Search results for: electrical network frequency stability

Authors: Taha Benarbia

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The advent of automated and connected vehicles has revolutionized the transportation industry, presenting new opportunities for enhancing the efficiency, safety, and sustainability of our transportation networks. This paper explores the integration of automated and connected vehicles into a smart transportation framework, leveraging the power of deep learning techniques to optimize the overall network performance. The first aspect addressed in this paper is the deployment of automated vehicles (AVs) within the transportation system. AVs offer numerous advantages, such as reduced congestion, improved fuel efficiency, and increased safety through advanced sensing and decisionmaking capabilities. The paper delves into the technical aspects of AVs, including their perception, planning, and control systems, highlighting the role of deep learning algorithms in enabling intelligent and reliable AV operations. Furthermore, the paper investigates the potential of connected vehicles (CVs) in creating a seamless communication network between vehicles, infrastructure, and traffic management systems. By harnessing real-time data exchange, CVs enable proactive traffic management, adaptive signal control, and effective route planning. Deep learning techniques play a pivotal role in extracting meaningful insights from the vast amount of data generated by CVs, empowering transportation authorities to make informed decisions for optimizing network performance. The integration of deep learning with automated and connected vehicles paves the way for advanced transportation network optimization. Deep learning algorithms can analyze complex transportation data, including traffic patterns, demand forecasting, and dynamic congestion scenarios, to optimize routing, reduce travel times, and enhance overall system efficiency. The paper presents case studies and simulations demonstrating the effectiveness of deep learning-based approaches in achieving significant improvements in network performance metrics

Keywords: automated vehicles, connected vehicles, deep learning, smart transportation network

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Authors: Jenna Metera, Olivia Graeve

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Ceramic capacitor technologies using lead based materials is being phased out for its environmental and handling hazards. Bismuth ferrite (BiFeO₃) is the next best replacement for those lead-based technologies. Unfortunately, the electrical properties in bismuth systems are not as robust as the lead alternatives. The improvement of electrical properties such as charge density, charge anisotropy, relative permittivity, and dielectric loss are the parameters that will make BiFeO₃ a competitive alternative to lead-based ceramic materials. In order to maximize the utility of these properties, we propose the ordering and an evaporation-induced self-assembly of a cubic morphology powder. Evaporation-induced self-assembly is a template-less, bottom-up, self-assembly option. The capillary forces move the particles closer together when the solvent evaporates, promoting organized agglomeration at the particle faces. The assembly of particles into organized structures can lead to enhanced properties compared to unorganized structures or single particles themselves. The interactions between the particles can be controlled based on the long-range order in the organized structure. The cubic particle morphology is produced through a hydrothermal synthesis with changes in the concentration of potassium hydroxide, which changes the morphology of the powder. Once the assembly materializes, the powder is fabricated into workable substrates for electrical testing after consolidation.

Keywords: evaporation, lead-free, morphology, self-assembly

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Authors: Ge Zheng, Jun Peng

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Unbalanced power output from individual cells of Proton Exchange Membrane Fuel Cell (PEMFC) has direct effects on PEMFC stack performance, in particular under transient operation. In the paper, a multi-layer ANN (Artificial Neural Network) model Radial Basis Functions (RBF) has been developed for predicting cells' output profiles by applying gas supply parameters, cooling conditions, temperature measurement of individual cells, etc. The feed-forward ANN model was validated with experimental data. Influence of relevant parameters of RBF on the network accuracy was investigated. After adequate model training, the modelling results show good correspondence between actual measurements and reconstructed output profiles. Finally, after the model was used to optimize the stack output performance under steady-state and transient operating conditions, it suggested that the developed ANN control model can help PEMFC stack to have obvious improvement on power output under fast acceleration process.

Keywords: proton exchange membrane fuel cell, PEMFC, artificial neural network, ANN, cell output profile, transient

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Authors: Leyla Yesilbas, M. Sukru Ozcoban, M. Ergenekon Selcuk

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From past to present, there is a constant need for engineering structures such as high-rise buildings, wide-span bridges, airports and stadiums, business towers due to technological developments and increasing population. Because of the large loads transferred from the superstructure to the ground layers in these types of structures, the bearing strength and seating problems usually occur on the floors. In order to solve these problems, piled foundations are used by passing the weak soil layers and transferring the loads from the superstructure to the solid soil layers. Considering the factors such as the characteristics of the building to be constructed, the purpose and location of the building, the basic cost of the pile should be at normal levels. When these requirements are taken into consideration, a new basic system called 'Barette Foundation' has been developed. In this thesis, an application made to provide slope stability with 'Baret Piles' was investigated. In addition, the ground parameters obtained from the field and laboratory experiments were numerically modeled using a PLAXİS 2D finite element software and barette piles. The effects of barette piles on slope stability were investigated by numerical analysis, and the results of inclinometer measurements in the field were compared with numerical analysis results.

Keywords: barette pile, PLAXİS 2D, slope, soil

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Authors: Saeedeh Barzegar Khalilsaraei, Manoocheher Kelarestaghi, Farshad Eshghi

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The vehicle color, as a prominent and stable feature, helps to identify a vehicle more accurately. As a result, vehicle color recognition is of great importance in intelligent transportation systems. Unlike conventional methods which use only a single Convolutional Neural Network (CNN) for feature extraction or classification, in this paper, four CNNs, with different architectures well-performing in different classes, are trained to extract various features from the input image. To take advantage of the distinct capability of each network, the multiple outputs are combined using a stack generalization algorithm as an ensemble technique. As a result, the final model performs better than each CNN individually in vehicle color identification. The evaluation results in terms of overall average accuracy and accuracy variance show the proposed method’s outperformance compared to the state-of-the-art rivals.

Keywords: Vehicle Color Recognition, Ensemble Algorithm, Stack Generalization, Convolutional Neural Network

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Authors: Nureni Asafe Yekini, Olawale Nasiru Lawal, Bola Dada, Gabriel Adeyemi Okunlola

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This study is focused on the strengths and challenges associated with using the online social network as a rapidly evolving medium in marketing tourism services and businesses among the youths in Nigeria. The paper examines the Nigerian tourists’ attitude, mainly towards three aspects: application of Internet for travel and tourism; usage of online social networks in sharing travel and tourism experiences; and trust in electronic-media for marketing tourism businesses and services. The aim of this research is to determine the level of application of internet tools in marketing tourism businesses and services in Nigeria. This study reports an empirical analysis based on data obtained from a survey among 1004 Nigerian tourists. The outcome confirms the research hypothesis and points to crucial importance of introducing online social network site for marketing tourism businesses and services in Nigeria, and increasing the awareness for Nigeria as a tourist destination. Moreover, the paper strongly recommends the use of online social network as a tool for marketing tourism businesses and services, and the need for identifying effective framework for application of ICT tools in marketing tourism businesses and services in Nigeria at large.

Keywords: tourism business, internet, online social networks, tourism services, ICT

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Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

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Authors: J. Pantoja Enríquez, G. P. Hernández, G. I. Duharte, X. Mathew, J. Moreira, P. J. Sebastian

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Cadmium sulfide films were deposited onto glass substrates by chemical bath deposition (CBD) from a bath containing cadmium acetate, ammonium acetate, thiourea, and ammonium hydroxide. The CdS thin films were annealed in air, argon, hydrogen and nitrogen for 1 h at various temperatures (300, 350, 400, 450 and 500 °C). The changes in optical and electrical properties of annealed treated CdS thin films were analyzed. The results showed that, the band-gap and resistivity depend on the post-deposition annealing atmosphere and temperatures. Thus, it was found that these properties of the films, were found to be affected by various processes with opposite effects, some beneficial and others unfavorable. The energy gap and resistivity for different annealing atmospheres was seen to oscillate by thermal annealing. Recrystallization, oxidation, surface passivation, sublimation and materials evaporation were found the main factors of the heat-treatment process responsible for this oscillating behavior. Annealing over 400 °C was seen to degrade the optical and electrical properties of the film.

Keywords: cds, thin films, annealing, optical, electrical properties

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Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

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Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.

Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images

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Authors: Sri Harimurti, Diah Reni Asih

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This study was designated to investigate the effect of intermittent and continuous blue lighting on the feeding behaviour, growth and corticosterone hormone concentration of broiler. Two thousands and seven hundreds unsexed day-old broiler were divided into three groups of lighting treatment. Each treatment consisted of three replicates of 300 birds. The treatments were ordinary lighting (C), intermittent blue lighting (IBL) and continuous blue lighting (CBL). The data were collected in the study were feeding behaviour such as feeding duration and frequency of feeding, growth rate of birds and corticosterone hormone concentration. Results showed that the CBL have significant effect (P<0,05) on duration and frequency of feeding and growth rate of birds. The CBL have the highest feeding duration, the lowest frequency of feeding that those 290.33±1.52 minutes/day, 35.58±0.50 times/day at 15 to 28 days of age.The concentration of corticosterone hormone of IBL and CBL were a significant (P<0.05) decrease. The conclusion of this study indicated that continuous blue lighting may be a good tool for improving welfare management of broiler.

Keywords: blue light, broiler chickens, corticosterone hormone, feeding behaviour, growth rate

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Authors: Asmaa Mandour, Ramzia El-Bagary, Asmaa El-Zaher, Ehab Elkady

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Background: An UHPLC (ultra high performance liquid chromatography) method for the simultaneous determination of norfloxacin (NOR), metronidazole (MET) and tinidazole (TNZ) using monolithic column is presented. Purpose: The method is considered an environmentally friendly method with relatively low organic composition of the mobile phase. Methods: The chromatographic separation was performed using Phenomenex® Onyex Monolithic C18 (50mmx 20mm) column. An elution program of mobile phase consisted of 0.5% aqueous phosphoric acid : methanol (85:15, v/v). Where elution of all drugs was completed within 3.5 min with 1µL injection volume. The UHPLC method was applied for the stability indication of NOR in the presence of its acid degradation product ND. Results: Retention times were 0.69, 1.19 and 3.23 min for MET, TNZ and NOR, respectively. While ND retention time was 1.06 min. Linearity, accuracy, and precision were acceptable over the concentration range of 5-50µg mL-1for all drugs. Conclusions: The method is simple, sensitive and suitable for the routine quality control and dosage form assay of the three drugs and can also be used for the stability indication of NOR in the presence of its acid degradation product.

Keywords: antibacterial, monolithic cilumn, simultaneous determination, UHPLC

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Authors: Mahmoud Samkan

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Dispersed Generation (DG) is a promising solution to many power system problems such as voltage regulation and power loss. This paper proposes a heuristic two-step method to optimize the location and size of DG for reducing active power losses and, therefore, improve the voltage profile in radial distribution networks. In addition to a DG placed at the system load gravity center, this method consists in assigning a DG to each lateral of the network. After having determined the central DG placement, the location and size of each lateral DG are predetermined in the first step. The results are then refined in the second step. This method is tested for 33-bus system for 100% DG penetration. The results obtained are compared with those of other methods found in the literature.

Keywords: optimal location, optimal size, dispersed generation (DG), radial distribution networks, reducing losses

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Authors: S. Kida

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We consider the flow of an incompressible viscous fluid in a precessing sphere whose spin and precession axes are orthogonal to each other. The flow is characterized by two non-dimensional parameters, the Reynolds number Re and the Poincare number Po. For which values of (Re, Po) will the flow approach a steady state from an arbitrary initial condition? To answer it we are searching the instability boundary of the steady states in the whole (Re, Po) plane. Here, we focus the rapidly rotating and weakly precessing limit, i.e., Re >> 1 and Po << 1. The steady flow was obtained by the asymptotic expansion for small ε=Po Re¹/² << 1. The flow exhibits nearly a solid-body rotation in the whole sphere except for a thin boundary layer which develops over the sphere surface. The thickness of this boundary layer is of O(δ), where δ=Re⁻¹/², except where two circular critical bands of thickness of O(δ⁴/⁵) and of width of O(δ²/⁵) which are located away from the spin axis by about 60°. We perform the linear stability analysis of the steady flow. We assume that the disturbances are localized in the critical bands and make an expansion analysis in terms of ε to derive the eigenvalue problem for the growth rate of the disturbance, which is solved numerically. As the solution, we obtain an asymptote of the stability boundary as Po=28.36Re⁻⁰.⁸. This agrees excellently with the corresponding laboratory experiments and numerical simulations. One of the most popular instability mechanisms so far is the parametric instability, which turns out, however, not to give the correct stability boundary. The present instability is different from the parametric instability.

Keywords: boundary layer, critical band, instability, precessing sphere

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Authors: Namrata Mishra

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Health of a person plays a vital role in the collective health of his community and hence the well-being of the society as a whole. But, in today’s fast paced technology driven world, health issues are increasingly being associated with human behaviors – their lifestyle. Social networks have tremendous impact on the health behavior of individuals. Many researchers have used social network analysis to understand human behavior that implicates their social and economic environments. It would be interesting to use a similar analysis to understand human behaviors that have health implications. This paper focuses on concepts of those behavioural analyses that have health implications using social networks analysis and provides possible algorithmic approaches. The results of these approaches can be used by the governing authorities for rolling out health plans, benefits and take preventive measures, while the pharmaceutical companies can target specific markets, helping health insurance companies to better model their insurance plans.

Keywords: breadth first search, directed graph, health behaviors, social network analysis

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Authors: Beata Jackowska-Zduniak

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We formulate and analyze a mathematical model describing dynamics of the hypothalamus-pituitary-thyroid homoeostatic mechanism in endocrine system. We introduce to this system two types of couplings and delay. In our model, feedback controls the secretion of thyroid hormones and delay reflects time lags required for transportation of the hormones. The influence of delayed feedback on the stability behaviour of the system is discussed. Analytical results are illustrated by numerical examples of the model dynamics. This system of equations describes normal activity of the thyroid and also a couple of types of malfunctions (e.g. hyperthyroidism).

Keywords: mathematical modeling, ordinary differential equations, endocrine system, delay differential equation

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Authors: Debneil Saha Roy

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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

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Authors: Raed Hanandeh

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This empirical study examines how knowledge sharing behaviors mediate the effect Technology-driven strategy on virtual organization success in Jordanian tourism sector firms. The results reveal that Social network ties are positively related to web knowledge seeking, web knowledge contributing and interactive system, but negatively related to accidental knowledge leakage. Furthermore, all types of knowledge sharing behavior are positively related to virtual organization success. Data collected from 23 firms. The total number of questionnaires mailed, 250 questionnaires were delivered. 214 were considered valid out of 241 Responses were received. The findings provide evidence that knowledge sharing behavior play a mediating role between Social network ties and virtual organization success and show that, web knowledge seeking, web knowledge contributing and interactive system playing an important impact on virtual organization success through knowledge sharing behaviors.

Keywords: social network ties, virtual organization success, knowledge sharing behaviors, web knowledge

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Authors: Mei Xutao, Nakano Kimihiko

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In order to enhance the energy harvesting efficiency, this paper presents a novel tri-stable energy harvesting system (TEHS), which is realized by the effect of magnetic force, in rotational motion to scavenge vibration energy. The device is meant to provide the power supply for wireless autonomous systems in low-frequency environment. The nonlinear TEHS is composed of the cantilever beam which is mounted on a rotating hub and partially covered by piezoelectric patch, a tip mass magnet in the end and two fixed magnets. A theoretical investigation using the Lagrangian formulation is derived to describe the motion of the energy harvesting system and the output voltage. Additionally, several numerical simulations were carried out to characterize the system under different external excitations and to validate its performance. The results demonstrated that TEHS owns a wide range of frequency of snap-through and high output voltage compared with the bi-stable energy harvesting system (BEHS). Moreover, some sets of experimental validations will be performed in the future work because the experimental setup is in the configuration now.

Keywords: piezoelectric beam, rotational motion, snap-through, tri-stable energy harvester

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Authors: Yu-Chu Yeh, WeiChin Hsu, Chih-Yen Chang

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Substitution, combination, modification, putting to other uses, elimination, and rearrangement (SCAMPER) has been regarded as an effective technique that provides a structured way to help people to produce creative ideas and solutions. Although some neuroscience studies regarding creativity training have been conducted, no study has focused on SCAMPER. This study therefore aimed at examining whether the learning of SCAMPER through video tutorials would result in alternations of neural activation. Thirty college students were randomly assigned to the experimental group or the control group. The experimental group was requested to watch SCAMPER videos, whereas the control group was asked to watch natural-scene videos which were regarded as neutral stimulating materials. Each participant was brain scanned in a Functional magnetic resonance imaging (fMRI) machine while undertaking a creativity test before and after watching the videos. Furthermore, a two-way ANOVA was used to analyze the interaction between groups (the experimental group; the control group) and tasks (C task; M task; X task). The results revealed that the left precuneus significantly activated in the interaction of groups and tasks, as well as in the main effect of group. Furthermore, compared with the control group, the experimental group had greater activation in the default mode network (left precuneus and left inferior parietal cortex) and the motor network (left postcentral gyrus and left supplementary area). The findings suggest that the SCAMPER training may facilitate creativity through the stimulation of the default mode network and the motor network.

Keywords: creativity, default mode network, neural activation, SCAMPER

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Authors: Yulin Rao, Zhixuan Li, Burra Venkata Durga Kumar

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Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions.

Keywords: artificial immune system, distributed artificial intelligence, multi-agent, intrusion detection system, neural network

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Authors: H. R. Kelidari, M. Saeedi, J. Akbari, K. Morteza-Semnani, H. Valizadeh

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Spironolactoe (SP) a synthetic steroid diuretic is a poorly water-soluble drug with a low and variable oral bioavailability. Regarding to the good solubility of SP in lipid materials, SP loaded Solid lipid nanoparticles (SP-SLNs) and nanostructured lipid carrier (SP-SLNs) were thus prepared in this work for accelerating dissolution of this drug. The SP loaded NLC with stearic acid (SA) as solid lipid and different Oleic Acid (OA) as liquid lipid content and SLN without OA were prepared by probe ultrasonication method. With increasing the percentage of OA from 0 to 30 wt% in SLN/NLC, the average size and zeta potential of nanoparticles felled down and entrapment efficiency (EE %) rose dramatically. The obtained micrograph particles showed pronounced spherical shape. Differential Scanning Calorimeter (DSC) measurements indicated that the presence of OA reduced the melting temperature and melting enthalpy of solid lipid in NLC structure. The results reflected good long-term stability of the nanoparticles and the measurements show that the particle size remains lower in NLC compare to SLN formulations, 6 months after production. Dissolution of SP-SLN and SP-NLC was about 5.1 and 7.2 times faster than raw drugs in 120 min respectively. These results indicated that the SP loaded NLC containing 70:30 solid lipid to liquid lipid ratio is a suitable carrier of SP with improved drug EE and steady drug release properties.

Keywords: drug release, lipid nanoparticles, spironolactone, stability

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Authors: Mojtaba Valinejadshoubi, Ashutosh Bagchi, Osama Moselhi

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Structures such as buildings, bridges, dams, wind turbines etc. need to be maintained against various factors such as deterioration, excessive loads, environment, temperature, etc. Choosing an appropriate monitoring system is important for determining any critical damage to a structure and address that to avoid any adverse consequence. Structural Health Monitoring (SHM) has emerged as an effective technique to monitor the health of the structures. SHM refers to an ongoing structural performance assessment using different kinds of sensors attached to or embedded in the structures to evaluate their integrity and safety to help engineers decide on rehabilitation measures. Ability of SHM in identifying the location and severity of structural damages by considering any changes in characteristics of the structures such as their frequency, stiffness and mode shapes helps engineers to monitor the structures and take the most effective corrective actions to maintain their safety and extend their service life. The main objective of this study is to review the overall SHM process specifically determining the natural frequency of an instrumented simply-supported concrete beam using modal testing and finite element model updating.

Keywords: structural health monitoring, natural frequency, modal analysis, finite element model updating

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Authors: Simon Oga Egboja, Agi Sunday

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The government of Nigeria have invested so much in our tertiary education but the desire qualitative goals and objectives are yet to be achieved because management at all level are not efficient and effective in implementing the desired educational policies and programmes due to some management challenges. This paper investigates some of the major challenges to effective management of tertiary institution in Nigeria some variable that are important to effective management includes political stability, adequate funding, establishment of information system, recruitment and appointment of qualified teachers and condition of service.

Keywords: effective management includes political stability, adequate funding, establishment of information system, recruitment and appointment of qualified teachers

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Authors: Ibetombi Soibam

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Li-Zn-Ni ferrite having the compositional formula Li0.4-0.5xZn0.2NixFe2.4-0.5xO4 where x = 0.02 ≤ x ≤0.1 in steps of 0.02 was fabricated by the citrate precursor method. In this method, metal nitrates and citric acid was used to prepare the gel which exhibit self-propagating combustion behavior giving the required ferrite sample. The ferrite sample was given a pre-firing at 650°C in a programmable conventional furnace for 3 hours with a heating rate of 5°C/min. A series of the sample was finally given conventional sintering (CS) at 1040°C after the pre-firing process. Another series was given microwave sintering (MS) at 1040°C in a programmable microwave furnace which uses a single magnetron operating at 2.45 GHz frequency. X- ray diffraction pattern confirmed the spinel phase structure for both the series. The theoretical and experimental density was calculated. It was observed that densification increases with the increase in Ni concentration in both the series. However, samples sintered by microwave technique was found to be denser. The microstructure of the two series of the sample was examined using scanning electron microscopy (SEM). Dielectric properties have been investigated as a function of frequency and composition for both series of samples sintered by CS and MS technique. The variation of dielectric constant with frequency show dispersion for both the series. It was explained in terms of Koop’s two layer model. From the analysis of dielectric measurement, it was observed that the value of room temperature dielectric constant decreases with the increase in Ni concentration for both the series. The microwave sintered samples show a lower dielectric constant making microwave sintering suitable for high-frequency applications. The possible mechanisms contributing to all the above behavior is being discussed.

Keywords: citrate precursor, dielectric constant, ferrites, microwave sintering

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Authors: Hossain Samani, Mohammad Ehteram

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In this paper, we consider finite volume method (FVM) in water hammer. We will simulate these techniques on a looped network with complex boundary conditions. After comparing methods, we see the FVM method as the best method. We compare the results of FVM with experimental data. Finite volume using staggered grid is applied for solving water hammer equations.

Keywords: hydraulic transient, water hammer, interpolation, non-liner interpolation

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Authors: Diogo Prosdocimi, Don Ross, Matthew Townshend

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Road authorities are mandated within limited budgets to both deliver improved access to basic services and facilitate economic growth. This responsibility is further complicated if maintenance backlogs and funding shortfalls exist, as evident in many countries including South Africa. These conditions require authorities to make difficult prioritisation decisions, with the effect that Road Asset Management Systems with a one-dimensional focus on traffic volumes may overlook the maintenance of low-volume roads that provide isolated communities with vital access to basic services. Given these challenges, this paper overlays the full South African road network with geo-referenced information for population, primary and secondary schools, and healthcare facilities to identify the network of connective roads between communities and basic service centres. This connective network is then rationalised according to the Gross Value Added and number of jobs per mesozone, administrative and functional road classifications, speed limit, and road length, location, and name to estimate the Basic Access Road Network. A two-step floating catchment area (2SFCA) method, capturing a weighted assessment of drive-time to service centres and the ratio of people within a catchment area to teachers and healthcare workers, is subsequently applied to generate a Multivariate Road Index. This Index is used to assign higher maintenance priority to roads within the Basic Access Road Network that provide more people with better access to services. The relatively limited incidence of Basic Access Roads indicates that authorities could maintain the entire estimated network without exhausting the available road budget before practical economic considerations get any purchase. Despite this fact, a final case study modelling exercise is performed for the Namakwa District Municipality to demonstrate the extent to which optimal relocation of schools and healthcare facilities could minimise the Basic Access Road Network and thereby release budget for investment in roads that best promote GDP growth.

Keywords: basic access roads, multivariate road index, road prioritisation, two-step floating catchment area method

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Authors: Svetlana Zhikrivetskaya, Nataliya Shirokova, Roman Bikanov, Elizaveta Musatova, Yana Kovaleva, Nataliya Vetrova, Ekaterina Pomerantseva

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Nowadays there is a growing number of couples with conceiving problems due to male or female infertility. Genetic abnormalities are responsible for about 31% of all cases of male infertility. These abnormalities include both chromosomal aberrations or aneuploidies and mutations in certain genes. Chromosomal abnormalities can be easily identified, thus the development of screening panels able to reveal genetic reasons of male infertility on gene level is of current interest. There are approximately 2,000 genes involved in male fertility that is the reason why it is very important to determine the most clinically relevant in certain population and ethnic conditions. An infertility screening panel containing 48 mutations in genes AMHR2, CFTR, DNAI1, HFE, KAL1, TSSK2 and AZF locus which are the most clinically relevant for the European population according to databases NCBI and ClinVar was designed. The aim of this research was to confirm clinic relevance of these mutations in the Russian population. Genotyping was performed in 220 patients with different types of male infertility and in 57 healthy males with normozoospermia. Mutations were identified by end-point PCR with TaqMan probes in microfluidic plates. The frequency of 5 mutations in healthy males and 13 mutations in patients with infertility was revealed and estimated. The frequency of mutation c.187C>G in HFE gene was significantly lower for healthy males (8.8%) compared with patients (17.7%) and the values for the European population according to ExAc database (13.7%) and dbSNP (17.2%). Analysis of c.3454G>C, and c.1545_1546delTA mutations in the CFTR gene revealed increased frequency (0.9 and 0.2%, respectively) in patients with infertility compared with data for the European population (0.04%, respectively (ExAc, European (Non-Finnish) and for the Aggregated Populations (0.002% (ExAc), because there is no data for European population for c.1545_1546delTA mutation. The frequency of del508 mutation (CFTR) in patients (1.59%) were lower comparing with male infertility Europeans (3.34-6.25% depending on nationality) and at the same level with healthy Europeans (1.06%, ExAc, European (Non-Finnish). Analysis of c.845G>A (HFE) mutation resulted in decreased frequency in patients (1.8%) in contrast with the European population data (5.1%, respectively, ExAc, European (Non-Finnish). Moreover, obtained data revealed no statistically significant frequency difference for c.845G>A mutation (HFE) between healthy males in the Russian and the European populations. Allele frequencies of mutations c.350G>A (CFTR), c.193A>T (HFE), c.774C>T, and c.80A>G (gene TSSK2) showed no significantly difference among patients with infertility, healthy males and Europeans. Analysis of AZF locus revealed increased frequency for AZFc microdeletion in patients with male infertility. Thereby, the new data of the allele frequencies in infertility patients in the Russian population was obtained. As well as the frequency differences of mutations associated with male infertility among patients, healthy males in the Russian population and the European one were estimated. The revealed differences showed that for high effectiveness of screening panel detecting genetically caused male infertility it is very important to consider ethnic and population characteristics of patients which will be screened.

Keywords: allele frequency, azoospermia, male infertility, mutation, population

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Authors: Nicolas Meylan, Eric Tardif

Abstract:

Many studies have highlighted the links between substance use and school difficulties. However, most of these studies address only the consumption in terms of frequency without considering the different types of behavior (use, abuse, dependence). Moreover, little is known about the associations between substance use and variables such as school engagement and school burnout recently described as a positive state of mind and an exhaustion syndrome related to school, respectively. Through this study, we wish to describe and compare school-related variables in adolescents with different type of substance use. Our study focuses on 402 Swiss adolescents, aged between 14 and 19 years old. They responded collectively and anonymously to a set of scales assessing substance use and several school variables (social support, stress, burnout, engagement and school climate). First, results on frequency and severity of substance use are relatively close to those observed in other studies. Second, it also appears that certain dimensions of stress, burnout, engagement and school climate are associated with the frequency of alcohol and cannabis consumption. Finally, adolescents’ substance abusers show particularly high scores of burnout, cynicism and stress related to workload, which can be understand as self-medication behavior. Additional analyzes are underway to clarify these associations. Results are discussed in terms of implications for research and clinical practice in academic burnout.

Keywords: school burnout, school engagement, adolescence, substance use, self-medication

Procedia PDF Downloads 293

Authors: Evangelia Kotrotsiou, Mairy Gouva, Theodosios Paralikas, Maria Fiaka, Styliani Kotrotsiou, Maria Malliarou

Abstract:

The purpose of this research is to investigate the way nurses perceive the care provided in comparison to the way patients perceive it, taking into account existing literature. As far as the sample of research is concerned, it has come from the population of nurses working in the General Hospital of Thessaloniki, St. Paul and the patients of its surgical clinic. In the present study, the sample consists of 100 nurses and 88 patients. The questionnaire used was the Caring Nurse-Patient Interactions Scale: 23-Item Version, created by Cossette et al. (2006). In the case of both patients and nurses, a high score was observed in relational care in the case of the frequency of nursing care in daily practice, as well as the satisfaction of providing nursing care. Overall, patients rated higher clinical care in the case of the frequency of nursing care in daily practice, as well as the satisfaction of the clinical care they were given. On the other hand, nurses rated higher comfort care in the case of the frequency of nursing care in everyday practice, as well as relational care in the area of the importance of nursing care in everyday practice.

Keywords: nursing care, patient needs, patient satisfaction, care giving

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Authors: M. Soltan Rezaee, M. R. Ghazavi, A. Najafi, W.-H. Liao

Abstract:

Generally, different rods in shaft systems can be misaligned based on the mechanical system usages. These rods can be linked together via U-coupling easily. The system is self-stimulated and may cause instabilities due to the inherent behavior of the coupling. In this study, each rod includes an elastic shaft with an angular stiffness and structural damping. Moreover, the mass of shafts is considered via attached solid disks. The impact of the system architecture and shaft mass on the instability of such mechanism are studied. Stability charts are plotted via a method based on Floquet theory. Eventually, the unstable points have been found and analyzed in detail. The results show that stabilizing the driveline is feasible by changing the system characteristics which include shaft mass and architecture.

Keywords: coupling, mechanical systems, oscillations, rotating shafts

Procedia PDF Downloads 163