Search results for: rearing parameters optimization

Authors: Tijjani Bashir Musa

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The recent global crisis which suddenly results to Nigerian stock market crash revealed some peculiarities of Nigerian firms. Some firms in Nigeria are performing but their stock prices are not increasing while some firms are at the brink of collapse but their stock prices are increasing. Thus, this study examines the relationship between firm performance and stock price in Nigeria. The study covered the period of 2005 to 2009. This period is the period of stock boom and also marked the period of stock market crash as a result of global financial meltdown. The study is a panel study. A total of 140 firms were sampled from 216 firms listed on the Nigerian Stock Exchange (NSE). Data were collected from secondary source. These data were divided into four strata comprising the most performing stock, the least performing stock, most performing firms and the least performing firms. Each stratum contains 35 firms with characteristic of most performing stock, most performing firms, least performing stock and least performing firms. Multiple linear regression models were used to analyse the data while statistical/econometrics package of Stata 11.0 version was used to run the data. The study found that, relationship exists between selected firm performance parameters (operating efficiency, firm profit, earning per share and working capital) and stock price. As such firm performance gave sufficient information or has predictive power on stock prices movements in Nigeria for all the years under study.. The study recommends among others that Managers of firms in Nigeria should formulate policies and exert effort geared towards improving firm performance that will enhance stock prices movements.

Keywords: firm, Nigeria, performance, stock price

Procedia PDF Downloads 477

Authors: E. G. Karvelas, C. Liosis, A. Theodorakakos, T. E. Karakasidis

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Magnetic navigation of the drug inside the human vessels is a very important concept since the drug is delivered to the desired area. Consequently, the quantity of the drug required to reach therapeutic levels is being reduced while the drug concentration at targeted sites is increased. Magnetic navigation of drug agents can be achieved with the use of magnetic nanoparticles where anti-tumor agents are loaded on the surface of the nanoparticles. The magnetic field that is required to navigate the particles inside the human arteries is produced by a magnetic resonance imaging (MRI) device. The main factors which influence the efficiency of the usage of magnetic nanoparticles for biomedical applications in magnetic driving are the size and the magnetization of the biocompatible nanoparticles. In this study, a computational platform for the simulation of the optimal gradient magnetic fields for the navigation of magnetic nanoparticles inside a carotid artery is presented. For the propulsion model of the particles, seven major forces are considered, i.e., the magnetic force from MRIs main magnet static field as well as the magnetic field gradient force from the special propulsion gradient coils. The static field is responsible for the aggregation of nanoparticles, while the magnetic gradient contributes to the navigation of the agglomerates that are formed. Moreover, the contact forces among the aggregated nanoparticles and the wall and the Stokes drag force for each particle are considered, while only spherical particles are used in this study. In addition, gravitational forces due to gravity and the force due to buoyancy are included. Finally, Van der Walls force and Brownian motion are taken into account in the simulation. The OpenFoam platform is used for the calculation of the flow field and the uncoupled equations of particles' motion. To verify the optimal gradient magnetic fields, a covariance matrix adaptation evolution strategy (CMAES) is used in order to navigate the particles into the desired area. A desired trajectory is inserted into the computational geometry, which the particles are going to be navigated in. Initially, the CMAES optimization strategy provides the OpenFOAM program with random values of the gradient magnetic field. At the end of each simulation, the computational platform evaluates the distance between the particles and the desired trajectory. The present model can simulate the motion of particles when they are navigated by the magnetic field that is produced by the MRI device. Under the influence of fluid flow, the model investigates the effect of different gradient magnetic fields in order to minimize the distance of particles from the desired trajectory. In addition, the platform can navigate the particles into the desired trajectory with an efficiency between 80-90%. On the other hand, a small number of particles are stuck to the walls and remains there for the rest of the simulation.

Keywords: artery, drug, nanoparticles, navigation

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Authors: Tania Bose, Minto Rattan, Neeraj Chamoli

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In this paper, an attempt has been made to study the effect of thermal gradation on the steady-state creep behavior of rotating isotropic disc made of functionally graded material using threshold stress based Sherby’s creep law. The composite discs made of aluminum matrix reinforced with silicon carbide particulate have been taken for analysis. The stress and strain rate distributions have been calculated for the discs rotating at elevated temperatures having thermal gradation. The material parameters of creep vary radially and have been estimated by regression fit of the available experimental data. Investigations for discs made up of linearly increasing particle content operating under linearly decreasing temperature from inner to outer radii have been done using von Mises’ yield criterion. The results are displayed and compared graphically in designer friendly format for the above said disc profile with the disc made of particle reinforced composite operating under uniform temperature profile. It is observed that radial and tangential stresses show minor variation and the strain rates vary significantly in the presence of thermal gradation as compared to disc having uniform temperature.

Keywords: creep, isotropic, steady-state, thermal gradation

Procedia PDF Downloads 233

Authors: Nelson Bii, Christopher Ouma, John Odhiambo

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Non-response is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random non-response using auxiliary data. In this study, it is assumed that random non-response occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random non-response. In particular, the auxiliary information is used via an improved Nadaraya-Watson kernel regression technique to compensate for random non-response. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at a 95% coverage rate. The results obtained in this study are useful, for instance, in choosing efficient estimators of the finite population mean in demographic sample surveys.

Keywords: mean squared error, random non-response, two-stage cluster sampling, confidence interval lengths

Procedia PDF Downloads 139

Authors: A. Asgharzadeh, M. Maroufi

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5G mobile communication system has drawn more and more attention. The 5G system needs to provide three different types of services, including enhanced Mobile BroadBand (eMBB), massive machine-type communication (mMTC), and ultra-reliable and low-latency communication (URLLC). Universal Filtered Multi-Carrier (UFMC), Filter Bank Multicarrier (FBMC), and Filtered Orthogonal Frequency Division Multiplexing (f-OFDM) are suggested as a well-known candidate waveform for the coming 5G system. Themachine-to-machine (M2M) communications are one of the essential applications in 5G, and it involves exchanging of concise messages with a very short latency. However, in UFMC systems, the subcarriers are grouped into subbands but f-OFDM only one subband covers the entire band. Furthermore, in FBMC, a subband includes only one subcarrier, and the number of subbands is the same as the number of subcarriers. This paper mainly discusses the performance of UFMC with different parameters for the UFMC system. Also, paper shows that UFMC is the best choice outperforming OFDM in any case and FBMC in case of very short packets while performing similarly for long sequences with channel estimation techniques for Interleave Division Multiple Access (IDMA) systems.

Keywords: universal filtered multi-carrier technique, UFMC, interleave division multiple access, IDMA, fifth-generation, subband

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Authors: Nadia A. Mohamed, Mehrevan M. Abdel-Moniem

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Zingiber officinale (ginger) has been cultivated for medicinal purposes due to their various proprieties both in vitro and in vivo, so we designed to evaluate the ginger’s potential effect on nephrin m RNA expression in cisplatin-induced nephrotoxic rats. Method: Forty male albino rats were divided into group I was injected (IP) with one ml saline, group II(cisplatin) injected (IP) with a single dose of 12 mg/kg cisplatin, group III (ginger) received (PO) 310 mg/kg for 30 successive days, and group IV(cisplatin and ginger) rats received ginger extract (310 mg/kg) daily for 20 successive days (PO), and then on day 20 of ginger extract administration each rat was injected(IP) with a single dose of 12 mg/kg cisplatin. The blood was sampled to assess urea, creatinine (SC), while the levels of malondialdehyde (MDA), nitric oxide (NO) and paraoxonase (PON1) were measured in kidney tissue homogenate. Expression of urinary nephrin gene (nephrin mRNA) was detected using qRT-PCR. Results: Treatment with ginger significantly decreased the levels of kidney function parameters as well as MDA and NO elevated by cisplatin injection, while PON1 was significantly reduced in the cisplatin group. However, the protection of male rats with ginger significantly increased the levels of nephrin gene expression and PON1 compared with the cisplatin-treated group. Our results generated a proposal on the ameliorating effect of ginger on nephrin mRNA gene expression reduction in cisplatin-induced nephrotoxicity.

Keywords: nephrin mRNA, ginger, cisplatin, nephrotoxicity

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Authors: Akalu Banbeta, Emmanuel Lesaffre, Reynaldo Martina, Joost Van Rosmalen

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Including data from previous studies (historical data) in the analysis of the current study may reduce the sample size requirement and/or increase the power of analysis. The most common example is incorporating historical control data in the analysis of a current clinical trial. However, this only applies when the historical control dataare similar enough to the current control data. Recently, several Bayesian approaches for incorporating historical data have been proposed, such as the meta-analytic-predictive (MAP) prior and the modified power prior (MPP) both for single control as well as for multiple historical control arms. Here, we examine the performance of the MAP and the MPP approaches for the analysis of (over-dispersed) count data. To this end, we propose a computational method for the MPP approach for the Poisson and the negative binomial models. We conducted an extensive simulation study to assess the performance of Bayesian approaches. Additionally, we illustrate our approaches on an overactive bladder data set. For similar data across the control arms, the MPP approach outperformed the MAP approach with respect to thestatistical power. When the means across the control arms are different, the MPP yielded a slightly inflated type I error (TIE) rate, whereas the MAP did not. In contrast, when the dispersion parameters are different, the MAP gave an inflated TIE rate, whereas the MPP did not.We conclude that the MPP approach is more promising than the MAP approach for incorporating historical count data.

Keywords: count data, meta-analytic prior, negative binomial, poisson

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Authors: Sabiha S. Marine, Nicole Flint, John Rolfe

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The Queensland state government introduced commercial gill net fishing closures in Cairns, Mackay, and Rockhampton in November 2015 to increase the recreational fishing opportunities, nature-based tourism, and economic benefits in these three regional areas. This management change is likely to improve the potential for more desirable stock structures through natural recruitment. Barramundi (Lates calcarifer) is one of the popular target fish for recreational and commercial fishers in Northern Australia. This investigation examines the effects of reduced commercial fishing from both biological and economic perspectives, particularly on the local Barramundi population of the Fitzroy River in Rockhampton, the largest river catchment flowing to the eastern coast of Australia. Data on different parameters of biological and economic aspects have been collated from secondary sources for analysis through a system simulation approach to identify the effectiveness of the commercial netting closures on recreational fishing effort, especially for the Barramundi population. The results have the potential to explain certain consequences of the netting closures in Queensland, which could serve to inform future fisheries management decisions. The study output as a whole will help in the better management of fisheries resources by evaluating recreational fishing opportunities in Queensland, where the potential for increases in recreation is high.

Keywords: Barramundi, bioeconomic model, fishery management, recreational fishing

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Authors: Mohammad Arabi

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The development of advanced technologies in the field of signal processing and vibration analysis has enabled more accurate analysis and fault detection in electrical systems. This research investigates the application of temporal and frequency features in detecting faults in electric motor bearings, aiming to enhance fault detection accuracy and prevent unexpected failures. The use of methods such as deep learning algorithms and neural networks in this process can yield better results. The main objective of this research is to evaluate the efficiency and accuracy of methods based on temporal and frequency features in identifying faults in electric motor bearings to prevent sudden breakdowns and operational issues. Additionally, the feasibility of using techniques such as machine learning and optimization algorithms to improve the fault detection process is also considered. This research employed an experimental method and random sampling. Vibration signals were collected from electric motors under normal and faulty conditions. After standardizing the data, temporal and frequency features were extracted. These features were then analyzed using statistical methods such as analysis of variance (ANOVA) and t-tests, as well as machine learning algorithms like artificial neural networks and support vector machines (SVM). The results showed that using temporal and frequency features significantly improves the accuracy of fault detection in electric motor bearings. ANOVA indicated significant differences between normal and faulty signals. Additionally, t-tests confirmed statistically significant differences between the features extracted from normal and faulty signals. Machine learning algorithms such as neural networks and SVM also significantly increased detection accuracy, demonstrating high effectiveness in timely and accurate fault detection. This study demonstrates that using temporal and frequency features combined with machine learning algorithms can serve as an effective tool for detecting faults in electric motor bearings. This approach not only enhances fault detection accuracy but also simplifies and streamlines the detection process. However, challenges such as data standardization and the cost of implementing advanced monitoring systems must also be considered. Utilizing temporal and frequency features in fault detection of electric motor bearings, along with advanced machine learning methods, offers an effective solution for preventing failures and ensuring the operational health of electric motors. Given the promising results of this research, it is recommended that this technology be more widely adopted in industrial maintenance processes.

Keywords: electric motor, fault detection, frequency features, temporal features

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Authors: Angelina Chiglintseva, Vladislav Shagapov

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We know from the geological data that quite sufficient gas reserves are concentrated in hydrates that occur on the Earth and on the ocean floor. Therefore, the development of these sources of energy and the storage of large reserves of gas hydrates is an acute global problem. An advanced technology for utilizing gas is to store it in a gas-hydrate state. Under natural conditions, storage facilities can be established, e.g., in underground reservoirs, where quite large volumes of gas can be conserved compared with reservoirs of pure gas. An analysis of the available experimental data of the kinetics and the mechanism of the gas-hydrate formation process shows the self-conservation effect that allows gas to be stored at negative temperatures and low values of pressures of up to several atmospheres. A theoretical model has been constructed for the gas-hydrate reservoir that represents a unique natural chemical reactor, and the principal possibility of the full extraction of gas from a hydrate due to the thermal reserves of the reservoirs themselves and the surrounding rocks has been analyzed. The influence exerted on the evolution of a gas hydrate reservoir by the reservoir thicknesses and the parameters that determine its initial state (a temperature, pressure, hydrate saturation) has been studied. It has been established that the shortest time of exploitation required by the reservoirs with a thickness of a few meters for the total hydrate decomposition is recorded in the cyclic regime when gas extraction alternated with the subsequent conservation of the gas hydrate deposit. The study was performed by a grant from the Russian Science Foundation (project No.15-11-20022).

Keywords: conservation, equilibrium state, gas hydrate reservoir, rocks

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Authors: Farnaz Amidi Fazli

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After long period of human experience about food processing from raw eating to canning of food in the last century now it is time to use novel technologies which are sometimes completely different from common technologies. It is possible to decontaminate food without using heat or the foods are stored without using cold chain. Pulsed electric field (PEF) processing is a non-thermal method of food preservation that uses short bursts of electricity, PEF can be used for processing liquid and semi-liquid food products. PEF processing offers high quality fresh-like liquid foods with excellent flavor, nutritional value, and shelf-life. High pressure processing (HPP) technology has the potential to fulfill both consumer and scientific requirements. The use of HPP for over 50 years has found applications in non-food industries. For food applications, ‘high pressure’ can be generally considered to be up to 600 MPa for most food products. After years, freezing has its high potential to food preservation due to new and quick freezing methods. Foods which are prepared by this technology have more acceptability and high quality comparing with old fashion slow freezing. Thus, quick freezing has further been adopted as a widespread commercial method for long-term preservation of perishable foods which improved both the health and convenience of everyone in the industrialised countries. Above parameters are achieved by Fluidised-bed freezing systems, freezing by immersion and Hydrofluidisation on the other hand new thawing methods like high-pressure, microwave, ohmic, and acoustic thawing have a key role in quality and adaptability of final product.

Keywords: quick freezing, thawing, high pressure, pulse electric, hydrofluidisation

Procedia PDF Downloads 321

Authors: Marwa Al-Subhi, Eiman Al-Ajmi, Mallak Al-Maamari, Humood Al-Dhuhli, Srinivasa Rao

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The orbit and its contents are affected by various pathologies and craniofacial anomalies. Sound knowledge of the normal orbital dimensions is clinically essential for successful surgical outcomes and also in the field of forensic anthropology. Racial, ethnic, and regional variations in the orbital dimensions have been reported. This study sought to determine the orbital dimensions of Omani subjects who had been referred for computed tomography (CT) images at a tertiary care hospital. A total of 273 patients’ CT images were evaluated retrospectively by using an electronic medical records database. The orbital dimensions were recorded using both axial and sagittal planes of CT images. The mean orbital index (OI) was found to be 83.25±4.83 and the prevalent orbital type was categorized as mesoseme. The mean orbital index was 83.34±5.05 and 83.16±4.57 in males and females, respectively, with their difference being statistically not significant (p=0.76). A statistically significant association was observed between the right and left orbits with regard to horizontal distance (p<0.05) and vertical distance (p<0.01) of orbit and OI (p<0.05). No significant difference between the OI and age groups was observed in both males and females. The mean interorbital distance and interzygomatic distance were found to be 19.45±1.52 mm and 95.59±4.08 mm, respectively. Both of these parameters were significantly higher in males (p<0.05). Results of the present study provide reference values of orbital dimensions in Omani subjects. The prevalent orbital type of Omani subjects is mesoseme, which is a hallmark of the white race.

Keywords: orbit, orbital index, mesoseme, ethnicity, variation

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Authors: Amrutha Jayan

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The cities are attractors of the human population, offering opportunities for economic activities for different linguistic, cultural, and ethnic groups. The urban form and design of the city impact the life of these people. Social and cultural exclusions result in spatial segregation and gentrification. The spaces provided in cities must be inclusive for all these communities for them to feel part of the city and contribute to society. Intercultural urbanism is a theory and practice of city building, planning, and design of urban spaces and architectures that are cognizant of the social impact of the built environment. The postulate acknowledges cultural differences and opportunities for cultural exchange. Literature on intercultural urbanism, culture and space, spatial justice, and cultural inclusion are analyzed to identify parameters contributing to intercultural placemaking. A qualitative study on Mattancherry shows how the precinct has sustained throughout the years with different communities living together within a radius of 5 km, creating a diverse and vibrant environment. The research identifies the urban elements that contribute to intercultural interactions and maintain the synergy between these communities. The public spaces, porous edges, built-form, streets, and accessibility contribute to chance encounters and intercultural interactivity. The research seeks to find the factors that contribute to intercultural placemaking.

Keywords: intercultural urbanism, cultural inclusion, spatial justice, public space

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Authors: Mamta Rana, K. K. Singh, Nisha Kumari

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The simulation models with its soil-weather-plant atmosphere interacting system are important tools for assessing the crops in changing climate conditions. The CERES-Wheat & Rice vs. 4.6 DSSAT was calibrated and evaluated for one of the major producers of wheat and rice state- Haryana, India. The simulation runs were made under irrigated conditions and three fertilizer applications dose of N-P-K to estimate crop yield and other growth parameters along with the phenological development of the crop. The genetic coefficients derived by iteratively manipulating the relevant coefficients that characterize the phenological process of wheat and rice crop to the best fit match between the simulated and observed anthesis, physological maturity and final grain yield. The model validated by plotting the simulated and remote sensing derived LAI. LAI product from remote sensing provides the edge of spatial, timely and accurate assessment of crop. For validating the yield and yield components, the error percentage between the observed and simulated data was calculated. The analysis shows that the model can be used to simulate crop yield and yield components for wheat and rice cultivar under different management practices. During the validation, the error percentage was less than 10%, indicating the utility of the calibrated model for climate risk assessment in the selected region.

Keywords: simulation model, CERES-wheat and rice model, crop yield, genetic coefficient

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Authors: Muhammad B. Ibrahim, Muhammad S. Sulaiman, Sadiq Sani

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Neem leaves were studied as plant wastes derived adsorbents for detoxification of Congo Red (CR) and Methyl Orange (MO) from aqueous solutions using batch adsorption technique. The objectives involved determining the effects of the basic adsorption parameters are namely, agitation time, adsorbent dosage, adsorbents particle size, adsorbate loading concentrations and initial pH, on the adsorption process as well as characterizing the adsorbents by determining their physicochemical properties, functional groups responsible for the adsorption process using Fourier Transform Infrared (FTIR) spectroscopy and surface morphology using scanning electron microscopy (SEM) coupled with energy dispersion X – ray spectroscopy (EDS). The adsorption behaviours of the materials were tested against Langmuir, Freundlich, etc. isotherm models. Percent adsorption increased with increase in agitation time (5 – 240 minutes), adsorbent dosage (100-500mg), initial concentration (100-300mg/L), and with decrease in particle size (≥75μm to ≤300μm) of the adsorbents. Both processes are dye pH-dependent, increasing or decreasing percent adsorption in acidic (2-6) or alkaline (8-12) range over the studied pH (2-12) range. From the experimental data the Langmuir’s separation factor (RL) suggests unfavourable adsorption for all processes, Freundlich constant (nF) indicates unfavourable process for CR and MO adsorption; while the mean free energy of adsorption

Keywords: adsorption, congo red, methyl orange, neem leave

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Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

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Authors: Batsukh Chultem, Oyunbileg Natsagdorj, Namsrai Steyrmunkh

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Many industries, tourist camps and houses, discharge aqueous effluents containing relatively high levels of heavy metals, harmful organic compounds water. Untreated effluent from these manufacturing processes has an adverse impact on the environment. A specific problem associated with waste water in the environment is accumulation in the food chain and persistence in the environment. The screening of microorganisms resistant to pollution and able to detoxification them is essential for the development of clean-up technologies. The purpose of this study is to use advanced microbiological technology products for oxidizing organic and heavy metals pollutants as a biological treatment, to reduce water pollution, which arise as a result of waste water due to day-to-day operations of industries and houses of Ulaanbaatar city and tourist camps located around the lake Hovsgol, in Hovsgol province of Mongolia. By comparing the results from tests of effective microorganism’s bio-preparation treated sewage samples and not treated sewage samples shows that the treated sewage samples pollution decreased defending on treatment period and ratio. Treated water analyses show that: the suspended solids 352 mg/l, pH 5.85-7.95, ammonium nitrate 81.25-221.2 mg NH₄/l, nitrite 0.088-0.227 mg NO₂/l, nitrate 8.5-11.5 mg NO₃/l, and orthophosphate 1.06-15.46 mg PO₄/l. Also, heavy metals were decreased and microbiological test results defined parameters, respectively show the waste water pollution was reduced.

Keywords: effective microorganims, environment, pollution, treatment

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Authors: Neda Valizadeh, Mani Mofidi, Sama Haghighi, Ali Hashemaghaee, Soudabeh Shafiee Ardestani

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Background: There are many systems and parameters to evaluate trauma patients in the emergency department. Most of these evaluations are to distinguish patients with worse conditions so that the care systems have a better prediction of condition for a better care-giving. The purpose of this study is to determine the relationship between axillary body temperature and mortality in patients hospitalized in the intensive care unit (ICU) with multiple traumas and with other clinical and para-clinical factors. Methods: All patients between 16 and 75 years old with multiple traumas who were admitted into Emergency Department then hospitalized in the ICU were included in our study. An axillary temperature in the first and the second day of admission, Glasgow cola scale (GCS), systolic blood pressure, Serum glucose levels, and white blood cell counts of all patients at the admission day were recorded and their relationship with mortality were analyzed by SPSS software with suitable statistical tests. Results: Axillary body temperatures in the first and second day were statistically lower in expired traumatic patients (p=0.001 and p<0,001 respectively). Patients with lower GCS had a significantly lower first-day temperature and a significantly higher mortality. (p=0.006 and p=0.006 respectively). Furthermore, the first-day axillary temperature was significantly lower in patients with a lower first-day systolic blood pressure (p=0.014). Conclusion: Our results showed that lower axillary body temperature in the first day is associated with higher mortality, lower GCS, and lower systolic blood pressure. Thus, this could be used as a predictor of mortality in evaluation of traumatic patients in emergency settings.

Keywords: fever, trauma, mortality, emergency

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Authors: Boris Kolev, Matt Kokan, Mohammad Deriszadeh, Farshid Bateni

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Due to the increasing demand for real estate and the need for efficient land utilization in Greater Vancouver, developers have been increasingly considering the construction of high-rise structures with multiple below-grade parking. The temporary excavations required to allow for the construction of underground levels have recently reached up to 40 meters in depth. One of the challenges with deep excavations is the prediction of wall displacements and ground settlements due to their effect on the integrity of City utilities, infrastructure, and adjacent buildings. A large database of survey monitoring data has been collected for deep excavations in various soil conditions and shoring systems. The majority of the data collected is for tie-back anchors and shotcrete lagging systems. The data were categorized, analyzed and the results were evaluated to find a relationship between the most dominant parameters controlling the displacement, such as depth of excavation, soil properties, and the tie-back anchor loading and arrangement. For a select number of deep excavations, finite element modeling was considered for analyses. The lateral displacements from the simulation results were compared to the recorded survey monitoring data. The study concludes with a discussion and comparison of the available empirical and numerical modeling methodologies for evaluating lateral displacements in deep excavations.

Keywords: deep excavations, lateral displacements, numerical modeling, shoring walls, tieback anchors

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Authors: Riccardo Houngbegnon, Loic Chrislin Nguedjio, Valery Doko, José Xavier, Miran Merhar, Rostand Moutou Pitti

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Against a backdrop of heightened awareness of environmental impact and the reduction of space debris, the use of sustainable materials for mobility applications is emerging as a promising solution to minimize the environmental footprint of our technologies. Among recent innovative developments in the use of wood, the Japanese species Magnolia obovata attracted particular interest when it was used in the design of the first wooden satellite launched in November 2024. The aim of this project is to explore new species that could replace M. obovata in a mobile context. Khaya ivorensis, a tropical African species, was selected and compared to M. obovata in terms of resistance to cracking, a key criterion in the durability of mobility infrastructures. Prior to the cracking tests, K. ivorensis and M. obovata were characterized to determine their basic mechanical properties. The results presented here relate to this characterization phase, in particular the four-point bending, compression and BING tests, which provided us with strengths and moduli. These results were compared with those found in the literature, which allowed us to observe a number of differences. CHARPY resilience tests were also performed and compare to critical energy release rate in order to estimate the ability of the two species to absorb energy, particularly following impacts and various shocks.

Keywords: energy release rate, Khaya ivorensis, magnolia obovata, wood for mobility

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Authors: Sheila Shahidi

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UV protection by fabrics has recently become a focus of great interest, particularly in connection with environmental degradation or ozone layer depletion. Fabrics provide simple and convenient protection against UV radiation (UVR), but not all fabrics offer sufficient UV protection. To describe the degree of UVR protection offered by clothing materials, the ultraviolet protection factor (UPF) is commonly used. UV-protective fabric can be generated by application of a chemical finish using normal wet-processing methodologies. However, traditional wet-processing techniques are known to consume large quantities of water and energy and may lead to adverse alterations of the bulk properties of the substrate. Recently, usage of plasmas to generate physicochemical surface modifications of textile substrates has become an intriguing approach to replace or enhance conventional wet-processing techniques. In this research work the effect of plasma treatment on UV protection properties of fabrics was investigated. DC magnetron sputtering was used and the parameters of plasma such as gas type, electrodes, time of exposure, power and, etc. were studied. The morphological and chemical properties of samples were analyzed using Scanning Electron Microscope (SEM) and Furrier Transform Infrared Spectroscopy (FTIR), respectively. The transmittance and UPF values of the original and plasma-treated samples were measured using a Shimadzu UV3101 PC (UV–Vis–NIR scanning spectrophotometer, 190–2, 100 nm range). It was concluded that, plasma which is an echo-friendly, cost effective and dry technique is being used in different branches of the industries, and will conquer textile industry in the near future. Also it is promising method for preparation of UV protection textile.

Keywords: fabric, plasma, textile, UV protection

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Authors: M. Javanmard

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This study was aimed at analyzing the effects of packaging (MAP) and preservation conditions on the packaged fresh walnut kernel quality. The central composite plan was used for evaluating the effect of oxygen (0–10%), carbon dioxide (0-10%), and temperature (4-26 °C) on qualitative characteristics of walnut kernels. Also, the response level technique was used to find the optimal conditions for interactive effects of factors, as well as estimating the best conditions of process using least amount of testing. Measured qualitative parameters were: peroxide index, color, decreased weight, mould and yeast counting test, and sensory evaluation. The results showed that the defined model for peroxide index, color, weight loss, and sensory evaluation is significant (p < 0.001), so that increase of temperature causes the peroxide value, color variation, and weight loss to increase and it reduces the overall acceptability of walnut kernels. An increase in oxygen percentage caused the color variation level and peroxide value to increase and resulted in lower overall acceptability of the walnuts. An increase in CO₂ percentage caused the peroxide value to decrease, but did not significantly affect other indices (p ≥ 0.05). Mould and yeast were not found in any samples. Optimal packaging conditions to achieve maximum quality of walnuts include: 1.46% oxygen, 10% carbon dioxide, and temperature of 4 °C.

Keywords: shelled walnut, MAP, quality, storage temperature

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Authors: Shaymaa Kennedy, Sam Clark, Paul Shaply

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With the upcoming construction of high-speed rail HS2 in the UK, a number of issues surrounding the construction technology and track design need to be answered. In this paper performance of subsoil subjected to dynamic loads were studied. The material of study is Mudrock backfill, a weak prevalent rock which response under indicative loading of high-speed rail line is unknown. This paper aims to investigate the use of different track types and the influence they will have on the underlying soil, in order to evaluate the behaviour of it. Ballstless track is a well-established concept in Europe, and the investigation the benefit of the form of construction due to its known savings in maintenance costs. Physical test using a triaxial cyclic loading machine was conducted to assess the expected mechanical behaviour of mudrock under a range of dynamic loads which could be generated beneath different track constructions. Some further parameters are required to frame the problem including determining the stress change with depth and cyclic response are vital to determine the residual plastic strain which is a major concern. In addition, Stress level is discussed in this paper, which are applied to recreate conditions of soil in the laboratory. Results indicate that stress levels are highly influential on the performance of soil at shallower depth and become insignificant with increasing depth.

Keywords: stress level, dynamic load, residual plastic strain, high speed railway

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Authors: Izabela Zielinska

Abstract:

Kinesio Taping is classified as physiotherapy method supporting rehabilitation and modulating some physiological processes. It is commonly used in sports medicine and orthopedics. This sensory method has influence on muscle function, pain sensation, intensifies lymphatic system as well as improves microcirculation. The aim of this study was to assess the effect of Kinesio Taping in patients with ongoing treatment of peripheral artery disease (PAD). The study group comprised 60 patients (stadium II B at Fontain's scale). All patients were divided into two groups (30 person/each), where 12 weeks long treadmill training was administrated. In the second group, the Kinesio Taping was applied to support the function of the gastrocnemius muscle. The measurements of distance and time until claudication pain, blood flow of arteries in lower limbs and ankle brachial index were taken under evaluation. Examination performed after Kinesio Taping therapy showed statistically significant increase in gait parameters and muscle strength in patients with intermittent claudication. The Kinesio Taping method has clinically significant effects on enhancement of pain-free distance and time until claudication pain in patients with peripheral artery disease. Kinesio Taping application can be used to support non-invasive treatment in patients with intermittent claudication. Kinesio Taping can be employed as an alternative way of therapy for patients with orthopedic or cardiac contraindications to be treated with treadmill training.

Keywords: intermittent claudication, kinesiotaping, peripheral artery disease, treadmill training

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Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

Abstract:

Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

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Authors: Hassen Hamouda, Mohamed Ouwais Kabaou, Med Salim Bouhlel

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The growth of wireless technology made opportunistic scheduling a widespread theme in recent research. Providing high system throughput without reducing fairness allocation is becoming a very challenging task. A suitable policy for resource allocation among users is of crucial importance. This study focuses on scheduling multiple streaming flows on the downlink of a WiMAX system based on orthogonal frequency division multiple access (OFDMA). In this paper, we take the first step in formulating and analyzing this problem scrupulously. As a result, we proposed a new scheduling scheme based on Round Robin (RR) Algorithm. Because of its non-opportunistic process, RR does not take in account radio conditions and consequently it affect both system throughput and multi-users diversity. Our contribution called MORRA (Modified Round Robin Opportunistic Algorithm) consists to propose a solution to this issue. MORRA not only exploits the concept of opportunistic scheduler but also takes into account other parameters in the allocation process. The first parameter is called courtesy coefficient (CC) and the second is called Buffer Occupancy (BO). Performance evaluation shows that this well-balanced scheme outperforms both RR and MaxSNR schedulers and demonstrate that choosing between system throughput and fairness is not required.

Keywords: OFDMA, opportunistic scheduling, fairness hierarchy, courtesy coefficient, buffer occupancy

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Authors: Md Masihuddin Siddiqui, Abdul Haakim Mohammed

Abstract:

Earthquakes are the most unpredictable and devastating of all natural disasters. The behaviour of a building during an earthquake depends on several factors such as stiffness, adequate lateral strength, ductility, and configurations. The experience from the performance of buildings during past earthquakes has shown that the buildings with regular geometry, uniformly distributed mass and stiffness in plan as well as in elevation suffer much less damage compared to irregular configurations. The three centers namely- centre of mass, centre of strength, centre of stiffness are the torsional parameters which contribute to the strength of the building in case of an earthquake. Inertial forces and resistive forces in a structural system act through the center of mass and center of rigidity respectively which together oppose the forces that are produced during seismic excitation. So these centers of a structural system should be positioned where the structural system is the strongest so that the effects produced due to the earthquake may have a minimal effect on the structure. In this paper, the effects of variation of strength eccentricity and stiffness eccentricity in reducing the torsional responses of the asymmetrical buildings by using pushover analysis are studied. The maximum reduction of base torsion was observed in the case of minimum strength eccentricity, and the least reduction was observed in the case of minimum stiffness eccentricity.

Keywords: strength eccentricity, stiffness eccentricity, asymmetric structure, base torsion, push over analysis

Procedia PDF Downloads 294

Authors: Jiajia Yao, Guanlin Wu, Fang Liu, Junshuai Xue, Yue Hao

Abstract:

This study reports on the epitaxial growth of a GaN-based resonant tunneling diode (RTD) structure with stable and repeatable double negative differential resistance (NDR) characteristics at room temperature on a c-plane GaN-on-sapphire template using plasma-assisted molecular beam epitaxy (PA-MBE) technology. In this structure, two independent AlN/GaN RTDs are epitaxially connected in series in the vertical growth direction through a silicon-doped GaN layer. As the collector electrode bias voltage increases, the two RTDs respectively align the ground state energy level in the quantum well with the 2DEG energy level in the emitter accumulation well to achieve quantum resonant tunneling and then reach the negative differential resistance (NDR) region. The two NDR regions exhibit similar peak current densities and peak-to-valley current ratios, which are 230 kA/cm² and 249 kA/cm², 1.33 and 1.38, respectively, for a device with a collector electrode mesa diameter of 1 µm. The consistency of the NDR is much higher than the results of on-chip discrete RTD device interconnection, resulting from the smaller chip area, fewer interconnect parasitic parameters, and less process complexity. The methods and results presented in this paper show the brilliant prospects of GaN RTDs in the development of multi-value logic digital circuits.

Keywords: MBE, AlN/GaN, RTDs, double NDR

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Authors: Ejaz Ahmed, Huang Yong

Abstract:

The operation of aero engines has a critical importance in the vicinity of lean blowout (LBO) limits. Lefebvre’s model of LBO based on empirical correlation has been extended to flame volume concept by the authors. The flame volume takes into account the effects of geometric configuration, the complex spatial interaction of mixing, turbulence, heat transfer and combustion processes inside the gas turbine combustion chamber. For these reasons, flame volume based LBO predictions are more accurate. Although LBO prediction accuracy has improved, it poses a challenge associated with Vf estimation in real gas turbine combustors. This work extends the approach of flame volume prediction previously based on fuel iterative approximation with cold flow simulations to reactive flow simulations. Flame volume for 11 combustor configurations has been simulated and validated against experimental data. To make prediction methodology robust as required in the preliminary design stage, reactive flow simulations were carried out with the combination of probability density function (PDF) and discrete phase model (DPM) in FLUENT 15.0. The criterion for flame identification was defined. Two important parameters i.e. critical injection diameter (Dp,crit) and critical temperature (Tcrit) were identified, and their influence on reactive flow simulation was studied for Vf estimation. Obtained results exhibit ±15% error in Vf estimation with experimental data.

Keywords: CFD, combustion, gas turbine combustor, lean blowout

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Authors: Nafiseh Mollaei, Reihaneh Kardehi Moghaddam

Abstract:

In case of Diabetes Mellitus the controlling of insulin is very difficult. This illness is an incurable disease affecting millions of people worldwide. Glucose is a sugar which provides energy to the cells. Insulin is a hormone which supports the absorption of glucose. Fuzzy control strategy is attractive for glucose control because it mimics the first and second phase responses that the pancreas beta cells use to control glucose. We propose two control algorithms a type-1 fuzzy controller and an interval type-2 fuzzy method for the insulin infusion. The closed loop system has been simulated for different patients with different parameters, in present of the food intake disturbance and it has been shown that the blood glucose concentrations at a normoglycemic level of 110 mg/dl in the reasonable amount of time. This paper deals with type 1 diabetes as a nonlinear model, which has been simulated in MATLAB-SIMULINK environment. The novel model, termed the Augmented Minimal Model is used in the simulations. There are some uncertainties in this model due to factors such as blood glucose, daily meals or sudden stress. In addition to eliminate the effects of uncertainty, different control methods may be utilized. In this article, fuzzy controller performance were assessed in terms of its ability to track a normoglycemic set point (110 mg/dl) in response to a [0-10] g meal disturbance. Finally, the development reported in this paper is supposed to simplify the insulin delivery, so increasing the quality of life of the patient.

Keywords: interval type-2, fuzzy controller, minimal augmented model, uncertainty

Procedia PDF Downloads 428