Search results for: gas distribution network

Authors: Esra Ece Var

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Beekeeping plays essential role both in terms of agricultural yields and agricultural economy; they produce honey, wax, royal jelly, apitoxin, pollen, and propolis. Nowadays, these natural products become more importantly suitable and preferable for nutrition, food supplement, medicine, and industry. However, to produce organic honey, majority of the apiaries are located in remote or distant rural areas where utilities such as electricity and Internet network are not available. Additionally, due to colony failures, world honey production decreases year by year despite the increase in the number of beehives. The objective of this paper is to develop a smart beehive monitoring system for apiaries including those that do not have access to Internet network. In this context, temperature and humidity inside the beehive, and ambient temperature were measured with RFID sensors. Control center, where all sensor data was sent and stored at, has a GSM module used to warn the beekeeper via SMS when an anomaly is detected. Simultaneously, using the collected data, an unsupervised machine learning algorithm is used for detecting anomalies and calibrating the warning system. The results show that the smart beehive monitoring system can detect fatal anomalies up to 4 weeks prior to colony loss.

Keywords: beekeeping, smart systems, machine learning, anomaly detection, apiculture

Procedia PDF Downloads 247

Authors: S. Sakthivel

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Currently, the global economy is based on the hydrocarbon economy, which is referencing the global hydrocarbon industry. Problems of using these fossil fuels (like oil, NG, coal) are emitting greenhouse gases (GHGs) and price fluctuation, supply/distribution, etc. These challenges can be overcome by using clean energy as hydrogen. The hydrogen economy is the use of hydrogen as a low carbon fuel, particularly for hydrogen vehicles, alternative industrial feedstock, power generation, and energy storage, etc. Engineering consulting firms have a significant role in this ambition and green hydrogen value chain (i.e., integration of renewables, production, storage, and distribution to end-users). Typically, the cost of green hydrogen is a function of the price of electricity needed, the cost of the electrolyser, and the operating cost to run the system. This article focuses on economic viability and explores the alluring business scenarios globally. Break-even analysis was carried out for green hydrogen production and in order to evaluate and compare the impact of the electricity price on the production costs of green hydrogen and relate it to fossil fuel-based brown/grey/blue hydrogen costs. It indicates that the cost of green hydrogen production will fall drastically due to the declining costs of renewable electricity prices and along with the improvement and scaling up of electrolyser manufacturing. For instance, in a scenario where electricity prices are below US$ 40/MWh, green hydrogen cost is expected to reach cost competitiveness.

Keywords: green hydrogen, cost analysis, break-even analysis, renewables, electrolyzer

Procedia PDF Downloads 147

Authors: Taylor Kolody, Farkhund Iqbal, Rabia Batool, Benjamin Fung, Mohammed Hussaeni, Saiqa Aleem

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The rising threat of climate change has led to an increase in public awareness and care about our collective and individual environmental impact. A key component of this impact is our use of cars and other polluting forms of transportation, but it is often difficult for an individual to know how severe this impact is. While there are applications that offer this feedback, they require manual entry of what transportation mode was used for a given trip, which can be burdensome. In order to alleviate this shortcoming, a data from the 2016 TRIPlab datasets has been used to train a variety of machine learning models to automatically recognize the mode of transportation. The accuracy of 89.6% is achieved using single deep neural network model with Gated Recurrent Unit (GRU) architecture applied directly to trip data points over 4 primary classes, namely walking, public transit, car, and bike. These results are comparable in accuracy to results achieved by others using ensemble methods and require far less computation when classifying new trips. The lack of trip context data, e.g., bus routes, bike paths, etc., and the need for only a single set of weights make this an appropriate methodology for applications hoping to reach a broad demographic and have responsive feedback.

Keywords: classification, gated recurrent unit, recurrent neural network, transportation

Procedia PDF Downloads 141

Authors: Jyotismita Talukdar

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In this paper, a study has been made on the possibility and accuracy of early prediction of several Heart Disease using Artificial Neural Network. (ANN). The study has been made in both noise free environment and noisy environment. The data collected for this analysis are from five Hospitals. Around 1500 heart patient’s data has been collected and studied. The data is analysed and the results have been compared with the Doctor’s diagnosis. It is found that, in noise free environment, the accuracy varies from 74% to 92%and in noisy environment (2dB), the results of accuracy varies from 62% to 82%. In the present study, four basic attributes considered are Blood Pressure (BP), Fasting Blood Sugar (FBS), Thalach (THAL) and Cholesterol (CHOL.). It has been found that highest accuracy(93%), has been achieved in case of PPI( Post-Permanent-Pacemaker Implementation ), around 79% in case of CAD(Coronary Artery disease), 87% in DCM (Dilated Cardiomyopathy), 89% in case of RHD&MS(Rheumatic heart disease with Mitral Stenosis), 75 % in case of RBBB +LAFB (Right Bundle Branch Block + Left Anterior Fascicular Block), 72% for CHB(Complete Heart Block) etc. The lowest accuracy has been obtained in case of ICMP (Ischemic Cardiomyopathy), about 38% and AF( Atrial Fibrillation), about 60 to 62%.

Keywords: coronary heart disease, chronic stable angina, sick sinus syndrome, cardiovascular disease, cholesterol, Thalach

Procedia PDF Downloads 180

Authors: K. Durairaj, I. N. Umar

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The asynchronous discussion forum is one of the most widely used activities in learning management system environment. Online forum allows participants to interact, construct knowledge, and can be used to complement face to face sessions in blended learning courses. However, to what extent do the students perceive the benefits or advantages of forum remain to be seen. Through content and social network analyses, instructors will be able to gauge the students’ engagement and knowledge construction level. Thus, this study aims to analyze the students’ level of knowledge construction and their participation level that occur through online discussion. It also attempts to investigate the relationship between the level of knowledge construction and their social interaction patterns. The sample involves 23 students undertaking a master course in one public university in Malaysia. The asynchronous discussion forum was conducted for three weeks as part of the course requirement. The finding indicates that the level of knowledge construction is quite low. Also, the density value of 0.11 indicating that the overall communication among the participants in the forum is low. This study reveals that strong and significant correlations between SNA measures (in-degree centrality, out-degree centrality) and level of knowledge construction. Thus, allocating these active students in a different groups aids the interactive discussion takes place. Finally, based upon the findings, some recommendations to increase students’ level of knowledge construction and also for further research are proposed.

Keywords: asynchronous discussion forums, content analysis, knowledge construction, social network analysis

Procedia PDF Downloads 378

Authors: Zunaidah Ab Hasan, Azhana Othman, Abd Halim Mohd Noor, Nor Shahrina Mohd Rafien

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Zakat is a portion of wealth contributed from financially able Muslims to be distributed to predetermine recipients; main among them are the poor and the needy. Distribution of the zakat fund is given with the objective to lift the recipients from poverty. Due to the multidimensional and multifaceted nature of poverty, it is imperative that the causes of poverty are properly identified for assistance given by zakat authorities reached the intended target. Despite, various studies undertaken to identify the poor correctly, there are reports of the poor not receiving the adequate assistance required from zakat. Thus, this study examines the determinants of poverty among applicants for zakat assistance distributed by the State Islamic Religious Council in Malacca (SIRCM). Malacca is a state in Malaysia. The respondents were based on the list of names of new zakat applicants for the month of April and May 2014 provided by SIRCM. A binary logistic regression was estimated based on this data with either zakat applications is rejected or accepted as the dependent variable and set of demographic variables and health as the explanatory variables. Overall, the logistic model successfully predicted factors of acceptance of zakat applications. Three independent variables namely gender, age; size of households and health significantly explain the likelihood of a successful zakat application. Among others, the finding suggests the importance of focusing on providing education opportunity in helping the poor.

Keywords: logistic regression, zakat distribution, status of zakat applications, poverty, education

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Authors: A. E. Kobryn

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We present both descriptive and predictive modeling of structural properties of blends of PCBM or organic-inorganic hybrid perovskites of the type CH3NH3PbX3 (X=Cl, Br, I) with P3HT, P3BT or squaraine SQ2 dye sensitizer, including adsorption on TiO2 clusters having rutile (110) surface. In our study, we use a methodology that allows computing the microscopic structure of blends on the nanometer scale and getting insight on miscibility of its components at various thermodynamic conditions. The methodology is based on the integral equation theory of molecular liquids in the reference interaction site representation/model (RISM) and uses the universal force field. Input parameters for RISM, such as optimized molecular geometries and charge distribution of interaction sites, are derived with the use of the density functional theory methods. To compare the diffusivity of the PCBM in binary blends with P3HT and P3BT, respectively, the study is complemented with MD simulation. A very good agreement with experiment and the reports of alternative modeling or simulation is observed for PCBM in P3HT system. The performance of P3BT with perovskites, however, seems as expected. The calculated nanoscale morphologies of blends of P3HT, P3BT or SQ2 with perovskites, including adsorption on TiO2, are all new and serve as an instrument in rational design of organic/hybrid photovoltaics. They are used in collaboration with experts who actually make prototypes or devices for practical applications.

Keywords: multiscale theory and modeling, nanoscale morphology, organic-inorganic halide perovskites, three dimensional distribution

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Authors: M. Moslehpour, S. Khorsandi

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Due to shortage in IPv4 addresses, transition to IPv6 has gained significant momentum in recent years. Like Address Resolution Protocol (ARP) in IPv4, Neighbor Discovery Protocol (NDP) provides some functions like address resolution in IPv6. Besides functionality of NDP, it is vulnerable to some attacks. To mitigate these attacks, Internet Protocol Security (IPsec) was introduced, but it was not efficient due to its limitation. Therefore, SEND protocol is proposed to automatic protection of auto-configuration process. It is secure neighbor discovery and address resolution process. To defend against threats on NDP’s integrity and identity, Cryptographically Generated Address (CGA) and asymmetric cryptography are used by SEND. Besides advantages of SEND, its disadvantages like the computation process of CGA algorithm and sequentially of CGA generation algorithm are considerable. In this paper, we parallel this process between network resources in order to improve it. In addition, we compare the CGA generation time in self-computing and distributed-computing process. We focus on the impact of the malicious nodes on the CGA generation time in the network. According to the result, although malicious nodes participate in the generation process, CGA generation time is less than when it is computed in a one-way. By Trust Management System, detecting and insulating malicious nodes is easier.

Keywords: NDP, IPsec, SEND, CGA, modifier, malicious node, self-computing, distributed-computing

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Authors: Katawut Kaewbanjong

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We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably.

Keywords: prediction model, statistical analysis, software project, user satisfaction factor

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Authors: V. Hashim, P. N. Dileep

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Stent implantation is being seen as the most successful method to treat coronary artery diseases. Different types of stents are available in the market these days and the success of a stent implantation greatly depends on the proper selection of a suitable stent for a patient. Computer numerical simulation is the cost effective way to choose the compatible stent. Studies confirm that the design characteristics of stent do have great importance with regards to the pressure it can sustain, the maximum displacement it can produce, the developed stress concentration and so on. In this paper different designs of stent were analyzed together with balloon to optimize the stent and balloon system. Commercially available stent Palmaz-Schatz has been selected for analysis. Abaqus software is used to simulate the system. This work is the finite element analysis of the artery stent implant to find out the design factors affecting the stress and strain. The work consists of two phases. In the first phase, stress distribution of three models were compared - stent without balloon, stent with balloon of equal length and stent with balloon of extra length than stent. In second phase, three different design models of Palmaz-Schatz stent were compared by keeping the balloon length constant. The results obtained from analysis shows that, the design of the strut have strong effect on the stress distribution. A design with chamfered slots found better results. The length of the balloon also has influence on stress concentration of the stent. Increase in length of the balloon will reduce stress, but will increase dog boning effect.

Keywords: coronary stent, finite element analysis, restenosis, stress concentration

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Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

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2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: artificial intelligence, machine learning, deep learning, convolutional neural networks

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Authors: Yue Yang, Qiang Wu, Xingyu Gao

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"Organized research units" have always been an important part of academia. According to the type of organization, there are public research units, university research units, and corporate research units. Existing research has explored the research unit in some depth from several perspectives. However, there is a research gap on the closer interaction between the three from a network perspective and the impact of this interaction on their performance as well as originality. Cliques are a special kind of structure under the concept of cohesive subgroups in the field of social networks, representing particularly tightly knit teams in a network. This study develops the concepts of the diversity of clique types and the diversity of clique geography based on cliques, starting from the diversity of collaborative activities characterized by them. Taking research units as subjects and assigning values to their power in cliques based on occupational age, we explore the impact of clique diversity and clique power on their performance as well as originality and the moderating role of clique relationship strength and structural holes in them. By collecting 9094 articles published in the field of quantum communication at WoSCC over the 15 years 2007-2021, we processed them to construct annual collaborative networks between a total of 533 research units and measured the network characteristic variables using Ucinet. It was found that the type and geographic diversity of cliques promoted the performance and originality of the research units, and the strength of clique relationships positively moderated the positive effect of the diversity of clique types on performance and negatively affected the promotional relationship between the geographic diversity of cliques and performance. It also negatively affected the positive effects of clique-type diversity and clique-geography diversity on originality. Structural holes positively moderated the facilitating effect of both types of factional diversity on performance and originality. Clique power promoted the performance of the research unit, but unfavorably affected its performance on novelty. Faction relationship strength facilitated the relationship between faction rights and performance and showed negative insignificance for clique power and originality. Structural holes positively moderated the effect of clique power on performance and originality.

Keywords: research unit, social networks, clique structure, clique power, diversity

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Authors: Yimeng Zhang

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Major depressive disorder (MDD) is a prevalent mental health condition with a substantial economic impact and limited treatment options. Acupuncture has gained attention as a promising non-pharmacological intervention for alleviating depressive symptoms. However, its mechanisms and clinical effectiveness remain incompletely understood. This meta-analysis aims to (1) synthesize existing evidence on the mechanisms and clinical effectiveness of acupuncture for depression and (2) compare these findings with pharmacological interventions, providing insights for future research. Evidence from animal models and clinical studies indicates that acupuncture may enhance hippocampal and network neuroplasticity and reduce brain inflammation, potentially alleviating depressive disorders. Clinical studies suggest that acupuncture can effectively relieve primary depression, particularly in milder cases, and is beneficial in managing post-stroke depression, pain-related depression, and postpartum depression, both as a standalone and adjunctive treatment. Notably, combining acupuncture with antidepressant pharmacotherapy appears to enhance treatment outcomes and reduce medication side effects, addressing a critical issue in conventional drug therapy's high dropout rates. This meta-analysis, encompassing 12 studies and 710 participants, draws data from eight digital databases (PubMed, EMBASE, Web of Science, EBSCOhost, CNKI, CBM, Wangfang, and CQVIP) covering the period from 2012 to 2022. Utilizing Stata software 15.0, the meta-analysis employed random-effects and fixed-effects models to assess the distribution of depression in Traditional Chinese Medicine (TCM). The results underscore the substantial evidence supporting acupuncture's beneficial effects on depression. However, the small sample sizes of many clinical trials raise concerns about the generalizability of the findings, indicating a need for further research to validate these outcomes and optimize acupuncture's role in treating depression.

Keywords: Chinese medicine, acupuncture, depression, meta-analysis

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Authors: Manasi Roy, Raju Mondal

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During last two decades the synthesis and design of MOFs or novel coordination polymers (CPs) has flourished as an emerging area of research due to their role as functional materials. Accordingly, ten new cobalt-based MOFs have been synthesized using a simple bispyrazole ligand, 4,4′-methylene-bispyrazole (H2MBP), and isophthalic acid (H2IPA) and its four 5-substituted derivatives R-H2IPA (R = COOH, OH, tBu, NH2). The major aim of this study was to validate the mutual influence of temperature and substitutions on the final structural self-assembly. Five different isophthalic acid derivatives were used to study the influence of substituents while each reaction was carried out at two different temperatures to assess the temperature effect. A clear correlation was observed between the reaction temperature and the coordination number of the cobalt atoms which consequently changes the self assembly pattern. Another fact that the periodical change in coordination number did bring about some systematic changes in the structural network via secondary building unit selectivity. With the presence of a tunable cavity inside the network, and unsaturated metal centers, MOFs show highly encouraging photocatalytic degradation of toxic dye with a potential application in waste water purification. Another fascinating aspect of this work is the construction of magnetic coordination polymers with the occurrence of a not-so-common MCE behavior of cobalt-based MOF.

Keywords: MOFs, temperature effect, MCE, dye degradation

Procedia PDF Downloads 140

Authors: David Oluigbo, Erik Hemberg, Nathan Shwatal, Wenqi Ding, Yin Yuan, Susanna Mierau

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Introduction: Calcium imaging is an established technique in neuroscience research for detecting activity in neural networks. Bursts of action potentials in neurons lead to transient increases in intracellular calcium visualized with fluorescent indicators. Manual identification of cell bodies and their contours by experts typically takes 10-20 minutes per calcium imaging recording. Our aim, therefore, was to design an automated pipeline to facilitate and optimize calcium imaging data analysis. Our pipeline aims to accelerate cell body and contour identification and production of graphical representations reflecting changes in neuronal calcium-based fluorescence. Methods: We created a Python-based pipeline that uses OpenCV (a computer vision Python package) to accurately (1) detect neuron contours, (2) extract the mean fluorescence within the contour, and (3) identify transient changes in the fluorescence due to neuronal activity. The pipeline consisted of 3 Python scripts that could both be easily accessed through a Python Jupyter notebook. In total, we tested this pipeline on ten separate calcium imaging datasets from murine dissociate cortical cultures. We next compared our automated pipeline outputs with the outputs of manually labeled data for neuronal cell location and corresponding fluorescent times series generated by an expert neuroscientist. Results: Our results show that our automated pipeline efficiently pinpoints neuronal cell body location and neuronal contours and provides a graphical representation of neural network metrics accurately reflecting changes in neuronal calcium-based fluorescence. The pipeline detected the shape, area, and location of most neuronal cell body contours by using binary thresholding and grayscale image conversion to allow computer vision to better distinguish between cells and non-cells. Its results were also comparable to manually analyzed results but with significantly reduced result acquisition times of 2-5 minutes per recording versus 10-20 minutes per recording. Based on these findings, our next step is to precisely measure the specificity and sensitivity of the automated pipeline’s cell body and contour detection to extract more robust neural network metrics and dynamics. Conclusion: Our Python-based pipeline performed automated computer vision-based analysis of calcium image recordings from neuronal cell bodies in neuronal cell cultures. Our new goal is to improve cell body and contour detection to produce more robust, accurate neural network metrics and dynamic graphs.

Keywords: calcium imaging, computer vision, neural activity, neural networks

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Authors: Sakiru Badmos, David R. Cole, Alberto Striolo

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It is known that confinement in nm-size pores affects many structural and transport properties of water and co-existing volatile species. Of particular interest for fluids in sub-surface systems, in catalysis, and in separations are reports that confinement can enhance the solubility of gases in water. Equilibrium molecular dynamics simulations were performed for aqueous H₂S confined in slit-shaped silica pores at 313K. The effect of pore width on the H₂S solubility in water was investigated. Other properties of interest include the molecular distribution of the various fluid molecules within the pores, the hydration structure for solvated H₂S molecules, and the dynamical properties of the confined fluids. The simulation results demonstrate that confinement reduces the H₂S solubility in water and that the solubility increases with pore size. Analysis of spatial distribution functions suggests that these results are due to perturbations on the coordination of water molecules around H₂S due to confinement. Confinement is found to dampen the dynamical properties of aqueous H₂S as well. Comparing the results obtained for aqueous H₂S to those reported elsewhere for aqueous CH₄, it can be concluded that H₂S permeates hydrated slit-shaped silica nano-pores faster than CH₄. In addition to contributing to better understanding the behavior of fluids in subsurface formations, these observations could also have important implications for developing new natural gas sweetening technologies.

Keywords: confinement, interfacial properties, molecular dynamic simulation, sub-surface formations

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Authors: Kavindan Balakrishnan

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Root cause Identification of the Vibration phenomenon in a feedwater pumping station was the main objective of this research. First, the mode shapes of the pumping structure were investigated using numerical and analytical methods. Then the flow pressure and streamline distribution in the pump sump were examined using C.F.D. simulation, which was hypothesized can be a cause of vibration in the pumping station. As the problem specification of this research states, the vibration phenomenon in the pumping station, with four parallel pumps operating at the same time and heavy vibration recorded even after several maintenance steps. They also specified that a relatively large amplitude of vibration exited by pumps 1 and 4 while others remain normal. As a result, the focus of this research was on determining the cause of such a mode of vibration in the pump station with the assistance of Finite Element Analysis tools and Analytical methods. Major outcomes were observed in structural behavior which is favorable to the vibration pattern phenomenon in the pumping structure as a result of this research. Behaviors of the numerical and analytical models of the pump structure have similar characteristics in their mode shapes, particularly in their 2nd mode shape, which is considerably related to the exact cause of the research problem statement. Since this study reveals several possible points of flow visualization in the pump sump model that can be a favorable cause of vibration in the system, there is more room for improved investigation on flow conditions relating to pump vibrations.

Keywords: vibration, simulation, analysis, Ansys, Matlab, mode shapes, pressure distribution, structure

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Authors: Apu Kumar Saha, Mrinmoy Majumder

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The increase in the concentration of Green House gases all over the World has induced global warming phenomena whereby the average temperature of the world has aggravated to impact the pattern of climate in different regions. The frequency of extreme event has increased, early onset of season and change in an average amount of rainfall all are engrossing the conclusion that normal pattern of climate is changing. Sophisticated and complex models are prepared to estimate the future situation of the climate in different zones of the Earth. As hydro-energy is directly related to climatic parameters like rainfall and evaporation such energy resources will have to sustain the onset of the climatic abnormalities. The present investigation has tried to assess the impact of climatic abnormalities upon hydropower potential of different regions of the World. In this regard multi-criteria, decision making, and the neural network is used to predict the impact of the change cognitively by an index. The results from the study show that hydro-energy potential of Asian region is mostly vulnerable with respect to other regions of the world. The model results also encourage further application of the index to analyze the impact of climate change on the potential of hydro-energy.

Keywords: hydro-energy potential, neural networks, multi criteria decision analysis, environmental and ecological engineering

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Authors: Petr Gurný

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One of the most important tasks in the risk management is the correct determination of probability of default (PD) of particular financial subjects. In this paper a possibility of determination of financial institution’s PD according to the credit-scoring models is discussed. The paper is divided into the two parts. The first part is devoted to the estimation of the three different models (based on the linear discriminant analysis, logit regression and probit regression) from the sample of almost three hundred US commercial banks. Afterwards these models are compared and verified on the control sample with the view to choose the best one. The second part of the paper is aimed at the application of the chosen model on the portfolio of three key Czech banks to estimate their present financial stability. However, it is not less important to be able to estimate the evolution of PD in the future. For this reason, the second task in this paper is to estimate the probability distribution of the future PD for the Czech banks. So, there are sampled randomly the values of particular indicators and estimated the PDs’ distribution, while it’s assumed that the indicators are distributed according to the multidimensional subordinated Lévy model (Variance Gamma model and Normal Inverse Gaussian model, particularly). Although the obtained results show that all banks are relatively healthy, there is still high chance that “a financial crisis” will occur, at least in terms of probability. This is indicated by estimation of the various quantiles in the estimated distributions. Finally, it should be noted that the applicability of the estimated model (with respect to the used data) is limited to the recessionary phase of the financial market.

Keywords: credit-scoring models, multidimensional subordinated Lévy model, probability of default

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Authors: Ahmet Kayabasi, Ali Akdagli

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In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457%, 0.399% and 0.600%, respectively. The constructed models were then tested and APE values as 0.601% for ANN, 0.744% for ANFIS and 0.623% for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results.

Keywords: a-shaped compact microstrip antenna, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM)

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Authors: Majid Mathlouthi, Fethi Lebdi

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Throughout the world, the extent and magnitude of droughts have economic, social and environmental consequences. Today climate change has become more and more felt; most likely they increase the frequency and duration of droughts. An analysis by event of dry event, from series of observations of the daily rainfall is carried out. A daily precipitation threshold value has been set. A catchment localized in Northern Tunisia where the average rainfall is about 600 mm has been studied. Rainfall events are defined as an uninterrupted series of rainfall days understanding at least a day having received a precipitation superior or equal to a fixed threshold. The dry events are constituted of a series of dry days framed by two successive rainfall events. A rainfall event is a vector of coordinates the duration, the rainfall depth per event and the duration of the dry event. The depth and duration are found to be correlated. So we use conditional probabilities to analyse the depth per event. The negative binomial distribution fits well the dry event. The duration of the rainfall event follows a geometric distribution. The length of the climatically cycle adjusts to the Incomplete Gamma. Results of this analysis was used to study of the effects of climate change on water resources and crops and to calibrate precipitation models with little rainfall records. In response to long droughts in the basin, the drought management system is based on three phases during each of the three phases; different measurements are applied and executed. The first is before drought, preparedness and early warning; the second is drought management, mitigation in the event of drought; and the last subsequent drought, when the drought is over.

Keywords: dry spell, precipitation threshold, climate vulnerability, adaptation measures

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Authors: Sergio Curilef, Boris Atenas

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Systems with long-range interactions are very common in nature. They are observed from the atomic scale to the astronomical scale and exhibit anomalies, such as inequivalence of ensembles, negative heat capacity, ergodicity breaking, nonequilibrium phase transitions, quasistationary states, and anomalous diffusion. These anomalies are exacerbated when special initial conditions are imposed; in particular, we use the so-called water bag initial conditions that stand for a uniform distribution. Several theoretical and practical implications are discussed here. A potential energy inspired by dipole-dipole interactions is proposed to build the dipole-type Hamiltonian mean-field model. As expected, the dynamics is novel and general to the behavior of systems with long-range interactions, which is obtained through molecular dynamics technique. Two plateaus sequentially emerge before arriving at equilibrium, which are corresponding to two different quasistationary states. The first plateau is a type of quasistationary state the lifetime of which depends on a power law of N and the second plateau seems to be a true quasistationary state as reported in the literature. The general behavior of the model according to its dynamics and thermodynamics is described. Using numerical simulation we characterize the mean kinetic energy, caloric curve, and the diffusion law through the mean square of displacement. The present challenge is to characterize the distributions in phase space. Certainly, the equilibrium state is well characterized by the Gaussian distribution, but quasistationary states in general depart from any Gaussian function.

Keywords: dipole-type interactions, dynamics and thermodynamics, mean field model, quasistationary states

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Authors: Thu Thuy Pham, Thi Chau Loan Tran, Van Duy Nguyen

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Marine fungi play an important role in the marine ecosystems. Marine fungi also supply biomass and metabolic products of industrial value. Currently, the biodiversity of marine fungi along the coastal areas of Vietnam has not yet been studied fully. The objective of this study is to assess the spatial and temporal diversity of planktonic fungi from the coastal waters of Nha Trang Bay and Van Phong Bay in Central Vietnam using culture-dependent and independent approach. Using culture-dependent approach, filamentous fungi and yeasts were isolated on selective media and then classified by phenotype and genotype based on the sequencing of ITS (internal transcribed spacers) regions of rDNA with two primer pairs (ITS1F_KYO2 and ITS4; NS1 and NS8). Using culture-independent approach, environmental DNA samples were isolated and amplified using fungal-specific ITS primer pairs. A total of over 160 strains were isolated from 10 seawater sampling stations at 50 cm depth. They were classified into diverse genera and species of both yeast and mold. At least 5 strains could be potentially novel species. Our results also revealed that planktonic fungi were molecularly diverse with hundreds of phylotypes recovered across these two bays. The results of the study provide data about the distribution and taxonomy of mycoplankton in this area, thereby allowing assessment of their positive role in the biogeochemical cycle of coastal ecosystems and the development of new bioactive compounds for industrial applications.

Keywords: biodiversity, ITS, marine fungi, Nha Trang Bay, Van Phong Bay

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Authors: Amanda Kavner, Richard Lamb

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Faster growth in science and technology of other nations may make staying globally competitive more difficult without shifting focus on how science is taught in US classes. An integral part of learning science involves visual and spatial thinking since complex, and real-world phenomena are often expressed in visual, symbolic, and concrete modes. The primary barrier to spatial thinking and visual literacy in Science, Technology, Engineering, and Math (STEM) fields is representational competence, which includes the ability to generate, transform, analyze and explain representations, as opposed to generic spatial ability. Although the relationship is known between the foundational visual literacy and the domain-specific science literacy, science literacy as a function of science learning is still not well understood. Moreover, the need for a more reliable measure is necessary to design resources which enhance the fundamental visuospatial cognitive processes behind scientific literacy. To support the improvement of students’ representational competence, first visualization skills necessary to process these science representations needed to be identified, which necessitates the development of an instrument to quantitatively measure visual literacy. With such a measure, schools, teachers, and curriculum designers can target the individual skills necessary to improve students’ visual literacy, thereby increasing science achievement. This project details the development of an artificial neural network capable of measuring science literacy using functional Near-Infrared Spectroscopy (fNIR) data. This data was previously collected by Project LENS standing for Leveraging Expertise in Neurotechnologies, a Science of Learning Collaborative Network (SL-CN) of scholars of STEM Education from three US universities (NSF award 1540888), utilizing mental rotation tasks, to assess student visual literacy. Hemodynamic response data from fNIRsoft was exported as an Excel file, with 80 of both 2D Wedge and Dash models (dash) and 3D Stick and Ball models (BL). Complexity data were in an Excel workbook separated by the participant (ID), containing information for both types of tasks. After changing strings to numbers for analysis, spreadsheets with measurement data and complexity data were uploaded to RapidMiner’s TurboPrep and merged. Using RapidMiner Studio, a Gradient Boosted Trees artificial neural network (ANN) consisting of 140 trees with a maximum depth of 7 branches was developed, and 99.7% of the ANN predictions are accurate. The ANN determined the biggest predictors to a successful mental rotation are the individual problem number, the response time and fNIR optode #16, located along the right prefrontal cortex important in processing visuospatial working memory and episodic memory retrieval; both vital for science literacy. With an unbiased measurement of science literacy provided by psychophysiological measurements with an ANN for analysis, educators and curriculum designers will be able to create targeted classroom resources to help improve student visuospatial literacy, therefore improving science literacy.

Keywords: artificial intelligence, artificial neural network, machine learning, science literacy, neuroscience

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Authors: Nahed Mohamed Ismail, Bayomy Mostafa, Ahmed Abdel Kader, Ahmed Mohamed Azzam

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Effect of lining watercourses on the distribution and abundance of fresh water snails at two Egyptian governorates, Baheria (new reclaimed area) and Giza was studied. Seasonal survey in lined and unlined sites during two successive years was carried out. Samples of snails and water were collected from each examined site and the ecological conditions were recorded. The collected snails from each site were placed in plastic aquaria and transferred to the laboratory, where they were sorted out, identified, counted and examined for natural infection. The size frequency distribution was calculated for each snail species. Results revealed that snails were represented in all examined watercourses (lined and unlined) at the two tested habitats by 14 species. (Biomphalaria alexandrina, B. glabrata, Bulinus truncatus, Physa acuta. Helisoma duryi, Lymnaea natalensis, Planorbis planorbis, Cleopatra bulimoids, Lanistes carinatus, Bellamya unicolor, Melanoides tuberculata, Theodoxus nilotica, Succinia cleopatra and Gabbiella senaarensis). During spring, the percentage of live (45%) and dead (55%) snail species was extremely highly significant lower (p>0.001) in lined water bodies compared to the unlined ones (93.5% and 6.5%, respectively) in the examined sites at Baheria. At Giza, the percentage values of live snail species from all lined watercourses (82.6% and 60.2%, during winter and spring, respectively) was significantly lower (p>0.05 & p>0.01) than those in unlined ones (91.1% and 79%, respectively). Size frequency distribution of snails collected from the lined and unlined water bodies at Baheria and Giza governorates during all seasons revealed that during survey, snail populations were stable and the recruitment of young to adult was continuing for some species, where the recruits were observed with adults. However, there was no sign of small snails occurrence in case of B. glabrata and B. alexandrina during autumn, winter and spring and disappear during summer at Giza. Meanwhile they completely absent during all seasons at Baheria Governorate. Chemical analysis of some heavy metals of water samples collected from lined and unlined sites from Baheria and Giza governorates during autumn, winter and spring were approximately as the same in both lined and unlined water bodies. However, Zn and Fe were higher in lined sites (0.78±0.37and 17.4 ± 4.3, respectively) than that of unlined ones (0.4±0.1 and 10.95 ± 1.93, respectively) and Cu was absent in both lined and unlined sites during summer at Baheria governorate. At Giza, Cu and Pb were absent and Fe were higher in lined sites (4.7± 4.2) than that of unlined ones (2.5 ± 1.4) during summer. Statistical analysis showed that no significant difference in all physico-chemical parameters of water in lined and unlined water bodies at the two tested habitats during all seasons. However, it was found that the water conductivity and TDS showed a lower mean values in lined sites than those of unlined ones. Thus, the present obtained data support the concept of utilizing environmental modification such as lining of water courses to help in minimizing the population density of certain vector snails and consequently reduce the transmission of snails born diseases.

Keywords: lining, fresh water, snails, watercourses

Procedia PDF Downloads 257

Authors: Justyna Skrzyńska, Zdzisław Kobos, Zbigniew Wochyński

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The assessment of characterologic type is an essential element which decides about the proper task performance in the Air Forces. The aim of the research was to specify the percentage distribution of characterologic features by cadets studying particular courses in Polish Air Force Academy with the use of questionnaire. 34 first-year cadets chosen by lot and disunited into aircrafts pilots (N-10), helicopter pilots (N-13) and navigators(N-11) participated in the research. All of the questioned have had their psychomotor education examined in Military Aviation Medicine Institute in Warsaw, Poland. Moreover all of them are characterised by very good fitness. In the research, an anonymous poll(based on Myers-Briggs Type Indicator) appraising cadets’ characterologic type has been used. Cadets were provided with the same accommodation and nutrition. The findings have shown that percentage distribution was diversified, however it could be distinctly observed that most of future helicopter pilots (69%) are introverts whereas the majority of aircrafts pilots (70%) and navigators (100%) are extraverts. Moreover, it was also observed that 70% of cadets studying aircrafts pilotage run regular lifestyle and have judging skill according to Myers-Briggs Type Indicator. In future navigators group, 73% of students do not have this characteristic. The research has shown that cadets studying pilotage are more likely to demonstrate the characteristics which are essential for a performance of the important tasks in pilots environment than the cadets studying navigation.

Keywords: pilot, Myers-Briggs Type indicator, questionnaire research, cadets, psychomotor education

Procedia PDF Downloads 485

Authors: Mokhtar Djeddou, Amine Mehel, Georges Fokoua, Anne Tanière, Patrick Chevrier

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Commuters' exposure to air pollution, particularly to particle matter, inside vehicles is a significant health issue. Assessing particles concentrations and characterizing their distribution is an important first step to understand and propose solutions to improve car cabin air quality. It is known that particles dynamics is intimately driven by particles-turbulence interactions. In order to analyze and model pollutants distribution inside the car the cabin, it is crucialto examine first the single-phase flow topology and turbulence characteristics. Within this context, Computational Fluid Dynamics (CFD) simulations were conducted to model airflow inside a full-scale car cabin using Reynolds Averaged Navier-Stokes (RANS)approach combined with the first order Realizable k- εmodel to close the RANS equations. To validate the numerical model, a campaign of velocity field measurements at different locations in the front and back of the car cabin has been carried out using hot-wire anemometry technique. Comparison between numerical and experimental results shows a good agreement of velocity profiles. Additionally, visualization of streamlines shows the formation of jet flow developing out of the dashboard air vents and the formation of large vortex structures, particularly in the back seats compartment. These vortex structures could play a key role in the accumulation and clustering of particles in a turbulent flow

Keywords: car cabin, CFD, hot wire anemometry, vortical flow

Procedia PDF Downloads 299

Authors: Y. Zheng, M. Busi, A. F. Pedersen, M. A. Beltran, C. Gundlach

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X-ray phase contrast imaging has shown to yield a better contrast compared to conventional attenuation X-ray imaging, especially for soft tissues in the medical imaging energy range. This can potentially lead to better diagnosis for patients. However, phase contrast imaging has mainly been performed using highly brilliant Synchrotron radiation, as it requires high coherence X-rays. Many research teams have demonstrated that it is also feasible using a laboratory source, bringing it one step closer to clinical use. Nevertheless, the requirement of fine gratings and high precision stepping motors when using a laboratory source prevents it from being widely used. Recently, a random phase object has been proposed as an analyzer. This method requires a much less robust experimental setup. However, previous studies were done using a particular X-ray source (liquid-metal jet micro-focus source) or high precision motors for stepping. We have been working on a much simpler setup with just small modification of a commercial bench-top micro-CT (computed tomography) scanner, by introducing a piece of sandpaper as the phase analyzer in front of the X-ray source. However, it needs a suitable algorithm for speckle tracking and 3D reconstructions. The precision and sensitivity of speckle tracking algorithm determine the resolution of the system, while the 3D reconstruction algorithm will affect the minimum number of projections required, thus limiting the temporal resolution. As phase contrast imaging methods usually require much longer exposure time than traditional absorption based X-ray imaging technologies, a dynamic phase contrast micro-CT with a high temporal resolution is particularly challenging. Different reconstruction methods, including neural network based techniques, will be evaluated in this project to increase the temporal resolution of the phase contrast micro-CT. A Monte Carlo ray tracing simulation (McXtrace) was used to generate a large dataset to train the neural network, in order to address the issue that neural networks require large amount of training data to get high-quality reconstructions.

Keywords: micro-ct, neural networks, reconstruction, speckle-based x-ray phase contrast

Procedia PDF Downloads 261

Authors: Josiane Yaguibou, Ngoy Kishimba, Issiaka V. Coulibaly, Sabrina Pestilli, Falinirina Razanalison, Hantanirina Andremanisa

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Background: Madagascar has one of the least developed road networks in the world with a majority of its national and local roads being earth roads and in poor condition. In addition, the country is affected by frequent natural disasters that further affect the road conditions limiting the accessibility to some parts of the country. In 2021 and 2022, 2.21 million people were affected by drought in the Grand Sud region, and by cyclones and floods in the coastal regions, with disruptions of the health system including last mile distribution of lifesaving maternal health commodities and reproductive health commodities in the health facilities. Program intervention: The intervention uses drone technology to deliver maternal health and family planning commodities in hard-to-reach health facilities in the Grand Sud and Sud-Est of Madagascar, the regions more affected by natural disasters. Methodology The intervention was developed in two phases. A first phase, conducted in the Grand Sud, used drones leased from a private company to deliver commodities in isolated health facilities. Based on the lesson learnt and encouraging results of the first phase, in the second phase (2023) the intervention has been extended to the Sud Est regions with the purchase of drones and the recruitment of pilots to reduce costs and ensure sustainability. Key findings: The drones ensure deliveries of lifesaving commodities in the Grand Sud of Madagascar. In 2023, 297 deliveries in commodities in forty hard-to-reach health facilities have been carried out. Drone technology reduced delivery times from the usual 3 - 7 days necessary by road or boat to only a few hours. Program Implications: The use of innovative drone technology demonstrated to be successful in the Madagascar context to reduce dramatically the distribution time of commodities in hard-to-reach health facilities and avoid stockouts of life-saving medicines. When the intervention reaches full scale with the completion of the second phase and the extension in the Sud-Est, 150 hard-to-reach facilities will receive drone deliveries, avoiding stockouts and improving the quality of maternal health and family planning services offered to 1,4 million people in targeted areas.

Keywords: commodities, drones, last-mile distribution, lifesaving supplies

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Authors: Mohammad Besharatloo

Abstract:

Intrusion detection is an important research topic in network security because of increasing growth in the use of computer network services. Intrusion detection is done with the aim of detecting the unauthorized use or abuse in the networks and systems by the intruders. Therefore, the intrusion detection system is an efficient tool to control the user's access through some predefined regulations. Since, the data used in intrusion detection system has high dimension, a proper representation is required to show the basis structure of this data. Therefore, it is necessary to eliminate the redundant features to create the best representation subset. In the proposed method, a hybrid model of differential evolution and firefly algorithms was employed to choose the best subset of properties. In addition, decision tree and support vector machine (SVM) are adopted to determine the quality of the selected properties. In the first, the sorted population is divided into two sub-populations. These optimization algorithms were implemented on these sub-populations, respectively. Then, these sub-populations are merged to create next repetition population. The performance evaluation of the proposed method is done based on KDD Cup99. The simulation results show that the proposed method has better performance than the other methods in this context.

Keywords: intrusion detection system, differential evolution, firefly algorithm, support vector machine, decision tree

Procedia PDF Downloads 97