), speed (20m sprint, 30m sprint) and balance tests (one-legged standing test) were performed. Changes of pre- and post- test values of the groups were determined by using dependent t test. According to the statistical analysis of data, no significant difference was found in terms of body composition in the both groups for pre- and post- test values. In the training group, all physical fitness measurements improved significantly after core training program (p<0.05) except 30m speed and handgrip strength (p>0.05). On the hand, only 20m speed test values improved after post-test period (p<0.05), but the other physical fitness tests values did not differ (p>0.05) between pre- and post- test measurement in the control group. The results of the study suggest that the core training program has positive effect on physical fitness characteristics in male volleyball players.

Keywords: body composition, core training, physical fitness, volleyball

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Authors: Matjaž Šraml, Marko Renčelj, Tomaž Tollazzi, Chiara Gruden

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Road safety is a worldwide issue with numerous and heterogeneous factors influencing it. On the side, driver state – comprising distraction/inattention, fatigue, drowsiness, extreme emotions, and socio-cultural factors highly affect road safety. On the other side, the vehicle state has an important role in mitigating (or not) the road risk. Finally, the road environment is still one of the main determinants of road safety, defining driving task complexity. At the same time, thanks to technological development, a lot of detailed data is easily available, creating opportunities for the detection of driver state, vehicle characteristics and road conditions and, consequently, for the design of ad hoc interventions aimed at improving driver performance, increase awareness and mitigate road risks. This is the challenge faced by the i-DREAMS project. i-DREAMS, which stands for a smart Driver and Road Environment Assessment and Monitoring System, is a 3-year project funded by the European Union’s Horizon 2020 research and innovation program. It aims to set up a platform to define, develop, test and validate a ‘Safety Tolerance Zone’ to prevent drivers from getting too close to the boundaries of unsafe operation by mitigating risks in real-time and after the trip. After the definition and development of the Safety Tolerance Zone concept and the concretization of the same in an Advanced driver-assistance system (ADAS) platform, the system was tested firstly for 2 months in a driving simulator environment in 5 different countries. After that, naturalistic driving studies started for a 10-month period (comprising a 1-month pilot study, 3-month baseline study and 6 months study implementing interventions). Currently, the project team has approved a common evaluation approach, and it is developing the assessment of the usage and outcomes of the i-DREAMS system, which is turning positive insights. The i-DREAMS consortium consists of 13 partners, 7 engineering universities and research groups, 4 industry partners and 2 partners (European Transport Safety Council - ETSC - and POLIS cities and regions for transport innovation) closely linked to transport safety stakeholders, covering 8 different countries altogether.

Keywords: advanced driver assistant systems, driving simulator, safety tolerance zone, traffic safety

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Authors: Naruemon Phongaksorn

Abstract:

The biofuel consumption has increased significantly over the decade resulting in the increasing request on agricultural land for biofuel feedstocks. However, the biofuel feedstocks are already stressed of having low productivity owing to inappropriate agricultural practices without considering suitability of crop land. This research evaluates the land suitability using GIS-integrated Analytic Hierarchy Processing (AHP) of biofuel crops: cassava, at Chachoengsao province, in Thailand. AHP method that has been widely accepted for land use planning. The objective of this study is compared between AHP method and the most limiting group of land characteristics method (classical approach). The reliable results of the land evaluation were tested against the crop performance assessed by the field investigation in 2015. In addition to the socio-economic land suitability, the expected availability of raw materials for biofuel production to meet the local biofuel demand, are also estimated. The results showed that the AHP could classify and map the physical land suitability with 10% higher overall accuracy than the classical approach. The Chachoengsao province showed high and moderate socio-economic land suitability for cassava. Conditions in the Chachoengsao province were also favorable for cassava plantation, as the expected raw material needed to support ethanol production matched that of ethanol plant capacity of this province. The GIS integrated AHP for biofuel crops land suitability evaluation appears to be a practical way of sustainably meeting biofuel production demand.

Keywords: Analytic Hierarchy Processing (AHP), Cassava, Geographic Information Systems, Land suitability

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Authors: H. Shokouhmand, A. Ghanami

Abstract:

Heat pipe is a simple heat transfer device which combines the conduction and phase change phenomena to control the heat transfer without any need for external power source. At hot surface of the heat pipe, the liquid phase absorbs heat and changes to vapor phase. The vapor phase flows to condenser region and with the loss of heat changes to liquid phase. Due to gravitational force, the liquid phase flows to evaporator section. In HVAC systems, the working fluid is chosen based on the operating temperature. The heat pipe has significant capability to reduce the humidity in HVAC systems. Each HVAC system which uses heater, humidifier or dryer is a suitable nominate for the utilization of heat pipes. Generally, heat pipes have three main sections: condenser, adiabatic region, and evaporator.Performance investigation and optimization of heat pipes operation in order to increase their efficiency is crucial. In the present article, a parametric study is performed to improve the heat pipe performance. Therefore, the heat capacity of the heat pipe with respect to geometrical and confining parameters is investigated. For the better observation of heat pipe operation in HVAC systems, a CFD simulation in Eulerian- Eulerian multiphase approach is also performed. The results show that heat pipe heat transfer capacity is higher for water as working fluid with the operating temperature of 340 K. It is also showed that the vertical orientation of heat pipe enhances its heat transfer capacity.

Keywords: heat pipe, HVAC system, grooved heat pipe, CFD simulation

Procedia PDF Downloads 497

Authors: Shryas Bhurat, Aryan Vashistha, Sampreet Dinakar Nayak, Ayush Gupta

Abstract:

As the technology is advancing more towards high computational resources, there is a paradigm shift in the usage of these resources to optimize the design process. This paper discusses the usage of ‘Generative Design using Artificial Intelligence’ to build better models that adapt the operations like selection, mutation, and crossover to generate results. The human mind thinks of the simplest approach while designing an object, but the intelligence learns from the past & designs the complex optimized CAD Models. Generative Design takes the boundary conditions and comes up with multiple solutions with iterations to come up with a sturdy design with the most optimal parameter that is given, saving huge amounts of time & resources. The new production techniques that are at our disposal allow us to use additive manufacturing, 3D printing, and other innovative manufacturing techniques to save resources and design artistically engineered CAD Models. Also, this paper discusses the Genetic Algorithm, the Non-Domination technique to choose the right results using biomimicry that has evolved for current habitation for millions of years. The computer uses parametric models to generate newer models using an iterative approach & uses cloud computing to store these iterative designs. The later part of the paper compares the topology optimization technology with Generative Design that is previously being used to generate CAD Models. Finally, this paper shows the performance of algorithms and how these algorithms help in designing resource-efficient models.

Keywords: genetic algorithm, bio mimicry, generative modeling, non-dominant techniques

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Authors: Shoukat Ali, Abdul Qadir Jandga, Amjad Hussain

Abstract:

Objective: The main purpose of this study is to compare the Percent Depth dose (PDD) and In-plane and cross-plane profiles of Varian Golden beam data to the measured data of 6 and 18 MV photons for the commissioning of Eclipse treatment planning system. Introduction: Commissioning of treatment planning system requires an extensive acquisition of beam data for the clinical use of linear accelerators. Accurate dose delivery require to enter the PDDs, Profiles and dose rate tables for open and wedges fields into treatment planning system, enabling to calculate the MUs and dose distribution. Varian offers a generic set of beam data as a reference data, however not recommend for clinical use. In this study, we compared the generic beam data with the measured beam data to evaluate the reliability of generic beam data to be used for the clinical purpose. Methods and Material: PDDs and Profiles of Open and Wedge fields for different field sizes and at different depths measured as per Varian’s algorithm commissioning guideline. The measurement performed with PTW 3D-scanning water phantom with semi-flex ion chamber and MEPHYSTO software. The online available Varian Golden Beam Data compared with the measured data to evaluate the accuracy of the golden beam data to be used for the commissioning of Eclipse treatment planning system. Results: The deviation between measured vs. golden beam data was in the range of 2% max. In PDDs, the deviation increases more in the deeper depths than the shallower depths. Similarly, profiles have the same trend of increasing deviation at large field sizes and increasing depths. Conclusion: Study shows that the percentage deviation between measured and golden beam data is within the acceptable tolerance and therefore can be used for the commissioning process; however, verification of small subset of acquired data with the golden beam data should be mandatory before clinical use.

Keywords: percent depth dose, flatness, symmetry, golden beam data

Procedia PDF Downloads 490

Authors: M. Heydari, A. Ghanami

Abstract:

Heat pipe is simple heat transfer device which combines the conduction and phase change phenomena to control the heat transfer without any need for external power source. At hot surface of heat pipe, the liquid phase absorbs heat and changes to vapor phase. The vapor phase flows to condenser region and with the loss of heat changes to liquid phase. Due to gravitational force the liquid phase flows to evaporator section.In HVAC systems the working fluid is chosen based on the operating temperature. The heat pipe has significant capability to reduce the humidity in HVAC systems. Each HVAC system which uses heater, humidifier or dryer is a suitable nominate for the utilization of heat pipes. Generally heat pipes have three main sections: condenser, adiabatic region and evaporator.Performance investigation and optimization of heat pipes operation in order to increase their efficiency is crucial. In present article, a parametric study is performed to improve the heat pipe performance. Therefore, the heat capacity of heat pipe with respect to geometrical and confining parameters is investigated. For the better observation of heat pipe operation in HVAC systems, a CFD simulation in Eulerian- Eulerian multiphase approach is also performed. The results show that heat pipe heat transfer capacity is higher for water as working fluid with the operating temperature of 340 K. It is also showed that the vertical orientation of heat pipe enhances it’s heat transfer capacity.

Keywords: heat pipe, HVAC system, grooved heat pipe, heat pipe limits

Procedia PDF Downloads 445

Authors: Yilin Liao, Hewen Li, Paula McConvey

Abstract:

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

Procedia PDF Downloads 110

Authors: Guillaume Craveur

Abstract:

This study shows the opportunity to use numerical tools dedicated to Fire Safety Engineering for the Rolling Stock. Indeed, some lawful requirements can now be demonstrated by using numerical tools. The first part of this study presents the use of modelling evacuation tool to satisfy the criteria of evacuation time for the rolling stock. The buildingEXODUS software is used to model and simulate the evacuation of rolling stock. Firstly, in order to demonstrate the reliability of this tool to calculate the complete evacuation time, a comparative study was achieved between a real test and simulations done with buildingEXODUS. Multiple simulations are performed to capture the stochastic variations in egress times. Then, a new study is done to calculate the complete evacuation time of a train with the same geometry but with a different interior architecture. The second part of this study shows some applications of Computational Fluid Dynamics. This work presents the approach of a multi scales validation of numerical simulations of standardized tests with Fire Dynamics Simulations software developed by the National Institute of Standards and Technology (NIST). This work highlights in first the cone calorimeter test, described in the standard ISO 5660, in order to characterize the fire reaction of materials. The aim of this process is to readjust measurement results from the cone calorimeter test in order to create a data set usable at the seat scale. In the second step, the modelisation concerns the fire seat test described in the standard EN 45545-2. The data set obtained thanks to the validation of the cone calorimeter test was set up in the fire seat test. To conclude with the third step, after controlled the data obtained for the seat from the cone calorimeter test, a larger scale simulation with a real part of train is achieved.

Keywords: fire safety engineering, numerical tools, rolling stock, multi-scales validation

Procedia PDF Downloads 303

Authors: Houda Najeh, Mahendra Pratap Singh, Stéphane Ploix, Antoine Caucheteux, Karim Chabir, Mohamed Naceur Abdelkrim

Abstract:

Building system is highly vulnerable to different kinds of faults and human misbehaviors. Energy efficiency and user comfort are directly targeted due to abnormalities in building operation. The available fault diagnosis tools and methodologies particularly rely on rules or pure model-based approaches. It is assumed that model or rule-based test could be applied to any situation without taking into account actual testing contexts. Contextual tests with validity domain could reduce a lot of the design of detection tests. The main objective of this paper is to consider fault validity when validate the test model considering the non-modeled events such as occupancy, weather conditions, door and window openings and the integration of the knowledge of the expert on the state of the system. The concept of heterogeneous tests is combined with test validity to generate fault diagnoses. A combination of rules, range and model-based tests known as heterogeneous tests are proposed to reduce the modeling complexity. Calculation of logical diagnoses coming from artificial intelligence provides a global explanation consistent with the test result. An application example shows the efficiency of the proposed technique: an office setting at Grenoble Institute of Technology.

Keywords: heterogeneous tests, validity, building system, sensor grids, sensor fault, diagnosis, fault detection and isolation

Procedia PDF Downloads 295

Authors: Madhuka G. P. D. Udantha, Gihan V. Dias, Surangika Ranathunga

Abstract:

Today Websites contain very interesting applications. But there are only few methodologies to analyze User navigations through the Websites and formulating if the Website is put to correct use. The web logs are only used if some major attack or malfunctioning occurs. Web Logs contain lot interesting dealings on users in the system. Analyzing web logs has become a challenge due to the huge log volume. Finding interesting patterns is not as easy as it is due to size, distribution and importance of minor details of each log. Web logs contain very important data of user and site which are not been put to good use. Retrieving interesting information from logs gives an idea of what the users need, group users according to their various needs and improve site to build an effective and efficient site. The model we built is able to detect attacks or malfunctioning of the system and anomaly detection. Logs will be more complex as volume of traffic and the size and complexity of web site grows. Unsupervised techniques are used in this solution which is fully automated. Expert knowledge is only used in validation. In our approach first clean and purify the logs to bring them to a common platform with a standard format and structure. After cleaning module web session builder is executed. It outputs two files, Web Sessions file and Indexed URLs file. The Indexed URLs file contains the list of URLs accessed and their indices. Web Sessions file lists down the indices of each web session. Then DBSCAN and EM Algorithms are used iteratively and recursively to get the best clustering results of the web sessions. Using homogeneity, completeness, V-measure, intra and inter cluster distance and silhouette coefficient as parameters these algorithms self-evaluate themselves to input better parametric values to run the algorithms. If a cluster is found to be too large then micro-clustering is used. Using Cluster Signature Module the clusters are annotated with a unique signature called finger-print. In this module each cluster is fed to Associative Rule Learning Module. If it outputs confidence and support as value 1 for an access sequence it would be a potential signature for the cluster. Then the access sequence occurrences are checked in other clusters. If it is found to be unique for the cluster considered then the cluster is annotated with the signature. These signatures are used in anomaly detection, prevent cyber attacks, real-time dashboards that visualize users, accessing web pages, predict actions of users and various other applications in Finance, University Websites, News and Media Websites etc.

Keywords: anomaly detection, clustering, pattern recognition, web sessions

Procedia PDF Downloads 288