Search results for: variable speed wind turbines

Authors: Ayesha Anjum, Arbaz Basha

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Active Galactic Nuclei (AGN) are the actively accreting centers of the galaxies that host supermassive black holes. AGN emits radiation in all wavelengths and also shows variability across all the wavelength bands. The analysis of flux variability tells us about the morphology of the site of emission radiation. Some of the major classifications of AGN are (a) Blazars, with featureless spectra. They are subclassified as BLLacertae objects, Flat Spectrum Radio Quasars (FSRQs), and others; (b) Seyferts with prominent emission line features are classified into Broad Line, Narrow Line Seyferts of Type 1 and Type 2 (c) quasars, and other types. Sloan Digital Sky Survey (SDSS) is an optical telescope based in Mexico that has observed and classified billions of objects based on automated photometric and spectroscopic methods. A sample of blazars is obtained from the third Fermi catalog. For variability analysis, we searched for light curves for these objects in Wide-Field Infrared Survey Explorer (WISE) and Near Earth Orbit WISE (NEOWISE) in two bands: W1 (3.4 microns) and W2 (4.6 microns), reducing the final sample to 256 objects. These objects are also classified into 155 BLLacs, 99 FSRQs, and 2 Narrow Line Seyferts, namely, PMNJ0948+0022 and PKS1502+036. Mid-infrared variability studies of these objects would be a contribution to the literature. With this as motivation, the present work is focused on studying a final sample of 256 objects in general and the Seyferts in particular. Owing to the fact that the classification is automated, SDSS has miclassified these objects into quasars, galaxies, and stars. Reasons for the misclassification are explained in this work. The variability analysis of these objects is done using the method of flux amplitude variability and excess variance. The sample consists of observations in both W1 and W2 bands. PMN J0948+0022 is observed between MJD from 57154.79 to 58810.57. PKS 1502+036 is observed between MJD from 57232.42 to 58517.11, which amounts to a period of over six years. The data is divided into different epochs spanning not more than 1.2 days. In all the epochs, the sources are found to be variable in both W1 and W2 bands. This confirms that the object is variable in mid-infrared wavebands in both long and short timescales. Also, the sources are observed for color variability. Objects either show a bluer when brighter trend (BWB) or a redder when brighter trend (RWB). The possible claim for the object to be BWB (present objects) is that the longer wavelength radiation emitted by the source can be suppressed by the high-energy radiation from the central source. Another result is that the smallest radius of the emission source is one day since the epoch span used in this work is one day. The mass of the black holes at the centers of these sources is found to be less than or equal to 108 solar masses, respectively.

Keywords: active galaxies, variability, Seyfert galaxies, SDSS, WISE

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Authors: Diego Abbo

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On February 15, 2012 off the Indian coast of Kerala, in position 091702N-0760180E by the oil tanker Enrica Lexie, flying the Italian flag, bursts of 5.56 x45 caliber shots were fired from assault rifles AR/70 Italian-made Beretta towards the Indian fisher boat St. Anthony. The shots that hit the St. Anthony fishing boat were six, of which two killed the Indian fishermen Ajesh Pink and Valentine Jelestine. From the analysis concerning the kinematic engagement of the two ships and from the autopsy and ballistic results of the Indian judicial authorities it is possible to reconstruct the trajectories of the six aforementioned shots. This essay reconstructs the trajectories of the six shots that cannot be of direct shooting but have undergone a rebound on the water. The investigation carried out scientifically demonstrates the rebound of the blows on the water, the gyrostatic deviation due to the rebound and the tumbling effect always due to the rebound as regards intermediate ballistics. In consideration of the four shots that directly impacted the fishing vessel, the current examination proves, with scientific value, that the trajectories could not be downwards but upwards. Also, the trajectory of two shots that hit to death the two fishermen could not be downwards but only upwards. In fact, this paper demonstrates, with scientific value: The loss of speed of the projectiles due to the rebound on the water; The tumbling effect in the ballistic medium within the two victims; The permanent cavities subject to the injury ballistics and the related ballistic trauma that prevented homeostasis causing bleeding in one case; The thermo-hardening deformation of the bullet found in Valentine Jelestine's skull; The upward and non-downward trajectories. The paper constitutes a tool in forensic ballistics in that it manages to reconstruct, from the final spot of the projectiles fired, all phases of ballistics like the internal one of the weapons that fired, the intermediate one, the terminal one and the penetrative structural one. In general terms the ballistics reconstruction is based on measurable parameters whose entity is contained with certainty within a lower and upper limit. Therefore, quantities that refer to angles, speed, impact energy and firing position of the shooter can be identified within the aforementioned limits. Finally, the investigation into the internal bullet track, obtained from any autopsy examination, offers a significant “lesson learned” but overall a starting point to contain or mitigate bleeding as a rescue from future gunshot wounds.

Keywords: impact physics, intermediate ballistics, terminal ballistics, tumbling effect

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Authors: Shael Brown, Reza Farivar

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Persistence diagrams capture valuable topological features of datasets that other methods cannot uncover. Still, their adoption in data pipelines has been limited due to the lack of publicly available tools in R (and python) for analyzing groups of them with machine learning and statistical inference. In an easy-to-use and scalable R package called TDApplied, we implement several applied analysis methods tailored to groups of persistence diagrams. The two main contributions of our package are comprehensiveness (most functions do not have implementations elsewhere) and speed (shown through benchmarking against other R packages). We demonstrate applications of the tools on simulated data to illustrate how easily practical analyses of any dataset can be enhanced with topological information.

Keywords: machine learning, persistence diagrams, R, statistical inference

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Authors: V. V. Singh

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We have studied the reliability measures of a system, which consists of two subsystems i.e. subsystem-1 and subsystem-2 in series configuration under different types of failure. The subsystem-1 has three identical units in parallel configuration and operating under 2-out-of-3: G policy and connected to subsystem-2 in series configuration. Each subsystem has different types of failure and repair rates. An important cause for failure of system is unsuitability of the environmental conditions, like overheating, weather conditions, heavy rainfall, storm etc. The environmental failure is taken into account in the proposed repairable system. Supplementary variable technique is used to study of system and some traditional measures such as; availability, reliability, MTTF and profit function are obtained for different values of parameters. In the proposed model, some particular cases of failure rates are explicitly studied.

Keywords: environmental failure, human failure, availability, MTTF, reliability, profit analysis, Gumbel-Hougaard family copula

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Authors: Sérgio S. de Jesus, Aline Santana, Rubens Maciel Filho

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Global oxygen transfer coefficient (kLa) was characterized in a mechanically agitated airlift bio reactor. The experiments were carried out in an airlift bio reactor (3.2 L) with internal re circulation (a concentric draft-tube airlift vessel device); the agitation is carried out through a turbine Rushton impeller located along with the gas sparger in the region comprised in the riser. The experiments were conducted using xanthan gum (0.6%) at 250 C and a constant rotation velocity of 0 and 800 rpm, as well as in the absence of agitation (airlift mode); the superficial gas velocity varied from 0.0157 to 0.0262 ms-1. The volumetric oxygen transfer coefficient dependence of the rotational speed revealed that the presence of agitation increased up to two times the kLa value.

Keywords: aeration, mass transfer, non-Newtonian fluids, stirred airlift bioreactor

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Authors: Zahra Ranjbar

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Interpretive structural modeling (ISM) is a well-established methodology for identifying relationships among specific variables, which define a problem or an issue. In this study most important variables that have critical role in children obesity problem were introduce by ISM questionnaire technique and their relationships were determine. Our findings suggested that sedentary activities are top level variables and school teachers and administrators, public education and scientific collaborations are bottom level variables in children obesity problem. Control of dietary, Physical education program, parents, government and motivation strategies variables are depend to other variables. They are very sensitive to external variables. Also, physical education program, parents, government, motivation, school teachers and administrators, public education and collaboration variables have strong driving power. They are linkage factors; it means that they can be effective on children obesity problem directly.

Keywords: ISM, variable, obesity, physical education, children

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Authors: N. Gayathri Devi, K. Batri

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The advent of technology has increased the traffic hazards and the road accidents take place. Collision detection system in automobile aims at reducing or mitigating the severity of an accident. This project aims at avoiding Vehicle head on collision by means of collision detection algorithm. This collision detection algorithm predicts the collision and the avoidance or minimization have to be done within few seconds on confirmation. Under critical situation collision minimization is made possible by turning the vehicle to the desired turn radius so that collision impact can be reduced. In order to avoid the collision completely, the turning of the vehicle should be achieved at reduced speed in order to maintain the stability.

Keywords: collision avoidance system, time to collision, time to turn, turn radius

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Authors: Malika Yaici, Kamel Hariche, Tim Clarke

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The relationship between eigenstructure (eigenvalues and eigenvectors) and latent structure (latent roots and latent vectors) is established. In control theory eigenstructure is associated with the state space description of a dynamic multi-variable system and a latent structure is associated with its matrix fraction description. Beginning with block controller and block observer state space forms and moving on to any general state space form, we develop the identities that relate eigenvectors and latent vectors in either direction. Numerical examples illustrate this result. A brief discussion of the potential of these identities in linear control system design follows. Additionally, we present a consequent result: a quick and easy method to solve the polynomial eigenvalue problem for regular matrix polynomials.

Keywords: eigenvalues/eigenvectors, latent values/vectors, matrix fraction description, state space description

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Authors: Mohamed Raouf Benmakrelouf, Wafa Karouche, Joseph Rynkiewicz

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In this paper, we propose an alternative method to construct a Bayesian Network (BN). This method relies on a convolutional neural network (CNN classifier), which determinates the edges of the network skeleton. We train a CNN on a normalized empirical probability density distribution (NEPDF) for predicting causal interactions and relationships. We have to find the optimal Bayesian network structure for causal inference. Indeed, we are undertaking a search for pair-wise causality, depending on considered causal assumptions. In order to avoid unreasonable causal structure, we consider a blacklist and a whitelist of causality senses. We tested the method on real data to assess the influence of education on the voting intention for the extreme right-wing party. We show that, with this method, we get a safer causal structure of variables (Bayesian Network) and make to identify a variable that satisfies the backdoor criterion.

Keywords: Bayesian network, structure learning, optimal search, convolutional neural network, causal inference

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Authors: Anjali Malik

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This paper presents a review of the literature on behavioral addictions with a particular focus on understanding online gaming habits among adolescents. Extant researches yielded many different sets of antecedent factors for developing pathological online gaming behavior. This paper draws findings from the most-cited publications most closely associated with factors explaining why individuals develop such kind of problematic behavior. What emerges as central to understanding this phenomenon is the presence of multiple variable causes that take into account the individual, the environment and their interaction to explain the risk behavior such as pathological online gaming. In addition to that role of some mediating factors and pull factors has also been discussed, along with the consequences on personal, social and academic performance resulting from such kind of addictive behavior. The paper also makes recommendations for future research including developing a deeper understanding of the phenomena studied here by examining the relative contribution of these multiple-risk contexts.

Keywords: pathological gaming, gaming addiction, adolescents, behavior

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Authors: Mohamed El Morsy, Gabriela Achtenová

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Condition monitoring is used to increase machinery availability and machinery performance, whilst reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient condition monitoring system provides early warning of faults by predicting them at an early stage. When a localized fault occurs in gears, the vibration signals always exhibit non-stationary behavior. The periodic impulsive feature of the vibration signal appears in the time domain and the corresponding gear mesh frequency (GMF) emerges in the frequency domain. However, one limitation of frequency-domain analysis is its inability to handle non-stationary waveform signals, which are very common when machinery faults occur. Particularly at the early stage of gear failure, the GMF contains very little energy and is often overwhelmed by noise and higher-level macro-structural vibrations. An effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are selected or optimized based on maximum Kurtosis. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers induce a load on the output joint shaft flanges. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. The gearbox used for experimental measurements is of the type most commonly used in modern small to mid-sized passenger cars with transversely mounted powertrain and front wheel drive: a five-speed gearbox with final drive gear and front wheel differential. The results obtained from practical experiments prove that the proposed method is very effective for gear fault diagnosis.

Keywords: wavelet analysis, pitted gear, autocorrelation, gear fault diagnosis

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Authors: M. Stefanovska, B. Samakoski, S. Risteska, G. Maneski

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In this study was performed in situ consolidation of polypropylene matrix/glass reinforced roving by combining heating systems and roll pressing. The commingled roving during hoop winding was winded on a cylindrical mandrel. The work also presents the advances made in the processing of these materials into composites by conventional technique filament winding. Experimental studies were performed with changing parameters – temperature, pressure and speed. Finally, it describes the investigation of the optimal processing conditions that maximize the mechanical properties of the composites. These properties are good enough for composites to be used as engineering materials in many structural applications.

Keywords: commingled fiber, consolidation heat, filament winding, voids

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Authors: Baziana Peristera

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The idea of the asynchronous transmission in wavelength division multiplexing (WDM) ring MANs is studied in this paper. Especially, we present an efficient access technique to coordinate the collisions-free transmission of the variable sizes of IP traffic in WDM ring core networks. Each node is equipped with a tunable transmitter and a tunable receiver. In this way, all the wavelengths are exploited for both transmission and reception. In order to evaluate the performance measures of average throughput, queuing delay and packet dropping probability at the buffers, a simulation model that assumes symmetric access rights among the nodes is developed based on Poisson statistics. Extensive numerical results show that the proposed protocol achieves apart from high bandwidth exploitation for a wide range of offered load, fairness of queuing delay and dropping events among the different packets size categories.

Keywords: asynchronous transmission, collision avoidance, wavelength division multiplexing, WDM

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Authors: Vinko Vidučić, Ljiljana Vidučić, Damir Boras

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The goals of the research include the determination of the characteristics of SMEs finance in Croatia, as well as the determination of indirect growth rates of the information model of the entrepreneurs` perception of business environment. The research results show that cost of finance and access to finance are most important constraining factor in setting up and running the business of small entrepreneurs in Croatia. Furthermore, small entrepreneurs in Croatia are significantly dissatisfied with the administrative barriers although relatively to a lesser extent than was the case in the pre-crisis time. High collateral requirement represents the main characteristic of bank lending concerning SMEs followed by long credit elaboration process. Formulated information model has defined the individual impact of indirect growth rates of the remaining variables on the model’s specific variable.

Keywords: business environment, information model, indirect growth rates, SME finance

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Authors: Sina Aghili, Ali Arasteh Nodeh

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Tamarisk usage as a new group of lignocelluloses material to produce fermentable sugars in bio-ethanol process was studied. The overall aim of this work was to establish the optimum condition for acid hydrolysis of this new material and a mathematical model predicting glucose release as a function of operation variable. Sulfuric acid concentration in the range of 20 to 60%(w/w), process temperature between 60 to 95oC, hydrolysis time from 120 to 240 min and solid content 5,10,15%(w/w) were used as hydrolysis conditions. HPLC was used to analysis of the product. This analysis indicated that glucose was the main fermentable sugar and was increased with time, temperature and solid content and acid concentration was a parabola influence in glucose production.The process was modeled by a quadratic equation. Curve study and model were found that 42% acid concentration, 15 % solid content and 90oC were in optimum condition.

Keywords: fermentable sugar, saccharification, wood, hydrolysis

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Authors: Kathy Mwende Kiema, Remember Samu, Murat Fahrioglu

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Despite the implementation of a Feed in Tariff (FiT) for solar power plants in Kenya, the uptake and subsequent development of utility scale power plants has been slow. This paper, therefore, proposes a Concentrating Solar Power (CSP) plant configuration that can supply both power to the grid and operate a sea water desalination plant, thus providing an economically viable alternative to Independent Power Producers (IPPs). The largest city on the coast, Mombasa, has a chronic water shortage and authorities are looking to employ desalination plants to supply a deficit of up to 100 million cubic meters of fresh water per day. In this study the desalination plant technology was selected based on an analysis of operational costs in $/m3 of plants that are already running. The output of the proposed CSP plant, Net Present Value (NPV), plant capacity factor, thermal efficiency and quantity of CO2 emission avoided were simulated using Greenius software (Green energy system analysis tool) developed by the institute of solar research at the German Aerospace Center (DLR). Data on solar irradiance were derived from the Solar and Wind Energy Resource Assessment (SWERA) for Kenya.

Keywords: desalination, feed in tariff, independent power producer, solar CSP

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Authors: Chen Jun

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This study investigates the impact of family involvement, a pivotal factor shaping the management structure of family firms, on the firm’s innovation outputs. The independent variable focuses on the percentage number of family members serving as directors, supervisors and senior management. Our hypothesis suggests that family involvement tends to make management more conservative, thereby increasing the likelihood of impeding innovation investments and resulting in adverse effects on innovation output. Our findings reveal that Chinese family firms with high family involvement exhibit poorer innovation outputs compared to those with lower family involvement. Subsample analyses indicate that this negative influence of family involvement on innovation output is strengthened as the firm faces higher industry competition and a low marketization context. The findings of our paper contribute to the literature on family involvement by empirically illustrating how family involvement hinders innovation efforts and performance in Chinese family firms.

Keywords: family firm, family involvement, firm innovation, Chinese family firm

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Authors: Malika Yaici, Soraya Aloui, Sara Semchaoui

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This paper deals with human activity recognition from sensor data. It is an active research area, and the main objective is to obtain a high recognition rate. In this work, a recognition system based on expert systems is proposed; the recognition is performed using the objects, object states, and gestures and taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions and the activity). The system recognizes complex activities after decomposing them into simple, easy-to-recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision.

Keywords: human activity recognition, ubiquitous computing, context-awareness, expert system

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Authors: Saba Harati, Nasrin Arian Parsa

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The current research was an attempt to investigate the effect of resilience on mental health and life satisfaction. In one Cross Sectional research, 287 (173 females and 114 males) students of Tehran University were participated their average age was 23.17 years old (SD=4.9). The instruments used for assessing the research variables included: Cutter and Davidson resilience scale (CD-RISC), the short form of the depression-anxiety-stress scale, and life satisfaction scale. The data analysis was done in the form of structural equation model. The results of Simultaneous Hierarchical Multiple Regression Analysis indicated that there was a significant mediating role of the negative emotions (depression, anxiety, and stress), in the relationship between the family resilience (p < 0.001) and satisfaction with life (p < 0.001). Resilience results in life satisfaction by reducing the emotional problems (or increasing the mental health level). The effect of the resilience variable on life satisfaction was indirect.

Keywords: resilience, negative emotion, mental health, life satisfaction

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Authors: Biswarup Mukherjee, Aniruddha Ghosal

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In this paper, we propose a new technique for implementing a low power full adder using a set of GDI multiplexers. Full adder circuits are used comprehensively in Application Specific Integrated Circuits (ASICs). Thus it is desirable to have low power operation for the sub components. The explored method of implementation achieves a low power design for the full adder. Simulated results using state-of-art Tanner tool indicates the superior performance of the proposed technique over conventional CMOS full adder. Detailed comparison of simulated results for the conventional and present method of implementation is presented.

Keywords: low power full adder, 2-T GDI MUX, ASIC (application specific integrated circuit), 12-T FA, CMOS (complementary metal oxide semiconductor)

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Authors: András Rövid, Takeshi Hashimoto

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The paper focuses on the problem of the point correspondence matching in stereo images. The proposed matching algorithm is based on the combination of simpler methods such as normalized sum of squared differences (NSSD) and a more complex phase correlation based approach, by considering the noise and other factors, as well. The speed of NSSD and the preciseness of the phase correlation together yield an efficient approach to find the best candidate point with sub-pixel accuracy in stereo image pairs. The task of the NSSD in this case is to approach the candidate pixel roughly. Afterwards the location of the candidate is refined by an enhanced phase correlation based method which in contrast to the NSSD has to run only once for each selected pixel.

Keywords: stereo matching, sub-pixel accuracy, phase correlation, SVD, NSSD

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Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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Authors: Falek Kamel

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Free vibration analysis of beams, based on the assumptions of Bernoulli-Euler theory, has been extensively studied. Many research works have focused on the study of transverse vibrations under the application of different boundary conditions where different theories have been applied. The stiffness and mass matrices considered are those obtained by assembling those resulting from the use of the finite element method. The Jacobi method has been used to solve the eigenvalue problem. These well-known concepts have been applied to the study of beams with constant geometric and mechanical characteristics having one to two overhangs with variable lengths. Murphy studied, by an algebraic solution approach, a simply supported beam with two overhangs of arbitrary length, allowing for an experimental determination of the elastic modulus E. The advantage of our article is that it offers the possibility of extending this approach to many interesting problems formed by transversely vibrating beams with various end constraints.

Keywords: beam, finite element, transverse vibrations, end restreint, Bernoulli-Euler theory

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Authors: Fawzia Al-Sakeer, Hassan Aldeeb

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Development in the communications field is proceeding at an amazing speed, which has led researchers to improve and develop electronic circuits by increasing their efficiency and reducing their size to reduce the weight of electronic devices. One of the most important of these circuits is the band-pass filter, which is what made us carry out this research, which aims to use an alternate technology to design a dual band-pass filter by using a stepped impedance microstrip transmission line. We designed a filter that works at two center frequency bands by designing with the ADS program, and the results were excellent, as we obtained the two design frequencies, which are 1 and 3GHz, and the values of insertion loss S11, which was more than 21dB with a small area.

Keywords: band pass filter, dual band band-pass filter, ADS, microstrip filter, stepped impedance

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Authors: Divya Sisodiya, Deepika Sipal

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Optical wireless communication (OWC) is a relatively new technology in optical communication systems that allows for high-speed wireless optical communication. This research focuses on developing a cost-effective OWC system using a hybrid configuration of optical amplifiers. In addition to using EDFA amplifiers, a comparison study was conducted to determine which modulation technique is more effective for communication. This research examines the performance of an OWC system based on ASK and PSK modulation techniques by varying OWC parameters under various atmospheric conditions such as rain, mist, haze, and snow. Finally, the simulation results are discussed and analyzed.

Keywords: OWC, bit error rate, amplitude shift keying, phase shift keying, attenuation, amplifiers

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Authors: Ofosuhene O. Apenteng, Noor Azina Ismail

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Recently tourism is a major foreign exchange earner in the World. In this paper, we propose the mathematical model to study the impact of tourists on the spread of HIV incidences using compartmental differential equation models. Simulation studies of reproduction number are used to demonstrate new insights on the spread of HIV disease. The periodogram analysis of a time series was used to determine the speed at which the disease is spread. The results indicate that with the persistent flow of tourism into a country, the disease status has increased the epidemic rate. The result suggests that the government must put more control on illegal prostitution, unprotected sexual activity as well as to emphasis on prevention policies that include the safe sexual activity through the campaign by the tourism board.

Keywords: HIV/AIDS, mathematical transmission modeling, tourists, stability, simulation

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Authors: Fernanda Pinheiro Martins, Pedro Teixeira Lacava

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The objective of this research is to analyze the effects of different spark plug conditions in engine-like conditions by applying computational fluid dynamics analysis. The 3D models applied consist of 3-Zones Extended Coherent Flame (ECFM-3Z) and Imposed Stretch Spark Ignition Model (ISSIM), respectively, for the combustion and the spark plug modelling. For this study, it was applied direct injection fuel system in a single cylinder engine operating with E0. The application of realistic operating conditions (load and speed) to the different cases studied will provide a deeper understanding of the effects of the spark plug gap, a result of parts outwearing in most of the cases, to the development of the combustion in engine-like conditions.

Keywords: engine, CFD, direct injection, combustion, spark plug

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Authors: Zhijie Li, Wenhui Peng, Zelong Yuan, Jianchun Wang

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Turbulence is a complex phenomenon that plays a crucial role in various fields, such as engineering, atmospheric science, and fluid dynamics. Predicting and understanding its behavior over long time scales have been challenging tasks. Traditional methods, such as large-eddy simulation (LES), have provided valuable insights but are computationally expensive. In the past few years, machine learning methods have experienced rapid development, leading to significant improvements in computational speed. However, ensuring stable and accurate long-term predictions remains a challenging task for these methods. In this study, we introduce the implicit U-net enhanced Fourier neural operator (IU-FNO) as a solution for stable and efficient long-term predictions of the nonlinear dynamics in three-dimensional (3D) turbulence. The IU-FNO model combines implicit re-current Fourier layers to deepen the network and incorporates the U-Net architecture to accurately capture small-scale flow structures. We evaluate the performance of the IU-FNO model through extensive large-eddy simulations of three types of 3D turbulence: forced homogeneous isotropic turbulence (HIT), temporally evolving turbulent mixing layer, and decaying homogeneous isotropic turbulence. The results demonstrate that the IU-FNO model outperforms other FNO-based models, including vanilla FNO, implicit FNO (IFNO), and U-net enhanced FNO (U-FNO), as well as the dynamic Smagorinsky model (DSM), in predicting various turbulence statistics. Specifically, the IU-FNO model exhibits improved accuracy in predicting the velocity spectrum, probability density functions (PDFs) of vorticity and velocity increments, and instantaneous spatial structures of the flow field. Furthermore, the IU-FNO model addresses the stability issues encountered in long-term predictions, which were limitations of previous FNO models. In addition to its superior performance, the IU-FNO model offers faster computational speed compared to traditional large-eddy simulations using the DSM model. It also demonstrates generalization capabilities to higher Taylor-Reynolds numbers and unseen flow regimes, such as decaying turbulence. Overall, the IU-FNO model presents a promising approach for long-term dynamics prediction in 3D turbulence, providing improved accuracy, stability, and computational efficiency compared to existing methods.

Keywords: data-driven, Fourier neural operator, large eddy simulation, fluid dynamics

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Authors: Basma Khelfa, Antoine Tordeux, Ibrahima Ba

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We present a descriptive analysis data of lane-changing events in multi-lane roads. The data are provided from The Highway Drone Dataset (HighD), which are microscopic trajectories in highway. This paper describes and analyses the role of the different parameters and their significance. Thanks to HighD data, we aim to find the most frequent reasons that motivate drivers to change lanes. We used the programming language R for the processing of these data. We analyze the involvement and relationship of different variables of each parameter of the ego vehicle and the four vehicles surrounding it, i.e., distance, speed difference, time gap, and acceleration. This was studied according to the class of the vehicle (car or truck), and according to the maneuver it undertook (overtaking or falling back).

Keywords: autonomous driving, physical traffic model, prediction model, statistical learning process

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Authors: Siddharth Ojha, Varun Dua

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Classified as one of the most important reasons of aerodynamic drag in the sedan automobiles is the fluid flow separation near the vehicle’s rear end. To retard the separation of flow, bump-shaped vortex generators are being tested for its implementation to the roof end of a sedan vehicle. Frequently used in the aircrafts to prevent the separation of fluid flow, vortex generators themselves produce drag, but they also substantially reduce drag by preventing flow separation at the downstream. The net effects of vortex generators can be calculated by summing the positive and negative impacts and effects. Since this effect depends on dimensions and geometry of vortex generators, those present on the vehicle roof are optimized for maximum efficiency and performance. The model was tested through ANSYS CFD analysis and modeling. The model was tested in the wind tunnel for observing it’s properties such as aerodynamic drag and flow separation and a major time lag was gained by employing vortex generators in the scaled model. Major conclusions which were recorded during the analysis were a substantial 24% reduction in the aerodynamic drag and 14% increase in the efficiency of the sedan automobile as the flow separation from the surface is delayed. This paper presents the results of optimization, the effect of vortex generators in the flow field and the mechanism by which these effects occur and are regulated.

Keywords: aerodynamics, aerodynamic devices, body, computational fluid dynamics (CFD), flow visualization

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