Search results for: data mining applications and discovery

Authors: Laraib Niaz

Abstract:

This paper analyzes the ways in which education and economic deprivation are linked to terrorism in Pakistan using data for terrorist incidents from the Global Terrorism Database (GTD). It employs the technique of negative binomial regression for the years between 1990 and 2013, presenting evidence for a positive association between education and terrorism. Conversely, a negative correlation with economic deprivation is signified in the results. The study highlights the element of radicalization as witnessed in the curriculum and textbooks of public schools as a possible reason for extremism, which in turn may lead to terrorism.

Keywords: education, Pakistan, terrorism, poverty

Procedia PDF Downloads 377

Authors: Joy Cao, Min Zhou

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Purpose: Acute Type A aortic dissection is a well-known cause of extremely high mortality rate. A highly accurate and cost-effective non-invasive predictor is critically needed so that the patient can be treated at earlier stage. Although various CFD approaches have been tried to establish some prediction frameworks, they are sensitive to uncertainty in both image segmentation and boundary conditions. Tedious pre-processing and demanding calibration procedures requirement further compound the issue, thus hampering their clinical applicability. Using the latest physics informed deep learning methods to establish an accurate and cost-effective predictor framework are amongst the main goals for a better Type A aortic dissection treatment. Methods: Via training a novel physics-informed deep residual network, with non-invasive 4D MRI displacement vectors as inputs, the trained model can cost-effectively calculate all these biomarkers: aortic blood pressure, WSS, and OSI, which are used to predict potential type A aortic dissection to avoid the high mortality events down the road. Results: The proposed deep learning method has been successfully trained and tested with both synthetic 3D aneurysm dataset and a clinical dataset in the aortic dissection context using Google colab environment. In both cases, the model has generated aortic blood pressure, WSS, and OSI results matching the expected patient’s health status. Conclusion: The proposed novel physics-informed deep residual network shows great potential to create a cost-effective, non-invasive predictor framework. Additional physics-based de-noising algorithm will be added to make the model more robust to clinical data noises. Further studies will be conducted in collaboration with big institutions such as Cleveland Clinic with more clinical samples to further improve the model’s clinical applicability.

Keywords: type-a aortic dissection, deep residual networks, blood flow modeling, data-driven modeling, non-invasive diagnostics, deep learning, artificial intelligence.

Procedia PDF Downloads 81

Authors: Yelda Kağnıcı, Seçil Seymenler, Bahar Baran, Erol Esen, Barışcan Öztürk, Ender Siyez, Diğdem M. Siyez

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Infertility is the inability to reproduce after twelve months or longer unprotected sexual relationship. Although infertility is not a life threatening illness, it is considered as a serious problem for both the individual and the society. At this point, the importance of examining attitudes towards infertility is critical. Negative attitudes towards infertility may postpone individuals’ help seeking behaviors. The aim of this study is to investigate university students’ attitudes towards infertility in terms of socio-demographic variables (gender, age, taking sexual health education, existence of an infertile individual in the social network, plans about having child and behaviors about health). The sample of the study was 9693 university students attending to 21 universities in Turkey. Of the 9693 students, % 51.6 (n = 5002) were female, % 48.4 (n = 4691) were male. The data was collected by Attitudes toward Infertility Scale developed by researchers and Personal Information Form. In data analysis first frequencies were calculated, then in order to test whether there were significant differences in attitudes towards infertility scores of university students in terms of socio-demographic variables, one way ANOVA was conducted. According to the results, it was found that female students, students who had sexual health education, who have sexual relationship experience, who have an infertile individual in their social networks, who have child plans, who have high caffeine usage and who use alcohol regularly have more positive attitudes towards infertility. On the other hand, attitudes towards infidelity did not show significant differences in terms of age and cigarette usage. When the results of the study were evaluated in general, it was seen that university students’ attitudes towards infertility were negative. The attitudes of students who have high caffeine and alcohols usage were high. It can be considered that these students are aware that their social habits are risky. Female students’ positive attitudes might be explained by their gender role. The results point out that in order to decrease university students’ negative attitudes towards infertility, there is a necessity to develop preventive programs in universities.

Keywords: infertility, attitudes, sex, university students

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Authors: Pieter De Vries, Renate Klaassen, Maria Ioannides

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This paper is about an explorative research into the use of emerging technologies for teaching and learning in higher engineering education. The assumption is that these technologies and applications, which are not yet widely adopted, will help to improve education and as such actively work on the ability to better deal with the mismatch of skills bothering our industries. Technologies such as 3D printing, the Internet of Things, Virtual Reality, and others, are in a dynamic state of development which makes it difficult to grasp the value for education. Also, the instruments in current educational research seem not appropriate to assess the value of such technologies. This explorative research aims to foster an approach to better deal with this new complexity. The need to find out is urgent, because these technologies will be dominantly present in the near future in all aspects of life, including education. The methodology used in this research comprised an inventory of emerging technologies and tools that potentially give way to innovation and are used or about to be used in technical universities. The inventory was based on both a literature review and a review of reports and web resources like blogs and others and included a series of interviews with stakeholders in engineering education and at representative industries. In addition, a number of small experiments were executed with the aim to analyze the requirements for the use of in this case Virtual Reality and the Internet of Things to better understanding the opportunities and limitations in the day-today learning environment. The major findings indicate that it is rather difficult to decide about the value of these technologies for education due to the dynamic state of change and therefor unpredictability and the lack of a coherent policy at the institutions. Most decisions are being made by teachers on an individual basis, who in their micro-environment are not equipped to select, test and ultimately decide about the use of these technologies. Most experiences are being made in the industry knowing that the skills to handle these technologies are in high demand. The industry though is worried about the inclination and the capability of education to help bridge the skills gap related to the emergence of new technologies. Due to the complexity, the diversity, the speed of development and the decay, education is challenged to develop an approach that can make these technologies work in an integrated fashion. For education to fully profit from the opportunities, these technologies offer it is eminent to develop a pro-active strategy and a sustainable approach to frame the emerging technologies development.

Keywords: emerging technologies, internet of things, pro-active strategy, virtual reality

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Authors: Emad A. Ahmed

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Computer software to calculate electron mobility with respect to different scattering mechanism has been developed. This software is adopted completely Graphical User Interface (GUI) technique and its interface has been designed by Microsoft Visual Basic 6.0. As a case study the electron mobility of n-GaN was performed using this software. The behaviour of the mobility for n-GaN due to elastic scattering processes and its relation to temperature and doping concentration were discussed. The results agree with other available theoretical and experimental data.

Keywords: electron mobility, relaxation time, GaN, scattering, computer software, computation physics

Procedia PDF Downloads 660

Authors: F. Elhaouzi, H. Lahlali, M. Zaghrioui, I. El Aboudi A. BelfKira, A. Mdarhri

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The effect of physico chemical properties of solvents on the transport process and mechanical properties in elastomeric nano composite materials is reported. The investigated samples are formed by a semi-crystalline ethylene-co-butyl acrylate polymer filled with hard spherical carbon black (CB) nano particles. The swelling behavior was studied by immersion the dried samples in selected solvents at room temperature during 2 days. For this purpose, two chemical compounds methyl derivatives of aromatic hydrocarbons of benzene, i.e. toluene and xylene, are used to search for the mass and molar volume dependence on the absorption kinetics. Mass gain relative to the mass of dry material at specific times was recorded to probe the absorption kinetics. The transport of solvent molecules in these filled elastomeric composites is following a Fickian diffusion mechanism. Additionally, the swelling ratio and diffusivity coefficient deduced from the Fickian law are found to decrease with the CB concentration. These results indicate that the CB nano particles increase the effective path length for diffusion and consequently limit the absorption of the solvent by occupation free volumes in the material. According to physico chemical properties of the two used solvents, it is found that the diffusion is more important for the toluene molecules solvent due to their low values of the molecular weight and volume molar compared to those for the xylene. Differential Scanning Calorimetry (DSC) and X-ray photo electron (XPS) were also used to probe the eventual change in the chemical composition for the swollen samples. Mechanically speaking, the stress-strain curves of uniaxial tensile tests pre- and post- swelling highlight a remarkably decrease of the strength and elongation at break of the swollen samples. This behavior can be attributed to the decrease of the load transfer density between the matrix and the CB in the presence of the solvent. We believe that the results reported in this experimental investigation can be useful for some demanding applications e.g. tires, sealing rubber.

Keywords: nanocomposite, absorption kinetics, mechanical behavior, diffusion, modelling, XPS, DSC

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Authors: Mimoza Canga, Edit Xhajanka, Irene Malagnino

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The COVID-19 pandemic has had a significant influence on the lives of millions of people and is a threat to public health. SARS-CoV-2 infection has been associated with a number of health problems, including damage to the lungs and central nervous system damage. Additionally, it can also cause oral health problems, such as pain and weakening of the chewing muscles. The purpose of the study is the assessment of the masticatory muscle function in young adults between 18 and 29 years old following SARS-CoV-2 infection. Materials and methods: This study is quantitative cross-sectional research conducted in Albania between March 2023 and September 2023. Our research involved a total of 104 students who participated in our research, of which 64 were female (61.5%) and 40 were male (38.5%). They were divided into four age groups: 18-20, 21-23, 24-26, and 27-29 years old. In this study, the students willingly consented to take part in this study and were guaranteed that their participation would remain anonymous. The study recorded no dropouts, and it was carried out in compliance with the Declaration of Helsinki. Statistical analysis was conducted using IBM SPSS Statistics Version 23.0 on Microsoft Windows Linux, Chicago, IL, USA. Data were evaluated utilizing analysis of variance (ANOVA), with a significance level set at P ≤ 0.05. Results: 80 (76.9%) of the participants who had passed COVID-19 reported chronic masticatory muscle pain (P < 0.0001) and masticatory muscle spasms (P = 0.002). According to data analysis, 70 (67.3%) of the participants had a sore throat (P=0.007). 74% of the students reported experiencing weakness in their chewing muscles (P=0.003). The participants reported having undergone the following treatments: azithromycin (500 mg daily), prednisolone sodium phosphate (15 mg/5 mL daily), Augmentin tablets (625 mg), vitamin C (1000 mg), magnesium sulfate (4 g/100 mL), oral vitamin D3 supplementation of 5000 IU daily, ibuprofen (400 mg every 6 hours), and tizanidine (2 mg every 6 hours). Conclusion: This study, conducted in Albania, has limitations, but it can be concluded that COVID-19 directly affects the functioning of the masticatory muscles.

Keywords: Albania, chronic pain, COVID-19, cross-sectional study, masticatory muscles, spasm

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Authors: Ahmed AlNouss, Salim Ahmed

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Process control refers to the techniques to control the variables in a process in order to maintain them at their desired values. Advanced process control (APC) is a broad term within the domain of control where it refers to different kinds of process control and control related tools, for example, model predictive control (MPC), statistical process control (SPC), fault detection and classification (FDC) and performance assessment. APC is often used for solving multivariable control problems and model predictive control (MPC) is one of only a few advanced control methods used successfully in industrial control applications. Advanced control is expected to bring many benefits to the plant operation; however, the extent of the benefits is plant specific and the application needs a large investment. This requires an analysis of the expected benefits before the implementation of the control. In a real plant simulation studies are carried out along with some experimentation to determine the improvement in the performance of the plant due to advanced control. In this research, such an exercise is undertaken to realize the needs of APC application. The main objectives of the paper are as follows: (1) To apply MPC to a number of simulations set up to realize the need of MPC by comparing its performance with that of proportional integral derivatives (PID) controllers. (2) To study the effect of controller parameters on control performance. (3) To develop appropriate performance index (PI) to compare the performance of different controller and develop novel idea to present tuning map of a controller. These objectives were achieved by applying PID controller and a special type of MPC which is dynamic matrix control (DMC) on the multi-tanks process simulated in loop-pro. Then the controller performance has been evaluated by changing the controller parameters. This performance was based on special indices related to the difference between set point and process variable in order to compare the both controllers. The same principle was applied for continuous stirred tank heater (CSTH) and continuous stirred tank reactor (CSTR) processes simulated in Matlab. However, in these processes some developed programs were written to evaluate the performance of the PID and MPC controllers. Finally these performance indices along with their controller parameters were plotted using special program called Sigmaplot. As a result, the improvement in the performance of the control loops was quantified using relevant indices to justify the need and importance of advanced process control. Also, it has been approved that, by using appropriate indices, predictive controller can improve the performance of the control loop significantly.

Keywords: advanced process control (APC), control loop, model predictive control (MPC), proportional integral derivatives (PID), performance indices (PI)

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Authors: Celestine Mbakaan, Iorkyaa Ahemen, A. D. Onoja, A. N. Amah, Emmanuel Barki

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Rice husk (RH) is a natural sheath that forms and covers the grain of rice. The husk composed of hard materials, including opaline silica and lignin. It separates from its grain during rice milling. RH also contains approximately 15 to 28 wt % of silica in hydrated amorphous form. Nanosilica was derived from the husk of different rice varieties after pre-treating the husk (RH) with HCl and calcination at 550°C. Nanosilica derived from the husk of Osi rice variety produced the highest silica yield, and further pretreatment with 0.8 M H₃PO₄ acid removed more mineral impurities. The silica obtained from this rice variety was selected as a host matrix for doping with Sm³⁺ ions. Rice husk silica (RH-SiO₂) doped with samarium (RH-SiO₂: xSm³⁺ (x=0.01, 0.05, and 0.1 molar ratios) nanophosphors were synthesized via the sol-gel method. The structural analysis by X-ray diffraction analysis (XRD) reveals amorphous structure while the surface morphology, as revealed by SEM and TEM, indicates agglomerates of nano-sized spherical particles with an average particle size measuring 21 nm. The nanophosphor has a large surface area measuring 198.0 m²/g, and Fourier transform infrared spectroscopy (FT-IR) shows only a single absorption band which is strong and broad with a valley at 1063 cm⁻¹. Diffuse reflectance spectroscopy (DRS) shows strong absorptions at 319, 345, 362, 375, 401, and 474 nm, which can be exclusively assigned to the 6H5/2→4F11/2, 3H7/2, 4F9/2, 4D5/2, 4K11/2, and 4M15/2 + 4I11/2, transitions of Sm³⁺ respectively. The photoluminescence excitation spectra show that near UV and blue LEDs can effectively be used as excitation sources to produce red-orange and yellow-orange emission from Sm³⁺ ion-doped RH-SiO₂ nanophosphors. The photoluminescence (PL) of the nanophosphors gives three main lines; 568, 605, and 652 nm, which are attributed to the intra-4f shell transitions from the excited level to ground levels, respectively under excitation wavelengths of 365 and 400 nm. The result, as confirmed from the 1931 CIE coordinates diagram, indicates the emission of red-orange light by RH-SiO₂: xSm³⁺ (x=0.01 and 0.1 molar ratios) and yellow-orange light from RH-SiO₂: 0.05 Sm³⁺. Finally, the result shows that RH-SiO₂ doped with samarium (Sm³⁺) ions can be applicable in display applications.

Keywords: luminescence, nanosilica, nanophosphors, Sm³⁺

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Authors: Kamal Kasmaoui, Farid Makhlouf

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This article seeks to assess the role of the level of interpersonal trust in a country in the remittance landscape. Using historical data from the 2010-2014 wave of the World Value Survey (WVS) for interpersonal trust, our findings underline the substitution role played by the interpersonal trust with remittances. More accurately, remittances tend to drop when the rate of interpersonal trust in the country of origin is high. Overall, a rise in trust is likely to underpin social cohesion, limiting, therefore, the need for remittances. These results are still fairly solid and unambiguous after controlling for confounding factors and possible reverse causality.

Keywords: interpersonal trust, social capital, remittances, 2SLS

Procedia PDF Downloads 167

Authors: Suhaib Radi, Ibrahim Basem Nafadi, Abdullah Ahmed Alsulami, Nawaf Faisal Halabi, Abdulrhman Abdullah Alsubhi, Sami Wesam Maghrabi, Waleed Saad Alshehri

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Background: Kidney stones greatly affect individuals. The range of these effects regarding multiple kidney stone factors (size, presence of obstruction, and modality of treatment) in stone formers with and without diabetes has not been well explored in the literature to the best of the author's knowledge. Our goal is to investigate this unexplored correlation between diabetes and kidney stones by conducting a Cohort retrospective study to precisely evaluate the effects of this condition and the existence of complications in adult individuals with diabetes in Saudi Arabia in comparison to a non-diabetic control group. Methodology: This is a retrospective cohort study aiming to evaluate the range of effects of kidney stones in stone formers in a group of adults diagnosed with type 2 diabetes mellitus and adults without diabetes between 2017 and 2019 in Jeddah, Saudi Arabia. An IRB approval has been granted for this study. The data was analyzed using SPSS. The data was collected from the 1st of December 2022 until the 1st of March 2023. Results: A total of 254 individuals diagnosed with kidney stones were included, 127 of whom were adult individuals with type 2 diabetes, and 127 were non-diabetics. Our study shows that the individuals affected with diabetes were more likely to have larger kidney stones in comparison to individuals without diabetes (13.12 mm vs. 10.53 mm, p-value = 0.03). Moreover, individuals with hypertension and dyslipidemia also had significantly larger stones. On the other hand, no significant difference was found in the presence of obstruction and modality of treatment between the two groups. Conclusion: This study done in Saudi Arabia found that individuals with kidney stones who concurrently had diabetes formed larger kidney stones, and they were also found to have other comorbidities such as HTN, dyslipidemia, obesity, and renal disease. The significance of these findings could assist in the future of primary and secondary prevention of renal stones.

Keywords: kidney stone, type 2 DM, metabolic syndrome, lithotripsy

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Authors: Fadil Elmenfi, Ahmed Gaibani

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This study investigates the role of gender in influencing public speaking anxiety. Questionnaire survey was administered to the samples of the study. Technique of correlation and descriptive analysis will be further applied to the data collected to determine the relationship between gender and public speaking anxiety. This study could serve as a guide to identify the effects of gender differences on public speaking anxiety and provide necessary advice on how to design a way of coping with or overcoming public speaking anxiety.

Keywords: across culture, communication, English language competence, gender, postgraduate students, speaking anxiety

Procedia PDF Downloads 551

Authors: Francisco Fernandez de Castro

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The Conditional Cash Transfer (CCT) model gives monetary transfers to beneficiary families on the condition that they take specific education and health actions. According to the economic rationale of CCTs the poor need incentives to invest in their human capital because they are trapped by a lack of information and misleading beliefs. If left to their own decision, the poor will not be able to choose what is in their best interests. The basic assumption of the CCT model is that the poor need incentives to take care of their own education and health-nutrition. Due to the incentives (income cash transfers and conditionalities), beneficiary families are supposed to attend doctor visits and health talks. Children would stay in the school. These incentivized behaviors would produce outcomes such as better health and higher level of education, which in turn will reduce poverty. Based on a grounded theory approach to conduct a two-year period of qualitative data collection in northern Mexico, this study shows that this explanation is incomplete. In addition to the information failure and inadequate beliefs, there are structural barriers in everyday life of households that make health-nutrition and education investments difficult. In-depth interviews and observation work showed that the program takes for granted local conditions in which beneficiary families should fulfill their co-responsibilities. Data challenged the program’s assumptions and unveiled local obstacles not contemplated in the program’s design. These findings have policy and research implications for the CCT agenda. They bring elements for late programming due to the gap between the CCT strategy as envisioned by policy designers, and the program that beneficiary families experience on the ground. As for research consequences, these findings suggest new avenues for scholarly work regarding the causal mechanisms and social processes explaining CCT outcomes.

Keywords: conditional cash transfers, incentives, poverty, structural barriers

Procedia PDF Downloads 110

Authors: Richard Ren

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Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

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Authors: Ezana Haddis Weldeghebrael

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Addis Ababa City Administration is engaged in a massive facelift of the inner-city. The paper, therefore, aims to analyze the challenges of the current urban regeneration effort by paying special attention to Lideta and Basha Wolde Chilot projects. To this end, the paper has adopted a documentary research strategy to collect the data and Institutionalist perspective as well as the concept of urban regeneration to analyze the data. The sources were selected based on relevance and recency. Academic research outputs were used primarily. However, where much scholastic publications are not available institutional reports, newspaper articles, and expert presentations were used. The major findings of the research revealed that although the second generation of urban redevelopment projects have attempted to involve affected groups and succeeded in designing better neighborhoods, they are riddled with three major drawbacks. The first one is institutional constraints, i.e. absence of urban redevelopment strategy as well as housing policy, broad definition of ‘public purpose’, little regard for informal businesses, limitation on rights groups, negotiation power not devolved at sub-city level and no plan for groups that cannot afford to pay the down payment for low-cost apartments. The second one is planning limitation, i.e. absence of genuine affected group participation as well as consultative level of public engagement. The third one is implementation failure, i.e. no regard to maintaining social bond, non-participatory and ill-informed resettlement, interference from senior government officials, failure to protect the poor from speculators, corruption and disregard to heritage buildings. Based on the findings, the paper concluded that the current inner-city redevelopment has failed to be socially sustainable and calls for enactment of housing policy as well as redevelopment strategy, affected group participation, on-site resettlement, empowering the Sub-city to manage the project and allowing housing rights groups to advocate for the poor slum dwellers.

Keywords: participation, redevelopment, planning, implementation, consultation

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Authors: O. O. Fola-Matthews, O. O. Soyinka, D. N. Bitalo

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Nigeria is one of the major shark fishing nations in Africa, but its fisheries managers still record catch data in aggregates ‘sharks’ with no species-specific details. This is because most of the shark specimens look identical in morphology, and field identification of some closely related species is tricky. This study uses DNA barcoding as a method to identify shark species from five different landing areas off the Nigerian Coast. 100 dorsal fins were sampled in order to provide a Chondrichthyan sequence that would be matched to reference specimens in a DNA barcode database

Keywords: BOLD, DNA barcoding, nigeria, sharks

Procedia PDF Downloads 157

Authors: Shammael Selorfia

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The Quality improvement project aimed to improve junior doctors and nurses’ knowledge and confidence in diagnosing and managing delirium on inpatient surgical wards in a tertiary hospital. The study aimed to develop a standardised assessment and management checklist for all staff working with patients who were presenting with signs of delirium. The aim of the study was to increase confidence of staff at dealing with delirium and improve the quality of referrals that were being sent to the Mental Health Liaison team over a 6-month period. A significant proportion of time was being spent by the Mental Health Liaison triage nurses on referrals for delirium. Data showed 28% of all delirium referrals from surgical teams were being closed at triage reflecting a poor standard of quality of those referrals. A qualitative survey of junior doctors in 6 surgical specialties in a UK tertiary hospital was conducted. These specialties include general surgery, vascular, plastic, urology, neurosurgery, and orthopaedics. The standardised checklist was distributed to all surgical wards. A comparison was made between the Mental health team caseload of delirium before intervention was compared and after. A Qualitative survey at end of 3-month cycle and compare overall caseload on Mental Health Liaison team to pre-QIP data with aim to improve quality of referrals and reduce workload on Mental Health Liaison team. At the end of the project cycle, we demonstrated an improvement in the quality of referrals with a decrease in the percentage of referrals being closed at triage by 8%. Our surveys also indicated an increase in the knowledge of official trust delirium guidelines and confidence at managing the patients. This project highlights that a new approach to delirium using multi-component interventions is needed, where the diagnosis of delirium is shared amongst medical and nursing staff, and everyone plays role in management. The key is improving awareness of delirium and encouraging the use of recognized diagnostic tools and official guidelines. Recommendations were made to the trust on how to implement a long-lasting change.

Keywords: delirium, surgery, quality, improvement

Procedia PDF Downloads 72

Authors: Leonore Robieux, Franck Zenasni, Marc Pocard, Clarisse Eveno

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Empirical data in health psychology studies show the necessity to consider the doctor-patient communication and its positive impact on outcomes such as patients’ satisfaction, treatment adherence, physical and psychological wellbeing. In this line, the present research aims to define the role and determinants of an effective doctor–patient communication during the treatment of patients with serious illness (peritoneal carcinomatosis). We carried out a prospective longitudinal study including patients treated for peritoneal carcinomatosis of various origins. From November 2016, to date, data were collected using validated questionnaires at two times of evaluation: one month before the surgery (T0) and one month after (T1). Thus, patients reported their (a) anxiety and depression levels, (b) standardized and individualized quality of life and (c) how they perceived communication, attitude and empathy of the surgeon. 105 volunteer patients (Mean age = 58.18 years, SD = 10.24, 62.2% female) participated to the study. PC arose from rare diseases (14%), colorectal (38%), eso-gastric (24%) and ovarian (8%) cancer. Three groups are defined according to the severity of their pathology and the treatment offered to them: (1) important surgical treatment with the goal of healing (53%), (2) repeated palliative surgical treatment (17%), and (3) the patients recused for surgical treatment, only palliative approach (30%). Results are presented according to Baron and Kenny recommendations. The regressions analyses show that only depression and anxiety are sensitive to the communication and empathy of surgeon. The main results show that a good communication and high level of empathy at T0 and T1 limit depression and anxiety of the patients in T1. Results also indicate that the severity of the disease modulates this positive impact of communication: better is the communication the less are the level of depression and anxiety of the patients. This effect is higher for patients treated for the more severe disease. These results confirm that, even in the case severe disease a good communication between patient and physician remains a significant factor in promoting the well-being of patients. More specific training need to be developed to promote empathic care.

Keywords: clinical empathy, determinants, healthcare, psychological wellbeing

Procedia PDF Downloads 117

Authors: Adriana Salgado, Sandra Castaneda, Ivan Perez

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Learning to read and write is a complex process involving several cognitive skills, contextual, and cultural environments. The basis of this development is linguistic skills, such as the ability to name and understand vocabulary, retell a story, phonological awareness, letter knowledge, among others. In children with autism spectrum disorder (ASD), one of the main concerns is related to language disorders. Nevertheless, most of the children with ASD are able to decode written information but have difficulties in reading comprehension. The research of these processes in the Spanish-speaking population is limited. However, the increasing prevalence of this diagnosis (1 in 115 children) in Mexico has implications at different levels. Educational research is an important area of interest in ASD children, such as emergent literacy. Reading and writing expand the possibilities of academic, cultural, and social information access. Taking this information into account, the objective of this research was to identify the relationship between language skills, alphabet knowledge, phonological awareness, and early reading and writing in ASD Spanish-speaking children. The method used for this research was based on tasks that were selected, adapted and in some cases designed to measure initial reading and writing, as well as language skills (naming, receptive vocabulary, and narrative skills), phonological awareness (similar phonological word pairs, beginning sound awareness and spelling) and letter knowledge, in a sample of 45 children (38 boys and 7 girls) with prior diagnosis of ASD. Descriptive analyses, as well as bivariate correlations, cluster analysis, and canonical correspondence, were obtained for the data results. Results showed that variability was large; however, it was possible to characterize the sample in low, medium, and high score groups regarding children performance. The low score group (46.7% of the sample), had a null or deficient performance in language skills and phonological awareness, some could identify up to five letters of the alphabet, showed no early reading skills but they could scribble. The middle score group was characterized by a highly variable performance in different tasks, with better language skills in receptive and naming vocabulary, some narrative, letter knowledge, and phonological awareness (beginning sound awareness) skills. The high score group, (24.4% of the sample) had the best performance in language skills in relation to the sample data, as well as in the rest of the measured skills. Finally, scores were canonically correlated between naming, receptive vocabulary, narrative, phonological awareness, letter knowledge and initial learning of reading and writing skills for the high score group and letter knowledge, naming and receptive vocabulary for the lower score group, which is consistent with previous research in typical and ASD children. In conclusion, the obtained data is consistent with previous studies. Despite large variability, it was possible to identify performance profiles and relations based on linguistic, phonological awareness, and letter knowledge skills. These skills were predictor variables of the initial development of reading and writing. The above has implications for a future program and strategies development that may benefit the acquisition of reading and writing in ASD children.

Keywords: autism, autism spectrum disorders, early literacy, emergent literacy

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Authors: Wisam H. Muragaa, Kamaruzzaman Seman, Mohd Fadzli Marhusin

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Software Defined Networking (SDN) is a new norm of networks. It is designed to facilitate the way of managing, measuring, debugging and controlling the network dynamically, and to make it suitable for the modern applications. Generally, measurement methods can be divided into two categories: Active and passive methods. Active measurement method is employed to inject test packets into the network in order to monitor their behaviour (ping tool as an example). Meanwhile the passive measurement method is used to monitor the traffic for the purpose of deriving measurement values. The measurement methods, both active and passive, are useful for the collection of traffic statistics, and monitoring of the network traffic. Although there has been a work focusing on measuring traffic statistics in SDN environment, it was only meant for measuring packets and bytes rates for non-web traffic. In this study, a feasible method will be designed to measure the number of packets and bytes in a certain time, and facilitate obtaining statistics for both web traffic and non-web traffic. Web traffic refers to HTTP requests that use application layer; while non-web traffic refers to ICMP and TCP requests. Thus, this work is going to be more comprehensive than previous works. With a developed module on POX OpenFlow controller, information will be collected from each active flow in the OpenFlow switch, and presented on Command Line Interface (CLI) and wireshark interface. Obviously, statistics that will be displayed on CLI and on wireshark interfaces include type of protocol, number of bytes and number of packets, among others. Besides, this module will show the number of flows added to the switch whenever traffic is generated from and to hosts in the same statistics list. In order to carry out this work effectively, our Python module will send a statistics request message to the switch requesting its current ports and flows statistics in every five seconds; while the switch will reply with the required information in a message called statistics reply message. Thus, POX controller will be notified and updated with any changes could happen in the entire network in a very short time. Therefore, our aim of this study is to prepare a list for the important statistics elements that are collected from the whole network, to be used for any further researches; particularly, those that are dealing with the detection of the network attacks that cause a sudden rise in the number of packets and bytes like Distributed Denial of Service (DDoS).

Keywords: mininet, OpenFlow, POX controller, SDN

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Authors: Huseyin Arasli, Maryam Abdullahi, Tugrul Gunay

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One of the prominence elements used by the destination marketing organization (DMO) as a marketing strategy is the application of Social media tools. During the current spread of coronavirus disease (COVID-19), travel restriction was placed in most countries of the world, leading to the closure of borders movement. It should be noted that most tourism travelers depend on social media to obtain and exchange different kinds of information about COVID-19 in an unprecedented scale. The situational information people received is valued, which calls for the response of the tourism industry on the epidemic. Therefore, it is highly important to recognize such situational information and to understand how people spread this propaganda on social media platforms so that suitable information that relates the COVID-19 epidemic is available in a manner that will not tarnish the marketing strategies, festival planners. Data for this research study was collected from the desk review, which is a secondary source data, online blogs, and interview through social media chat. The results of this research show that the widespread of COVID-19 pandemics led to rapid lockdown in states and cities all over Nigeria, causing declining demands in hotels, airlines, recreation, and tourism centers. Additionally, billions of dollars lost has been recorded in the high increase of hotels and travel bookings cancellations which caused hundreds and thousands of job loss in the country. The result of this research also revealed that COVID-19 is causing more havoc on the unemployment rate indices of the country. Similarly, the over-dependence of government on petroleum has further caused considerable revenue loss, thereby raising a high poverty rate among less privileged Nigerians. Based on this result, the study suggested that there is an urgent need for the government to diversify its economy by looking at other different sectors such as tourism and agricultural farm produce to harmonize other commercial trades sectors in the country.

Keywords: social media, destination marketing organizations, DMOs, cultural COVID-19, coronavirus, hospitality, travel tour, tourism

Procedia PDF Downloads 94

Authors: Jaan-Willem Simon, Bertram Stier, Brett Bednarcyk, Evan Pineda, Stefanie Reese

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Textile composites, in which the reinforcing fibers are woven or braided, have become very popular in numerous applications in aerospace, automotive, and maritime industry. These textile composites are advantageous due to their ease of manufacture, damage tolerance, and relatively low cost. However, physics-based modeling of the mechanical behavior of textile composites is challenging. Compared to their unidirectional counterparts, textile composites introduce additional geometric complexities, which cause significant local stress and strain concentrations. Since these internal concentrations are primary drivers of nonlinearity, damage, and failure within textile composites, they must be taken into account in order for the models to be predictive. The macro-scale approach to modeling textile-reinforced composites treats the whole composite as an effective, homogenized material. This approach is very computationally efficient, but it cannot be considered predictive beyond the elastic regime because the complex microstructural geometry is not considered. Further, this approach can, at best, offer a phenomenological treatment of nonlinear deformation and failure. In contrast, the mesoscale approach to modeling textile composites explicitly considers the internal geometry of the reinforcing tows, and thus, their interaction, and the effects of their curved paths can be modeled. The tows are treated as effective (homogenized) materials, requiring the use of anisotropic material models to capture their behavior. Finally, the micro-scale approach goes one level lower, modeling the individual filaments that constitute the tows. This paper will compare meso- and micro-scale approaches to modeling the deformation, damage, and failure of textile-reinforced polymer matrix composites. For the mesoscale approach, the woven composite architecture will be modeled using the finite element method, and an anisotropic damage model for the tows will be employed to capture the local nonlinear behavior. For the micro-scale, two different models will be used, the one being based on the finite element method, whereas the other one makes use of an embedded semi-analytical approach. The goal will be the comparison and evaluation of these approaches to modeling textile-reinforced composites in terms of accuracy, efficiency, and utility.

Keywords: multiscale modeling, continuum damage model, damage interaction, textile composites

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Authors: Thanaa Abdel Moghny, Mohamed Keshawy, Mahmoud Fathy, Abdul-Raheim M. Abdul-Raheim, Khalid I. Kabel, Ahmed F. El-Kafrawy, Mahmoud Ahmed Mousa, Ahmed E. Awadallah

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Ecological pollution is of great concern for human health and the environment. Numerous organic and inorganic pollutants usually discharged into the water caused carcinogenic or toxic effect for human and different life form. In this respect, this work aims to treat water contaminated by organic and inorganic waste using sorbent based on polystyrene. Therefore, two different series of adsorbent material were prepared; the first one included the preparation of polymeric sorbent from the reaction of styrene acrylate ester and alkyl acrylate. The second series involved syntheses of composite ion exchange resins of waste polystyrene and   amorphous carbon thin film (WPS/ACTF) by solvent evaporation using micro emulsion polymerization. The produced ACTF/WPS nanocomposite was sulfonated to produce cation exchange resins ACTF/WPSS nanocomposite. The sorbents of the first series were characterized using FTIR, ¹H NMR, and gel permeation chromatography. The thermal properties of the cross-linked sorbents were investigated using thermogravimetric analysis, and the morphology was characterized by scanning electron microscope (SEM). The removal of organic pollutant was determined through absorption tests in a various organic solvent. The chemical and crystalline structure of nanocomposite of second series has been proven by studies of FTIR spectrum, X-rays, thermal analysis, SEM and TEM analysis to study morphology of resins and ACTF that assembled with polystyrene chain. It is found that the composite resins ACTF/WPSS are thermally stable and show higher chemical stability than ion exchange WPSS resins. The composite resin was evaluated for calcium hardness removal. The result is evident that the ACTF/WPSS composite has more prominent inorganic pollutant removal than WPSS resin. So, we recommend the using of nanocomposite resin as new potential applications for water treatment process.

Keywords: nanocomposite, sorbent materials, waste water, waste polystyrene

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Authors: Radin Siti Aishah Radin A Rahman, Norasmah Othman, Zaidatol Akmaliah Lope Pihie, Hariyaty Ab. Wahid

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The recent instability in economy was found to be influencing the situation in Malaysia whether directly or indirectly. Taking that into consideration, the government needs to find the best approach to balance its citizen’s socio-economic strata level urgently. Through education platform is among the efforts planned and acted upon for the purpose of balancing the effects of the influence, through the exposure of social entrepreneurial activity towards youth especially those in higher institution level. Armed with knowledge and skills that they gained, with the support by entrepreneurial culture and environment while in campus; indirectly, the students will lean more on making social entrepreneurship as a career option when they graduate. Following the issues of marketability and workability of current graduates that are becoming dire, research involving how far the willingness of student to create social innovation that contribute to the society without focusing solely on personal gain is relevant enough to be conducted. With that, this research is conducted with the purpose of identifying the level of entrepreneurial intention and social entrepreneurship among higher institution students in Malaysia. Stratified random sampling involves 355 undergraduate students from five public universities had been made as research respondents and data were collected through surveys. The data was then analyzed descriptively using min score and standard deviation. The study found that the entrepreneurial intention of higher education students are on moderate level, however it is the contrary for social entrepreneurship activities, where it was shown on a high level. This means that while the students only have moderate level of willingness to be a social entrepreneur, they are very committed to created social innovation through the social entrepreneurship activities conducted. The implication from this study can be contributed towards the higher institution authorities in prediction the tendency of student in becoming social entrepreneurs. Thus, the opportunities and facilities for realizing the courses related to social entrepreneurship must be created expansively so that the vision of creating as many social entrepreneurs as possible can be achieved.

Keywords: entrepreneurial intention, higher education institutions (HEIs), social entrepreneurship, social entrepreneurial activity, gender

Procedia PDF Downloads 256

Authors: Neelam Vats, Madhvi Pathak Pillai, Rajender Lal, Indu Dabas

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The pressure of scoring higher marks to be able to get admission in a higher ranked institution has become a social stigma for school students. It leads to various social and academic pressures on them, causing psychological anxiety. This undue stress on students sometimes may even steer to aggressive behavior or suicidal tendencies. Human mind is always surrounded by the some desires, emotions and passions, which usually disturbs our mental peace. In such a scenario, we look for a solution that helps in removing all the obstacles of mind and make us mentally peaceful and strong enough to be able to deal with all kind of pressure. Preksha meditation is one such technique which aims at bringing the positive changes for overall transformation of personality. Hence, the present study was undertaken to assess the impact of Preksha Meditation on the academic anxiety on female teenagers. The study was conducted on 120 high school students from the capital city of India. All students were in the age group of 13-15 years. They also belonged to similar social as well as economic status. The sample was equally divided into two groups i.e. experimental group (N = 60) and control group (N = 60). Subjects of the experimental group were given the intervention of Preksha Meditation practice by the trained instructor for one hour per day, six days a week, for three months for the first experimental stage and another three months for the second experimental stage. The subjects of the control group were not assigned any specific type of activity rather they continued doing their normal official activities as usual. The Academic Anxiety Scale was used to collect data during multi-level stages i.e. pre-experimental stage, post-experimental stage phase-I, and post-experimental stage phase-II. The data were statistically analyzed by computing the two-tailed-‘t’ test for inter group comparison and Sandler’s ‘A’ test with alpha = or p < 0.05 for intra-group comparisons. The study concluded that the practice for longer duration of Preksha Meditation practice brings about very significant and beneficial changes in the pattern of academic anxiety.

Keywords: academic anxiety, academic pressure, Preksha, meditation

Procedia PDF Downloads 123

Authors: Mwafaq Ibdah, Mossab, Yahyaa, Dor Rachmany, Yoram Gerchman, Doron Holland, Liora Shaltiel-Harpaz

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Pear Psylla is the most important pest of pear in all pear-growing regions, in Asian, European, and the USA. Pear psylla damages pears in several ways: high-density populations of these insects can cause premature leaf and fruit drop, diminish plant growth, and reduce fruit size. In addition, their honeydew promotes sooty mold on leaves and russeting on fruit. Pear psyllas are also considered vectors of pear pathogens such as Candidatus Phytoplasma pyri causing pear decline that can lead to loss of crop and tree vigor, and sometimes loss of trees. Psylla control is a major obstacle to efficient integrated pest management. Recently we have identified two naturally resistance pear accessions (Py.760-261 and Py.701-202) in the Newe Ya’ar live collection. GC-MS volatile metabolic profiling identified several volatile compounds common in these accessions but lacking, or much less common, in a sensitive accession, the commercial Spadona variety. Among these volatiles were styrene and its derivatives. When the resistant accessions were used as inter-stock, the volatile compounds appear in commercial Spadona scion leaves, and it showed reduced susceptibility to pear psylla. Laboratory experiments and applications of some of these volatile compounds were very effective against psylla eggs, nymphs, and adults. The genes and enzymes involved in the specific reactions that lead to the biosynthesis of styrene in plant are unknown. We have identified a phenolic acid decarboxylase that catalyzes the formation of p-hydroxystyrene, which occurs as a styrene analog in resistant pear genotypes. The His-tagged and affinity chromatography purified E. coli-expressed pear PyPAD1 protein could decarboxylate p-coumaric acid and ferulic acid to p-hydroxystyrene and 3-methoxy-4-hydroxystyrene. In addition, PyPAD1 had the highest activity toward p-coumaric acid. Expression analysis of the PyPAD gene revealed that its expressed as expected, i.e., high when styrene levels and psylla resistance were high.

Keywords: pear Psylla, volatile, GC-MS, resistance

Procedia PDF Downloads 136

Authors: N. W. I. A. Jayawardana, W. A. T. A. Jayalath, W. M. T. Madhujith, U. Ralapanawa, R. S. Jayasekera, S. A. S. B. Alagiyawanna, A. M. K. R. Bandara, N. S. Kalupahana

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There is mounting evidence to the effect that dietary and lifestyle changes affect the incidence of non-communicable diseases (NCDs). This study was conducted to investigate the association of diet, physical activity, smoking, alcohol consumption and duration of sleep with overweight, obesity, hypertension and diabetes in a cohort of males from the Central Province of Sri Lanka. A total of 2694 individuals aged between 17 – 68 years (Mean = 31) were included in the study. Body Mass Index cutoff values for Asians were used to categorize the participants as normal, overweight and obese. The dietary data were collected using a food frequency questionnaire [FFQ] and data on the level of physical activity, smoking, alcohol consumption and sleeping hours were obtained using a self-administered validated questionnaire. Systolic and diastolic blood pressure, random blood glucose levels were measured to determine the incidence of hypertension and diabetes. Among the individuals, the prevalence of overweight and obesity were 34% and 16.4% respectively. Approximately 37% of the participants suffered from hypertension. Overweight and obesity were associated with older age men (P<0.0001), frequency of smoking (P=0.0434), alcohol consumption level (P=0.0287) and the quantity of lipid intake (P=0.0081). Consumption of fish (P=0.6983) and salty snacks (P=0.8327), sleeping hours (P=0.6847) and the level of physical activity were not significantly (P=0.3301) associated with the incidence of overweight and obesity. Based on the fitted model, only age was significantly associated with hypertension (P < 0.001). Further, age (P < 0.0001), sleeping hours (P=0.0953) and consumption of fatty foods (P=0.0930) were significantly associated with diabetes. Age was associated with higher odds of pre diabetes (OR:1.089;95% CI:1.053,1.127) and diabetes (OR:1.077;95% CI:1.055,1.1) whereas 7-8 hrs. of sleep per day was associated with lesser odds of diabetes (OR:0.403;95% CI:0.184,0.884). High prevalence of overweight, obesity and hypertension in working-age males is a threatening sign for this area. As this population ages in the future and urbanization continues, the prevalence of above risk factors will likely to escalate.

Keywords: age, males, non-communicable diseases, obesity

Procedia PDF Downloads 332

Authors: Xianglu Tang, Zhenxue Jiang, Zhuo Li

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Characterization of shale multi scale heterogeneity is an important part to evaluate size and space distribution of shale gas reservoirs in sedimentary basins. The origin of shale heterogeneity has always been a hot research topic for it determines shale micro characteristics description and macro quality reservoir prediction. Shale multi scale heterogeneity was discussed based on thin section observation, FIB-SEM, QEMSCAN, TOC, XRD, mercury intrusion porosimetry (MIP), and nitrogen adsorption analysis from 30 core samples in Silurian Longmaxi formation. Results show that shale heterogeneity can be characterized by pore structure and mineral composition. The heterogeneity of shale pore is showed by different size pores at nm-μm scale. Macropores (pore diameter > 50 nm) have a large percentage of pore volume than mesopores (pore diameter between 2~ 50 nm) and micropores (pore diameter < 2nm). However, they have a low specific surface area than mesopores and micropores. Fractal dimensions of the pores from nitrogen adsorption data are higher than 2.7, what are higher than 2.8 from MIP data, showing extremely complex pore structure. This complexity in pore structure is mainly due to the organic matter and clay minerals with complex pore network structures, and diagenesis makes it more complicated. The heterogeneity of shale minerals is showed by mineral grains, lamina, and different lithology at nm-km scale under the continuous changing horizon. Through analyzing the change of mineral composition at each scale, random arrangement of mineral equal proportion, seasonal climate changes, large changes of sedimentary environment, and provenance supply are considered to be the main reasons that cause shale minerals heterogeneity from microcosmic to macroscopic. Due to scale effect, the change of shale multi scale heterogeneity is a discontinuous process, and there is a transformation boundary between homogeneous and in homogeneous. Therefore, a shale multi scale heterogeneity changing model is established by defining four types of homogeneous unit at different scales, which can be used to guide the prediction of shale gas distribution from micro scale to macro scale.

Keywords: heterogeneity, homogeneous unit, multiscale, shale

Procedia PDF Downloads 444

Authors: Javier Muro, Anja Linstadter, Florian Manner, Lisa Schwarz, Stephan Wollauer, Paul Magdon, Gohar Ghazaryan, Olena Dubovyk

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Monitoring the trade-off between biomass production and biodiversity in grasslands is critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can model grasslands’ characteristics with varying accuracies. However, studies often fail to cover a sufficiently broad range of environmental conditions, and evidence suggests that prediction models might be case specific. In this study, biomass production and biodiversity indices (species richness and Fishers’ α) are modeled in 150 grassland plots for three sites across Germany. These sites represent a North-South gradient and are characterized by distinct soil types, topographic properties, climatic conditions, and management intensities. Predictors used are derived from Sentinel-1 & 2 and a set of topoedaphic variables. The transferability of the models is tested by training and validating at different sites. The performance of feed-forward deep neural networks (DNN) is compared to a random forest algorithm. While biomass predictions across gradients and sites were acceptable (r2 0.5), predictions of biodiversity indices were poor (r2 0.14). DNN showed higher generalization capacity than random forest when predicting biomass across gradients and sites (relative root mean squared error of 0.5 for DNN vs. 0.85 for random forest). DNN also achieved high performance when using the Sentinel-2 surface reflectance data rather than different combinations of spectral indices, Sentinel-1 data, or topoedaphic variables, simplifying dimensionality. This study demonstrates the necessity of training biomass and biodiversity models using a broad range of environmental conditions and ensuring spatial independence to have realistic and transferable models where plot level information can be upscaled to landscape scale.

Keywords: ecosystem services, grassland management, machine learning, remote sensing

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Authors: Sukru Merey, Caglar Sinayuc

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Gas hydrate deposits which are found in deep ocean sediments and in permafrost regions are supposed to be a fossil fuel reserve for the future. The Black Sea is also considered rich in terms of gas hydrates. It abundantly contains gas hydrates as methane (CH₄~80 to 99.9%) source. In this study, by using the literature, seismic and other data of the Black Sea such as salinity, porosity of the sediments, common gas type, temperature distribution and pressure gradient, the optimum gas production method for the Black Sea gas hydrates was selected as mainly depressurization method. Numerical simulations were run to analyze gas production from gas hydrate deposited in turbidites in the Black Sea by depressurization.

Keywords: CH4 hydrate, Black Sea hydrates, gas hydrate experiments, HydrateResSim

Procedia PDF Downloads 613