Search results for: testing techniques

Authors: Nurunnahar Akter, Elena Kulinskaya, Nicholas Steel, Ilyas Bakbergenuly

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Although Hormone Replacement Therapy (HRT) is an effective treatment in ameliorating menopausal symptoms, it has mixed effects on different health outcomes, increasing, for instance, the risk of breast cancer. Because of this, many symptomatic women are left untreated. Untreated menopausal symptoms may result in other health issues, which eventually put an extra burden and costs to the health care system. All-cause mortality analysis may explain the net benefits and risks of the HRT therapy. However, it received far less attention in HRT studies. This study investigated the impact of HRT on all-cause mortality using electronically recorded primary care data from The Health Improvement Network (THIN) that broadly represents the female population in the United Kingdom (UK). The study entry date for this study was the record of the first HRT prescription from 1984, and patients were followed up until death or transfer to another GP practice or study end date, which was January 2017. 112,354 HRT users (cases) were matched with 245,320 non-users by age at HRT initiation and general practice (GP). The hazards of all-cause mortality associated with HRT were estimated by a parametric Weibull-Cox model adjusting for a wide range of important medical, lifestyle, and socio-demographic factors. The multilevel multiple imputation techniques were used to deal with missing data. This study found that during 32 years of follow-up, combined HRT reduced the hazard ratio (HR) of all-cause mortality by 9% (HR: 0.91; 95% Confidence Interval, 0.88-0.94) in women of age between 46 to 65 at first treatment compared to the non-users of the same age. Age-specific mortality analyses found that combined HRT decreased mortality by 13% (HR: 0.87; 95% CI, 0.82-0.92), 12% (HR: 0.88; 95% CI, 0.82-0.93), and 8% (HR: 0.92; 95% CI, 0.85-0.98), in 51 to 55, 56 to 60, and 61 to 65 age group at first treatment, respectively. There was no association between estrogen-only HRT and women’s all-cause mortality. The findings from this study may help to inform the choices of women at menopause and to further educate the clinicians and resource planners.

Keywords: hormone replacement therapy, multiple imputations, primary care data, the health improvement network (THIN)

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Authors: Yaxin Bi, Peter Nicholl

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The rapid advancement of Information and communication technologies (ICT) is extremely influencing every aspect of Higher Education. It has transformed traditional teaching, learning, assessment and feedback into a new era of Digital Education. This also introduces many challenges in capturing and understanding student engagement with their studies in Higher Education. The School of Computing at Ulster University has developed a Feedback And Notification (FAN) Online tool that has been used to send students links to personalized feedback on their submitted assessments and record students’ frequency of review of the shared feedback as well as the speed of collection. The feedback that the students initially receive is via a personal email directing them through to the feedback via a URL link that maps to the feedback created by the academic marker. This feedback is typically a Word or PDF report including comments and the final mark for the work submitted approximately three weeks before. When the student clicks on the link, the student’s personal feedback is viewable in the browser and they can view the contents. The FAN tool provides the academic marker with a report that includes when and how often a student viewed the feedback via the link. This paper presents an investigation into student engagement through analyzing the interaction timestamps and frequency of review by the student. We have proposed an approach to modeling interaction timestamps and use sentiment classification techniques to analyze the data collected over the last five years for a set of modules. The data studied is across a number of final years and second-year modules in the School of Computing. The paper presents the details of quantitative analysis methods and describes further their interactions with the feedback overtime on each module studied. We have projected the students into different groups of engagement based on sentiment analysis results and then provide a suggestion of early targeted intervention for the set of students seen to be under-performing via our proposed model.

Keywords: feedback, engagement, interaction modelling, sentiment analysis

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Authors: Magy Atef Awad Attia

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The objective of this study was to study the effect of social stories on social interaction of students with autism. The sample was at level 5 student with autism, Another University Demonstration School student, who was diagnosed by the Physician as High Functioning Autism since he was able to read, write, calculate and was studying in inclusive classroom. However, he still had disability in social interaction to participate in social activity group and communication. He could not learn how to develop friendship or create relationship. He had inappropriate behavior in social context. He did not understand complex social situations. In addition, he did seemed to not know time and place. He was not able to understand feeling of oneself as well as the others. Consequently, he could not express his emotion appropriately. He did not understand or express his non-verbal language for communicating with friends. He lacked of common interest or emotion with nearby persons. He greeted inappropriately or was not interested in greeting. In addition, he did not have eye contact. He used inadequate language etc. He was elected by Purposive Sampling. His parents were willing to allow them to participate in this study. The research instruments were the lesson plan of social stories, and the picture book of social stories. The instruments used for data collection, were the social interaction evaluation of autistic students. This research was Experimental Research as One Group Pre-test, Post-test Design. For the Pre-test, the experiment was conducted by social stories. Then, the Post-test was implemented. The statistic used for data analysis. The research results were shown by scale. The results revealed that the autistic students taught by social stories indicated better social reaction after being taught by social stories.

Keywords: autism, autistic behavior, stability, harsh environments, techniques, thermal, properties, materials, applications, brittleness, fragility, disadvantages, bank, branches, profitability, setting prediction, effective target, measurement, evaluation, performance, commercial, business, sustainability, financial, system.

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Authors: Dmitriy Babenko, Sergey Yegorov, Ilya Korshukov, Aidana Sultanbekova, Valentina Barkhanskaya, Tatiana Bashirova, Yerzhan Zhunusov, Yevgeniya Li, Viktoriya Parakhina, Svetlana Kolesnichenko, Yeldar Baiken, Aruzhan Pralieva, Zhibek Zhumadilova, Matthew S. Miller, Gonzalo H. Hortelano, Anar Turmuhambetova, Antonella E. Chesca, Irina Kadyrova

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Introduction: The rapidly evolving COVID-19 pandemic, along with the re-emergence of pathogens causing acute respiratory infections (ARI), has necessitated the development of novel diagnostic tools to differentiate various causes of ARI. MALDI-MS, due to its wide usage and affordability, has been proposed as a potential instrument for diagnosing SARS-CoV-2 versus non-SARS-CoV-2 ARI. The aim of this study was to investigate the potential of MALDI-MS in conjunction with a machine learning model to accurately distinguish between symptomatic infections caused by SARS-CoV-2 and non-SARS-CoV-2 during both the early and later phases of the pandemic. Furthermore, this study aimed to analyze mass spectrometry (MS) data obtained from nasal swabs of healthy individuals. Methods: We gathered mass spectra from 252 samples, comprising 108 SARS-CoV-2-positive samples obtained in 2020 (Covid 2020), 7 SARS-CoV- 2-positive samples obtained in 2023 (Covid 2023), 71 samples from symptomatic individuals without SARS-CoV-2 (Control non-Covid ARVI), and 66 samples from healthy individuals (Control healthy). All the samples were subjected to RT-PCR testing. For data analysis, we employed the caret R package to train and test seven machine-learning algorithms: C5.0, KNN, NB, RF, SVM-L, SVM-R, and XGBoost. We conducted a training process using a five-fold (outer) nested repeated (five times) ten-fold (inner) cross-validation with a randomized stratified splitting approach. Results: In this study, we utilized the Covid 2020 dataset as a case group and the non-Covid ARVI dataset as a control group to train and test various machine learning (ML) models. Among these models, XGBoost and SVM-R demonstrated the highest performance, with accuracy values of 0.97 [0.93, 0.97] and 0.95 [0.95; 0.97], specificity values of 0.86 [0.71; 0.93] and 0.86 [0.79; 0.87], and sensitivity values of 0.984 [0.984; 1.000] and 1.000 [0.968; 1.000], respectively. When examining the Covid 2023 dataset, the Naive Bayes model achieved the highest classification accuracy of 43%, while XGBoost and SVM-R achieved accuracies of 14%. For the healthy control dataset, the accuracy of the models ranged from 0.27 [0.24; 0.32] for k-nearest neighbors to 0.44 [0.41; 0.45] for the Support Vector Machine with a radial basis function kernel. Conclusion: Therefore, ML models trained on MALDI MS of nasopharyngeal swabs obtained from patients with Covid during the initial phase of the pandemic, as well as symptomatic non-Covid individuals, showed excellent classification performance, which aligns with the results of previous studies. However, when applied to swabs from healthy individuals and a limited sample of patients with Covid in the late phase of the pandemic, ML models exhibited lower classification accuracy.

Keywords: SARS-CoV-2, MALDI-TOF MS, ML models, nasopharyngeal swabs, classification

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Authors: Emrehan Ozcan, Dilek Beyazlı

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The development of tourism depends on an accurate planning approach as well as on the right planning process. This dependency is also a key factor in ensuring sustainability of tourism. The types of tourism, social expectations, planning practice, the socio-economic and the cultural structure of the region are determinants of planning approaches for tourism development. The tourism plans prepared for the historic city centers are usually based on the revitalization of cultural and historical values. The preservation and development of the tourism potentials of the historic city centers are important for providing an economic contribution to the locality, creating livable solutions for local residents and also the sustainability of tourism. This research is about experiencing and discussing a planning approach that will provide tourism development based on historical and cultural values. Historical and cultural values in the historical city center of Trabzon -which has a settlement history of approximately 4000 years, is located on the Black Sea coast of Turkey- wear out over years and lose their tourism potential. A planning study has been experienced with strategic spatial planning approach for Trabzon, which has not done a tourism-oriented planning study until now. The stages of the planning process provided by strategic spatial planning approach are an assessment of the current situation; vision, strategies, and actions; action planning; designing and implementation of actions and monitoring-evaluation. In the discussion section, the advantages, planning process, methods and techniques of the approach are discussed for the possibilities and constraints in terms of tourism planning. In this context, it is aimed to put forth tourism planning process, stages, and implementation tools within the scope of strategic spatial planning approach by comparing approaches used in the tourism-oriented/priority planning of historical city centers. Suggestions on the position and effect of the preferred planning approach in the existing spatial planning practice are the outputs of the study.

Keywords: cultural heritage, tourism oriented planning, Trabzon, strategic spatial Planning

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Authors: Khanyisile Mnguni, Muthukrishnavellaisamy Kumarasamy, Jeff C. Smithers

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Umgeni Water is a water board that supplies most parts of KwaZulu Natal with bulk portable water. Currently, Umgeni Water is running its distribution system based on required reservoir levels and demands and does not consider the energy cost at different times of the day, number of pump switches, and background leakages. Including these constraints can reduce operational cost, energy usage, leakages, and increase performance. Optimising pump schedules can reduce energy usage and costs while adhering to hydraulic and operational constraints. Umgeni Water has installed an online hydraulic software, WaterNet Advisor, that allows running different operational scenarios prior to implementation in order to optimise the distribution system. This study will investigate operation scenarios using optimisation techniques and WaterNet Advisor for a local water distribution system. Based on studies reported in the literature, introducing pump scheduling optimisation can reduce energy usage by approximately 30% without any change in infrastructure. Including tariff structures in an optimisation problem can reduce pumping costs by 15%, while including leakages decreases cost by 10%, and pressure drop in the system can be up to 12 m. Genetical optimisation algorithms are widely used due to their ability to solve nonlinear, non-convex, and mixed-integer problems. Other methods such as branch and bound linear programming have also been successfully used. A suitable optimisation method will be chosen based on its efficiency. The objective of the study is to reduce energy usage, operational cost, and leakages, and the feasibility of optimal solution will be checked using the Waternet Advisor. This study will provide an overview of the optimisation of hydraulic networks and progress made to date in multi-objective optimisation for a selected sub-system operated by Umgeni Water.

Keywords: energy usage, pump scheduling, WaterNet Advisor, leakages

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Authors: Jinhoa Lee

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The relationships between environmental quality, energy use and economic output have created growing attention over the past decades among researchers and policy makers. Focusing on the empirical aspects of the role of CO2 emissions and energy use in affecting the economic output, this paper is an effort to fulfill the gap in a comprehensive case study at a country level using modern econometric techniques. To achieve the goal, this country-specific study examines the short-run and long-run relationships among energy consumption (using disaggregated energy sources: crude oil, coal, natural gas, electricity), carbon dioxide (CO2) emissions and gross domestic product (GDP) for Israel using time series analysis from the year 1980-2010. To investigate the relationships between the variables, this paper employs the Phillips–Perron (PP) test for stationarity, Johansen maximum likelihood method for cointegration and a Vector Error Correction Model (VECM) for both short- and long-run causality among the research variables for the sample. The long-run equilibrium in the VECM suggests significant positive impacts of coal and natural gas consumptions on GDP in Israel. In the short run, GDP positively affects coal consumption. While there exists a positive unidirectional causality running from coal consumption to consumption of petroleum products and the direct combustion of crude oil, there exists a negative unidirectional causality running from natural gas consumption to consumption of petroleum products and the direct combustion of crude oil in the short run. Overall, the results support arguments that there are relationships among environmental quality, energy use and economic output but the associations can to be differed by the sources of energy in the case of Israel over of period 1980-2010.

Keywords: CO2 emissions, energy consumption, GDP, Israel, time series analysis

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Authors: Sayyed Benyamin Alavi, Giovanni Cerri, Leila Chennaoui, Ambra Giovannelli, Stefano Mazzoni

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Primary energy consumption and emissions of pollutants (including CO2) sustainability call to search methodologies to lower power absorption for unit of a given product. Cool production plants based on vapour compression are widely used for many applications: air conditioning, food conservation, domestic refrigerators and freezers, special industrial processes, etc. In the field of cool production, the amount of Yearly Consumed Primary Energy is enormous, thus, saving some percentage of it, leads to big worldwide impact in the energy consumption and related energy sustainability. Among various techniques to reduce power required by a Vapour Compression Cool Production Plant (VCCPP), the technique based on Power Regeneration by means of Internal Direct Cycle (IDC) will be considered in this paper. Power produced by IDC reduces power need for unit of produced Cool Power by the VCCPP. The paper contains basic concepts that lead to develop IDCs and the proposed options to use the IDC Power. Among various selections for using turbo machines, Best Economically Available Technologies (BEATs) have been explored. Based on vehicle engine turbochargers, they have been taken into consideration for this application. According to BEAT Database and similarity rules, the best turbo machine selection leads to the minimum nominal power required by VCCPP Main Compressor. Results obtained installing the prototype in “ad hoc” designed test bench will be discussed and compared with the expected performance. Forecasts for the upgrading VCCPP, various applications will be given and discussed. 4-6% saving is expected for air conditioning cooling plants and 15-22% is expected for cryogenic plants.

Keywords: Refrigeration Plant, Vapour Pressure Amplifier, Compressor, Expander, Turbine, Turbomachinery Selection, Power Saving

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Authors: Elif Tugce Aksun Tumerkan

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Although seafood is an important part of human diet and categorized highly traded food industry internationally, it is remain overlooked generally in the global food security aspect. Food product authentication is the main interest in the aim of both avoids commercial fraud and to consider the risks that might be harmful to human health safety. In recent years, with increasing consumer demand for regarding food content and it's transparency, there are some instrumental analyses emerging for determining food fraud depend on some analytical methodologies such as proteomic and metabolomics. While, fish and seafood consumed as fresh previously, within advanced technology, processed or packaged seafood consumption have increased. After processing or packaging seafood, morphological identification is impossible when some of the external features have been removed. The main fish and seafood quality-related issues are the authentications of seafood contents such as mislabelling products which may be contaminated and replacement partly or completely, by lower quality or cheaper ones. For all mentioned reasons, truthful consistent and easily applicable analytical methods are needed for assurance the correct labelling and verifying of seafood products. DNA-barcoding methods become popular robust that used in taxonomic research for endangered or cryptic species in recent years; they are used for determining food traceability also. In this review, when comparing the other proteomic and metabolic analysis, DNA-based methods are allowing a chance to identification all type of food even as raw, spiced and processed products. This privilege caused by DNA is a comparatively stable molecule than protein and other molecules. Furthermore showing variations in sequence based on different species and founding in all organisms, make DNA-based analysis more preferable. This review was performed to clarify the main advantages of using DNA-barcoding for determining seafood fraud among other techniques.

Keywords: DNA-barcoding, genetic analysis, food fraud, mislabelling, packaged seafood

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Authors: Mikel Niño, Roberto V. Zicari, Todor Ivanov, Kim Hee, Naveed Mushtaq, Marten Rosselli, Concha Sánchez-Ocaña, Karsten Tolle, José Miguel Blanco, Arantza Illarramendi, Jörg Besier, Harry Underwood

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One of the application fields for data analysis techniques and technologies gaining momentum is the area of social good or “common good”, covering cases related to humanitarian crises, global health care, or ecology and environmental issues, among others. The promotion of data-driven projects in this field aims at increasing the efficacy and efficiency of social initiatives, improving the way these actions help humanity in general and people in need in particular. This application field, however, poses its own barriers and challenges when developing data-driven projects, lagging behind in comparison with other scenarios. These challenges derive from aspects such as the scope and scale of the social issue to solve, cultural and political barriers, the skills of main stakeholders and the technological resources available, the motivation to be engaged in such projects, or the ethical and legal issues related to sensitive data. This paper analyzes the application of data projects in the field of social good, reviewing its current state and noteworthy initiatives, and presenting a framework covering the key aspects to analyze in such projects. The goal is to provide guidelines to understand the main challenges and opportunities for this type of data project, as well as identifying the main differential issues compared to “classical” data projects in general. A case study is presented on the initial steps and stakeholder analysis of a data project for the inclusion of refugees in the city of Frankfurt, Germany, in order to empirically confront the framework with a real example.

Keywords: data-driven projects, humanitarian operations, personal and sensitive data, social good, stakeholders analysis

Procedia PDF Downloads 328

Authors: Halkiopoulos Constantinos, Gkintoni Evgenia, Koutsopoulou Ioanna, Antonopoulou Hera

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E-sports and e-gaming have emerged in recent years since the increase in internet use have become universal and e-gamers are the new reality in our homes. The excessive involvement of young adults with e-sports has already been revealed and the adverse consequences have been reported in researches in the past few years, but the issue has not been fully studied yet. The present research is conducted in Greece and studies the psychological profile of video game players and provides information on personality traits, habits and emotional status that affect online gamers’ behaviors in order to help professionals and policy makers address the problem. Three standardized self-report questionnaires were administered to participants who were young male and female adults aged from 19-26 years old. The Profile of Mood States (POMS) scale was used to evaluate people’s perceptions of their everyday life mood; the personality features that can trace back to people’s habits and anticipated reactions were measured by Eysenck Personality Questionnaire (EPQ), and the Trait Emotional Intelligence Questionnaire (TEIQue) was used to measure which cognitive (gamers’ beliefs) and emotional parameters (gamers’ emotional abilities) mainly affected/ predicted gamers’ behaviors and leisure time activities?/ gaming behaviors. Data mining techniques were used to analyze the data, which resulted in machine learning algorithms that were included in the software package R. The research findings attempt to designate the effect of personality traits, emotional status and emotional intelligence influence and correlation with e-sports, gamers’ behaviors and help policy makers and stakeholders take action, shape social policy and prevent the adverse consequences on young adults. The need for further research, prevention and treatment strategies is also addressed.

Keywords: e-sports, e-gamers, personality traits, POMS, emotional intelligence, data mining, R

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Authors: Chen Wang, Gerard Rowe

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The number of undergraduate engineering students enrolled in university has been increasing rapidly recently, leading to challenges associated with increased student-instructor ratios and increased diversity in academic preparedness of the entrants. An increased student-instructor ratio makes the interaction between teachers and students more difficult, with the resulting student ‘anonymity’ known to be a risk to academic success. With increasing student numbers, there is also an increasing diversity in the academic preparedness of the students at entry to university. Conceptual understanding of the entrants has been quantified via diagnostic testing, with the results for the first-year course in electrical engineering showing significant conceptual misunderstandings amongst the entry cohort. The solution is clearly multi-faceted, but part of the solution likely involves greater demands being placed on students to be masters of their own learning. In consequence, it is highly desirable that instructors help students to develop better self-regulated learning skills. A self-regulated learner is one who is capable of setting up their own learning goals, monitoring their study processes, adopting and adjusting learning strategies, and reflecting on their own study achievements. The methods by which instructors might cultivate students’ self-regulated learning abilities is receiving increasing attention from instructors and researchers. The aim of this study was to help students understand fully their self-regulated learning skill levels and provide targeted instructions to help them improve particular learning abilities in order to meet the curriculum requirements. As a survey tool, this research applied the questionnaire ‘Motivated Strategies for Learning Questionnaire’ (MSLQ) to collect first year engineering student’s self-reported data of their cognitive abilities, motivational orientations and learning strategies. MSLQ is a widely-used questionnaire for assessment of university student’s self-regulated learning skills. The questionnaire was offered online as a part of the online-scaffolding-learning tools to develop student understanding of self-regulated learning theories and learning strategies. The online tools, which have been under development since 2015, are designed to help first-year students understand their self-regulated learning skill levels by providing prompt feedback after they complete the questionnaire. In addition, the online tool also supplies corresponding learning strategies to students if they want to improve specific learning skills. A total of 866 first year engineering students who enrolled in the first-year electrical engineering course were invited to participate in this research project. By the end of the course 857 students responded and 738 of their questionnaires were considered as valid questionnaires. Analysis of these surveys showed that 66% of the students thought the online-scaffolding-learning tools helped significantly to improve their self-regulated learning abilities. It was particularly pleasing that 16.4% of the respondents thought the online-scaffolding-learning tools were extremely effective. A current thrust of our research is to investigate the relationships between students’ self-regulated learning abilities and their academic performance. Our results are being used by the course instructors as they revise the curriculum and pedagogy for this fundamental first-year engineering course, but the general principles we have identified are applicable to most first-year STEM courses.

Keywords: academic preparedness, online-scaffolding-learning tool, self-regulated learning, STEM education

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Authors: Tamar Trop, Michal Roffeh

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One of the emerging trends in construction is installing an exterior “green wall” (GW). GW is an overarching and most common term for various techniques of incorporating greenery into buildings’ vertical elements, mainly facades. This green infrastructure yields numerous benefits for the urban environment, the public, and the buildings’ tenants and users, such as enhancing air quality and biodiversity, managing stormwater runoff, mitigating urban heat island and climate change, improving urban aesthetics and mental wellbeing, improving indoor comfort conditions, and saving energy. Yet, the penetration rate of GWs into the construction market, especially into the housing sector, is still very slow. Furthermore, the research regarding prospective homebuyers’ willingness to purchase and pay a price premium for GW apartments is scarce and does not refer to newly built buildings and specific GW types. This research aims to narrow these knowledge gaps by exploring the willingness of prospective homebuyers in Israel to purchase a newly built apartment with a hydroponic living wall, the size of the PP that they would be willing to pay for it, and the various factors ̶ knowledge-related, concern, economic, and personal ̶ that influence these motivations. A nationwide online survey was conducted among a sample of 514 adults using a structured questionnaire. Findings show that despite low familiarity with GWs and strong concerns about various kinds of nuisance, technical issues, and maintenance costs, potential homebuyers express a relatively high willingness to purchase and pay a significant price premium for such an apartment. The main motivations behind this willingness were found to be potential energy savings and governmental incentives. Study findings can contribute to a better understanding of the maturity of the housing market in Israel to adopt GWs and to better tailor intervention tools for increasing GWs’ uptake among potential homebuyers.

Keywords: green façade, green wall, living wall, willingness to pay

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Authors: I. Kade Wiratama

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As a result of the significant growth of wind turbines in size, blade load control has become the main challenge for large wind turbines. Many advanced techniques have been investigated aiming at developing control devices to ease blade loading. Amongst them, trailing edge flaps have been proven as effective devices for load alleviation. The present study aims at investigating the potential benefits of flaps in enhancing the energy capture capabilities rather than blade load alleviation. A software tool is especially developed for the aerodynamic simulation of wind turbines utilising blades equipped with flaps. As part of the aerodynamic simulation of these wind turbines, the control system must be also simulated. The simulation of the control system is carried out via solving an optimisation problem which gives the best value for the controlling parameter at each wind turbine run condition. Developing a genetic algorithm optimisation tool which is especially designed for wind turbine blades and integrating it with the aerodynamic performance evaluator, a design optimisation tool for blades equipped with flaps is constructed. The design optimisation tool is employed to carry out design case studies. The results of design case studies on wind turbine AWT 27 reveal that, as expected, the location of flap is a key parameter influencing the amount of improvement in the power extraction. The best location for placing a flap is at about 70% of the blade span from the root of the blade. The size of the flap has also significant effect on the amount of enhancement in the average power. This effect, however, reduces dramatically as the size increases. For constant speed rotors, adding flaps without re-designing the topology of the blade can improve the power extraction capability as high as of about 5%. However, with re-designing the blade pretwist the overall improvement can be reached as high as 12%.

Keywords: flaps, design blade, optimisation, simulation, genetic algorithm, WTAero

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Authors: Adel Khorramrouz, Monireh Ahmadi Bani, Ehsan Norouzi, Morvarid Lalenoor

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Background: Plantar pressure distribution measurement has been used for a long time to assess foot disorders. Plantar pressure is an important component affecting the foot and ankle function and Changes in plantar pressure distribution could indicate various foot and ankle disorders. Morphologic and mechanical properties of the foot may be important factors affecting the plantar pressure distribution. Accurate and early measurement may help to reduce the prevalence of pes planus. With recent developments in technology, new techniques such as machine learning have been used to assist clinicians in predicting patients with foot disorders. Significance of the study: This study proposes a neural network learning-based flat foot classification methodology using static foot pressure distribution. Methodologies: Data were collected from 895 patients who were referred to a foot clinic due to foot disorders. Patients with pes planus were labeled by an experienced physician based on clinical examination. Then all subjects (with and without pes planus) were evaluated for static plantar pressures distribution. Patients who were diagnosed with the flat foot in both feet were included in the study. In the next step, the leg length was normalized and the network was trained for plantar pressure mapping images. Findings: From a total of 895 image data, 581 were labeled as pes planus. A computational neural network (CNN) ran to evaluate the performance of the proposed model. The prediction accuracy of the basic CNN-based model was performed and the prediction model was derived through the proposed methodology. In the basic CNN model, the training accuracy was 79.14%, and the test accuracy was 72.09%. Conclusion: This model can be easily and simply used by patients with pes planus and doctors to predict the classification of pes planus and prescreen for possible musculoskeletal disorders related to this condition. However, more models need to be considered and compared for higher accuracy.

Keywords: foot disorder, machine learning, neural network, pes planus

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Authors: Rajendra Jangde, Deependra Singh

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Present work was based with objective to release of the antimicrobial and debriding agent in sustained manner at the wound surface. In order to provide a long-lasting antimicrobial action and moist environment on wound space, Biocompatible moist system was developed for complete healing. In the present study, a biocompatible moist system of PVA-gelatin hydrogel was developed capable of carrying multiple drugs- Quercetin and Cabopol in controlled manner for effective and complete wound healing. Carbopol and Quercetin were prepared by thin film hydration techniques and optimized system was incorporated in PVA-Gelatin slurry. PVA-Gelatin hydrogels were prepared by freeze thaw method. The prepared dispersion was casted into films to prepare multiphase hydrogel system and characterized by in vitro and in vivo studies. Results revealed the uniform dispersion of microspheres in a three-dimensional matrix of the PVA-Gelatin hydrogel observed at different magnifications. The in vitro release data showed typical biphasic release pattern, i.e., a burst release followed by a slower sustained release for 5 days. Prepared system was found to be stable under both normal and accelerated conditions. Histopathological study showed significant (p<0.05) increase in fibroblast cells, collagen fibres and blood vessels formation. All parameters such as wound contraction, tensile strength, histopathological and biochemical parameters- hydroxyproline content, protein level, etc. were observed significant (p<0.05) in comparison to control group. Present results suggest an accelerated re-epithelialization under moist wound environment with delivery of multiple drugs effective at different stages of wound healing cascade with minimum disturbance of wound bed.

Keywords: multiphase hydrogel, optimization quercetin, wound healing

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Authors: Hadjer Didouh, Mohammed Hadj Melliani, Izzeddine Sameut Bouhaik

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Corrosion is a serious problem in industrial installations or metallic transport pipes. Corrosion is an interfacial process controlled by several parameters. The presence of microorganisms affects the kinetics of corrosion. This type of corrosion is often referred as bio-corrosion or corrosion influenced by microorganisms (MIC). The action of a microorganism or a bacterium is carried out by the formation of biofilm following its attachment to the metal surface. The formation of biofilm isolates the metal surface from its environment and allows the bacteria to control the parameters of the metal/bacteria interface. Biofilm formation by sulfate-reducing bacteria (SRB) X52 steel, poses substantial challenges in oil and gas industry SONATRACH of Algeria. This research delves into the complex attachment mechanisms employed by SRB biofilm on X52 carbon steel and investigates strategies for effective mitigation using biocides. The exploration commences by elucidating the underlying mechanisms facilitating SRB biofilm adhesion to X52 carbon steel, considering factors such as surface morphology, electrostatic interactions, and microbial extracellular substances. Advanced microscopy and spectroscopic techniques provide a support to the attachment processes, laying the foundation for targeted mitigation strategies. The use of 100 ppm of Moringa Oleifera extract biocide as a promising approach to control and prevent SRB biofilm formation on X52 carbon steel surfaces. Green extract undergo evaluation for their effectiveness in disrupting biofilm development while ensuring the integrity of the steel substrate. Systematic analysis is conducted on the biocide's impact on the biofilm's structural integrity, microbial viability, and overall attachment strength. This two-pronged investigation aims to deepen our comprehension of SRB biofilm dynamics and contribute to the development of effective strategies for mitigating its impact on X52 carbon steel.

Keywords: bio-corrosion, biofilm, attachement, metal/bacteria interface

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Authors: Ioanna Skoufali

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The present study is directed towards adaptation to climate change closely related to the phenomenon of the urban heat island (UHI). This issue is widespread and common to different urban realities, but particularly in Mediterranean cities that are characterized by dense urban. The attention of this work of redevelopment of the open space is focused on mitigation techniques aiming to solve local problems such as microclimatic parameters and the conditions of thermal comfort in summer, related to urban morphology. This quantitative analysis, evaluation, and verification survey involves the methodological elaboration applied in a real study case by Serres, through the experimental support of the ENVImet Pro V4.1 and BioMet software developed: i) in two phases concerning the anteoperam (phase a1 # 2013) and the post-operam (phase a2 # 2016); ii) in scenario A (+ 25% of green # 2017). The first study tends to identify the main intervention strategies, namely: the application of cool pavements, the increase of green surfaces, the creation of water surface and external fans; moreover, it obtains the minimum results achieved by the National Program 'Bioclimatic improvement project for public open space', EPPERAA (ESPA 2007-2013) related to the four environmental parameters illustrated below: the TAir = 1.5 o C, the TSurface = 6.5 o C, CDH = 30% and PET = 20%. In addition, the second study proposes a greater potential for improvement than postoperam intervention by increasing the vegetation within the district towards the SW/SE. The final objective of this in-depth design is to be transferable in homogeneous cases of urban regeneration processes with obvious effects on the efficiency of microclimatic mitigation and thermal comfort.

Keywords: cool pavements, microclimate parameters (TAir, Tsurface, Tmrt, CDH), mitigation strategies, outdoor thermal comfort (PET & UTCI)

Procedia PDF Downloads 202

Authors: Mohamed Aboelghar, Ayman Abou Hadid, Usama Albehairy, Asmaa Khater

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Sustainable agricultural development of the desert areas of Egypt under the pressure of irrigation water scarcity is a significant national challenge. Existing water harvesting techniques on the northwest coast of Egypt do not ensure the optimal use of rainfall for agricultural purposes. Basin-scale hydrology potentialities were studied to investigate how available annual rainfall could be used to increase agricultural production. All data related to agricultural production included in the form of geospatial layers. Thematic classification of Sentinal-2 imagery was carried out to produce the land cover and crop maps following the (FAO) system of land cover classification. Contour lines and spot height points were used to create a digital elevation model (DEM). Then, DEM was used to delineate basins, sub-basins, and water outlet points using the Soil and Water Assessment Tool (Arc SWAT). Main soil units of the study area identified from Land Master Plan maps. Climatic data collected from existing official sources. The amount of precipitation, surface water runoff, potential, and actual evapotranspiration for the years (2004 to 2017) shown as results of (Arc SWAT). The land cover map showed that the two tree crops (olive and fig) cover 195.8 km2 when herbaceous crops (barley and wheat) cover 154 km2. The maximum elevation was 250 meters above sea level when the lowest one was 3 meters below sea level. The study area receives a massive variable amount of precipitation; however, water harvesting methods are inappropriate to store water for purposes.

Keywords: water catchements, remote sensing, GIS, sustainable agricultural development

Procedia PDF Downloads 115

Authors: Marcio Mesquita, Diogo Henrique Morato de Moraes, Henrique Fonseca Elias de Oliveira, Rilner Alves Flores, Mateus Rodrigues Ferreira, Dalva Graciano Ribeiro

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This study aimed to analyze the movement of water in irrigated and non-irrigated rice (Oryza sativa L.) leaves, from the xylem to the stomata, through numerical simulations. Through three-dimensional modeling, it was possible to determine how the spacing of parenchyma cells and the permeability of these cells influence the apoplastic flow and the opening of the stomata. The thickness of the cuticle and the number of vascular bundles are greater in plants subjected to water stress, indicating an adaptive response of plants to environments with water deficit. In addition, numerical simulations revealed that the opening of the stomata, the permeability of the parenchyma cells and the cell spacing have significant impacts on the energy loss and the speed of water movement. It was observed that a more open stoma facilitates water flow, decreasing the resistance and energy required for transport, while higher levels of permeability reduce energy loss, indicating that a more permeable tissue allows for more efficient water transport. Furthermore, it was possible to note that stomatal aperture, parenchyma permeability and cell spacing are crucial factors in the efficient water management of plants, especially under water stress conditions. These insights are essential for the development of more effective agricultural management strategies and for the breeding of plant varieties that are more resistant to adverse growing conditions. Computed fluid dynamics has allowed us to overcome the limitations of conventional techniques by providing a means to visualize and understand the complex hydrodynamic processes within the vascular system of plants.

Keywords: numerical modeling, vascular anatomy, vascular hydrodynamics, xylem, Oryza sativa L.

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Authors: Jean Berger, Mohamed Barkaoui

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Discrete search path planning in time-constrained uncertain environment relying upon imperfect sensors is known to be hard, and current problem-solving techniques proposed so far to compute near real-time efficient path plans are mainly bounded to provide a few move solutions. A new information-theoretic –based open-loop decision model explicitly incorporating false alarm sensor readings, to solve a single agent military logistics search-and-delivery path planning problem with anticipated feedback is presented. The decision model consists in minimizing expected entropy considering anticipated possible observation outcomes over a given time horizon. The model captures uncertainty associated with observation events for all possible scenarios. Entropy represents a measure of uncertainty about the searched target location. Feedback information resulting from possible sensor observations outcomes along the projected path plan is exploited to update anticipated unit target occupancy beliefs. For the first time, a compact belief update formulation is generalized to explicitly include false positive observation events that may occur during plan execution. A novel genetic algorithm is then proposed to efficiently solve search path planning, providing near-optimal solutions for practical realistic problem instances. Given the run-time performance of the algorithm, natural extension to a closed-loop environment to progressively integrate real visit outcomes on a rolling time horizon can be easily envisioned. Computational results show the value of the approach in comparison to alternate heuristics.

Keywords: search path planning, false alarm, search-and-delivery, entropy, genetic algorithm

Procedia PDF Downloads 360

Authors: Richard Adu Antwako

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Coastal wetland destruction and its consequences have recently taken the center stage of global discussions. This phenomenon is no gray area to humanity as coastal wetland-human interaction seems inevitably ingrained in the earliest civilizations, amidst the demanding use of its resources to meet their necessities. The severity of coastal wetland destruction parallels with growing civilizations, and it is against this backdrop that, this paper interrogated the causes of coastal wetland destruction in Harper City in Liberia, compared the degree of coastal wetland stressors to the non-equilibrium thermodynamic scale as well as suggested an integrated coastal zone management to address the problems. Literature complemented the primary data gleaned via global positioning system devices, field observation, questionnaire, and interviews. Multi-sampling techniques were used to generate data from the sand miners, institutional heads, fisherfolk, community-based groups, and other stakeholders. Non-equilibrium thermodynamic theory remains vibrant in discerning the ecological stability, and it would be employed to further understand the coastal wetland destruction in Harper City, Liberia and to measure the coastal wetland stresses-amplitude and elasticity. The non-equilibrium thermodynamics postulates that the coastal wetlands are capable of assimilating resources (inputs), as well as discharging products (outputs). However, the input-output relationship exceedingly stretches beyond the thresholds of the coastal wetlands, leading to coastal wetland disequilibrium. Findings revealed that the sand mining, mangrove removal, and crude dumping have transformed the coastal wetlands, resulting in water pollution, flooding, habitat loss and disfigured beaches in Harper City in Liberia. This paper demonstrates that the coastal wetlands are converted into developmental projects and agricultural fields, thus, endangering the future society against nature.

Keywords: amplitude, crude dumping, elasticity, non-equilibrium thermodynamics, wetland destruction

Procedia PDF Downloads 141

Authors: Abdullah Bal

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This paper presents a one-class classification (OCC) technique based on Fukunaga-Koontz Transform (FKT) for binary classification problems. The FKT is originally a powerful tool to feature selection and ordering for two-class problems. To utilize the standard FKT for data domain description problem (i.e., one-class classification), in this paper, a set of non-class samples which exist outside of positive class (target class) describing boundary formed with limited training data has been constructed synthetically. The tunnel-like decision boundary around upper and lower border of target class samples has been designed using statistical properties of feature vectors belonging to the training data. To capture higher order of statistics of data and increase discrimination ability, the proposed method, termed one-class FKT (OC-FKT), has been extended to its nonlinear version via kernel machines and referred as OC-KFKT for short. Multiple kernel learning (MKL) is a favorable family of machine learning such that tries to find an optimal combination of a set of sub-kernels to achieve a better result. However, the discriminative ability of some of the base kernels may be low and the OC-KFKT designed by this type of kernels leads to unsatisfactory classification performance. To address this problem, the quality of sub-kernels should be evaluated, and the weak kernels must be discarded before the final decision making process. MKL/OC-FKT and selective MKL/OC-FKT frameworks have been designed stimulated by ensemble learning (EL) to weight and then select the sub-classifiers using the discriminability and diversities measured by eigenvalue ratios. The eigenvalue ratios have been assessed based on their regions on the FKT subspaces. The comparative experiments, performed on various low and high dimensional data, against state-of-the-art algorithms confirm the effectiveness of our techniques, especially in case of small sample size (SSS) conditions.

Keywords: ensemble methods, fukunaga-koontz transform, kernel-based methods, multiple kernel learning, one-class classification

Procedia PDF Downloads 21

Authors: Gihan Thilakarathna, Fernando Ishara, Rathnayake Yasith, Bandara A. M. R. Y.

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E-learning is a crucial component of intelligent education. Even in the midst of a pandemic, E-learning is becoming increasingly important in the educational system. Several e-learning programs are accessible for students. Here, we decided to create an e-learning framework for children. We've found a few issues that teachers are having with their online classes. When there are numerous students in an online classroom, how does a teacher recognize a student's focus on academics and below-the-surface behaviors? Some kids are not paying attention in class, and others are napping. The teacher is unable to keep track of each and every student. Key challenge in e-learning is online exams. Because students can cheat easily during online exams. Hence there is need of exam proctoring is occurred. In here we propose an automated online exam cheating detection method using a web camera. The purpose of this project is to present an E-learning platform for math education and include games for kids as an alternative teaching method for math students. The game will be accessible via a web browser. The imagery in the game is drawn in a cartoonish style. This will help students learn math through games. Everything in this day and age is moving towards automation. However, automatic answer evaluation is only available for MCQ-based questions. As a result, the checker has a difficult time evaluating the theory solution. The current system requires more manpower and takes a long time to evaluate responses. It's also possible to mark two identical responses differently and receive two different grades. As a result, this application employs machine learning techniques to provide an automatic evaluation of subjective responses based on the keyword provided to the computer as student input, resulting in a fair distribution of marks. In addition, it will save time and manpower. We used deep learning, machine learning, image processing and natural language technologies to develop these research components.

Keywords: math, education games, e-learning platform, artificial intelligence

Procedia PDF Downloads 156

Authors: Yusuf Hussaini Atulukwu, Daramola Japheth, Tabitit S. Tabiti, Daramola Elizabeth Lara

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In view of the recent limitations faced by the township concerning poorly constructed and in some cases non - existence of drainage facilities that resulted into incessant flooding in some parts of the community poses threat to life,property and the environment. The research seeks to address this issue by showing the spatial distribution of drainage network in Bida Urban using Geographic information System techniques. Relevant features were extracted from existing Bida based Map using un-screen digitization and x, y, z, data of existing drainages were acquired using handheld Global Positioning System (GPS). These data were uploaded into ArcGIS 9.2, software, and stored in the relational database structure that was used to produce the spatial data drainage network of the township. The result revealed that about 40 % of the drainages are blocked with sand and refuse, 35 % water-logged as a result of building across erosion channels and dilapidated bridges as a result of lack of drainage along major roads. The study thus concluded that drainage network systems in Bida community are not in good working condition and urgent measures must be initiated in order to avoid future disasters especially with the raining season setting in. Based on the above findings, the study therefore recommends that people within the locality should avoid dumping municipal waste within the drainage path while sand blocked or weed blocked drains should be clear by the authority concerned. In the same vein the authority should ensured that contract of drainage construction be awarded to professionals and all the natural drainages caused by erosion should be addressed to avoid future disasters.

Keywords: drainage network, spatial, digitization, relational database, waste

Procedia PDF Downloads 334

Authors: Sitarrine Thongpussawal, Dasarath Jayasuriya, Thanaroj Woraratprasert, Sakawtree Prajamwong

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Thailand grapples with recurrent floods causing substantial repercussions on its economy, society, and environment. In 2021, the economic toll of these floods amounted to an estimated 53,282 million baht, primarily impacting the agricultural sector. The existing flood monitoring system in Thailand suffers from inaccuracies and insufficient information, resulting in delayed warnings and ineffective communication to the public. The Office of the National Water Resources (OWNR) is tasked with developing and integrating data and information systems for efficient water resources management, yet faces challenges in monitoring accuracy, forecasting, and timely warnings. This study endeavors to evaluate the viability of enhancing Thailand's Flood Forecasting and Warning (FFW) systems. Additionally, it aims to formulate a comprehensive work package grounded in international best practices to enhance the country's FFW systems. Employing qualitative research methodologies, the study conducted in-depth interviews and focus groups with pertinent agencies. Data analysis involved techniques like note-taking and document analysis. The study substantiates the feasibility of developing and enhancing FFW systems in Thailand. Implementation of international best practices can augment the precision of flood forecasting and warning systems, empowering local agencies and residents in high-risk areas to prepare proactively, thereby minimizing the adverse impact of floods on lives and property. This research underscores that Thailand can feasibly advance its FFW systems by adopting international best practices, enhancing accuracy, and improving preparedness. Consequently, the study enriches the theoretical understanding of flood forecasting and warning systems and furnishes valuable recommendations for their enhancement in Thailand.

Keywords: flooding, forecasting, warning, monitoring, communication, Thailand

Procedia PDF Downloads 61

Authors: Uzaira Rafique, Kousar Parveen

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The discharge volume and composition of Textile effluents gains scientific concern due to its hazards and biotoxcity of azo dyes. Azo dyes are non-biodegradable due to its complex molecular structure and recalcitrant nature. Serious attempts have been made to synthesize and develop new materials to combat the environmental problems. The present study is designed for removal of a range of azo dyes (Methyl orange, Congo red and Basic fuchsine) from synthetic aqueous solutions and real textile effluents. For this purpose, Metal (cobalt) doped alumina hybrids are synthesized and applied as adsorbents in the batch experiment. Two different aluminium precursor (aluminium nitrate and spent aluminium foil) and glucose are mixed following sol gel method to get hybrids. The synthesized materials are characterized for surface and bulk properties using FTIR, SEM-EDX and XRD techniques. The characterization of materials under FTIR revealed that –OH (3487-3504 cm-1), C-H (2935-2985 cm-1), Al-O (~ 800 cm-1), Al-O-C (~1380 cm-1), Al-O-Al (659-669 cm-1) groups participates in the binding of dyes onto the surface of hybrids. Amorphous shaped particles and elemental composition of carbon (23%-44%), aluminium (29%-395%), and oxygen (11%-20%) is demonstrated in SEM-EDX micrograph. Time-dependent batch-experiments under identical experimental parameters showed 74% congo red, 68% methyl orange and 85% maximum removal of basic fuchsine onto the surface of cobalt doped alumina hybrids probably through the ion-exchange mechanism. The experimental data when treated with adsorption models is found to have good agreement with pseudo second order kinetic and freundlich isotherm for adsorption process. The present study concludes the successful synthesis of novel and efficient cobalt doped alumina hybrids providing environmental friendly and economical alternative to the commercial adsorbents for the treatment of industrial effluents.

Keywords: alumina hybrid, adsorption, dopant, isotherm, kinetic

Procedia PDF Downloads 193

Authors: Dawit Thomas

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Feeding and weaning practices vary from culture to culture and depend on different child-rearing values. The socio-cultural dimensions that influence the acceptable infant feeding practices are varied and complex. Understanding cultural differences in beliefs and practices relating to infant feeding is important to enhance designing programs for delivering successful psychological, social, physiological and economic well-being of mothers and infants. The main purpose of this study was exploring mothers infant feeding practices in the context of Hadiyya culture. After purposively selecting Harche Huyaya Uyaya Kebele eight infant feeding mothers were selected using snowball sampling technique. The study employed interviews and focus group discussion. The study found out early initiation and prolonged breastfeeding and early complementary feeding in some instances immediately after birth. In addition, infants were not forced to wean unless the mothers encounter pressing issues like pregnancy and health related problems. Furthermore, the main weaning techniques were putting unpleasant materials on the tip of nipples and sending infants to grandparents home. The study also found out gender difference in weaning, i.e., early initiation of weaning for girls. This can be indicative of gender-based bias on weaning practice. Finally, health extension workers, office of women and children affairs and Hadiyya Zone Tourism office should organize awareness raising programs to preserve vital infant feeding practices like prolonged breastfeeding and length of weaning. In addition, the offices should raise awareness among communities on negative side effects of sending infant to grandparents home that may weaken infant-mothers attachment and create favorable ground for the development of phobia.

Keywords: feeding, infant, practices, weaning

Procedia PDF Downloads 325

Authors: Ahmed Khalil, Elhamy Tarabees, Svetlana Kovacikova

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The Red Sea Mountains have been famous for their ore deposits since ancient times. Also, petrographic analysis and previous potential field surveys indicated large unexplored accumulations of ore minerals in the area. Therefore, the main goal of the presented study is to contribute to the discovery of hitherto unknown ore mineral deposits in the Red Sea region. To achieve this goal, we used two geophysical techniques: land magnetic survey and magnetotelluric data. A high-resolution land magnetic survey has been acquired using two proton magnetometers, one instrument used as a base station for the diurnal correction and the other used to measure the magnetic field along the study area. Two hundred eighty land magnetic stations were measured over a mesh-like area with a 500m spacing interval. The necessary reductions concerning daily variation, regional gradient and time observation were applied. Then, the total intensity anomaly map was constructed and transformed into the reduced magnetic pole (RTP). The magnetic interpretation was carried out using the analytical signal as well as regional–residual separation is carried out using the power spectrum. Also, the tilt derivative method (TDR) technique is applied to delineate the structure and hidden anomalies. Data analysis has been performed using trend analysis and Euler deconvolution. The results indicate that magnetic contacts are not the dominant geological feature of the study area. The magnetotleruric survey consisted of two profiles with a total of 8 broadband measurement points with a duration of about 24 hours crossing a wadi um Gheig approximately 50 km south of El Quseir. Collected data have been inverted to the electrical resistivity model using the 3D modular 3D inversion technique ModEM. The model revealed a non-conductive body in its central part, probably corresponding to a dolerite dyke, with which possible ore mineralization could be related.

Keywords: magnetic survey, magnetotelluric, mineralization, 3d modeling

Procedia PDF Downloads 27

Authors: Bahram Fathi, Karim Alizadeh, Azam Mohammadbagheri

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The aim of this paper is to investigate the role of oil price shocks in explaining business cycles in Iran using a dynamic stochastic general equilibrium approach. This model incorporates both productivity and oil revenue shocks. The results indicate that productivity shocks are relatively more important to business cycles than oil shocks. The model with two shocks produces different values for volatility, but these values have the same ranking as that of the actual data for most variables. In addition, the actual data are close to the ratio of standard deviations to the output obtained from the model with two shocks. The results indicate that productivity shocks are relatively more important to business cycles than the oil shocks. The model with only a productivity shock produces the most similar figures in term of volatility magnitude to that of the actual data. Next, we use the Impulse Response Functions (IRF) to evaluate the capability of the model. The IRF shows no effect of an oil shock on the capital stocks and on labor hours, which is a feature of the model. When the log-linearized system of equations is solved numerically, investment and labor hours were not found to be functions of the oil shock. This research recommends using different techniques to compare the model’s robustness. One method by which to do this is to have all decision variables as a function of the oil shock by inducing the stationary to the model differently. Another method is to impose a bond adjustment cost. This study intends to fill that gap. To achieve this objective, we derive a DSGE model that allows for the world oil price and productivity shocks. Second, we calibrate the model to the Iran economy. Next, we compare the moments from the theoretical model with both single and multiple shocks with that obtained from the actual data to see the extent to which business cycles in Iran can be explained by total oil revenue shock. Then, we use an impulse response function to evaluate the role of world oil price shocks. Finally, I present implications of the findings and interpretations in accordance with economic theory.

Keywords: oil price, shocks, dynamic stochastic general equilibrium, Iran

Procedia PDF Downloads 438