Search results for: human behaviour; socio-hydrology; water resource systems

Authors: Zhi Hao Chow, Cian Mulligan, Jack Walsh, Antonio Garzon Vico, Dimitar Krastev

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Drug development is resource-intensive, time-consuming, and increasingly expensive with each developmental stage. The success rates of drug development are also relatively low, and the resources committed are wasted with each failed candidate. As such, a reliable method of predicting the success of drug development is in demand. The hypothesis was that some examples of failed drug candidates are pushed through developmental pipelines based on false confidence and may possess common linguistic features identifiable through sentiment analysis. Here, the concept of using text analysis to discover such features in research publications and investor reports as predictors of success was explored. R studios were used to perform text mining and lexicon-based sentiment analysis to identify affective phrases and determine their frequency in each document, then using SPSS to determine the relationship between our defined variables and the accuracy of predicting outcomes. A total of 161 publications were collected and categorised into 4 groups: (i) Cancer treatment, (ii) Neurodegenerative disease treatment, (iii) Vaccines, and (iv) Others (containing all other drugs that do not fit into the 3 categories). Text analysis was then performed on each document using 2 separate datasets (BING and AFINN) in R within the category of drugs to determine the frequency of positive or negative phrases in each document. A relative positivity and negativity value were then calculated by dividing the frequency of phrases with the word count of each document. Regression analysis was then performed with SPSS statistical software on each dataset (values from using BING or AFINN dataset during text analysis) using a random selection of 61 documents to construct a model. The remaining documents were then used to determine the predictive power of the models. Model constructed from BING predicts the outcome of drug performance in clinical trials with an overall percentage of 65.3%. AFINN model had a lower accuracy at predicting outcomes compared to the BING model at 62.5% but was not effective at predicting the failure of drugs in clinical trials. Overall, the study did not show significant efficacy of the model at predicting outcomes of drugs in development. Many improvements may need to be made to later iterations of the model to sufficiently increase the accuracy.

Keywords: data analysis, drug development, sentiment analysis, text-mining

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Authors: Reza Reiazi

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Attempts to verify Geant4 hadronic physic to transport antiproton beam using standard physics list have not reach to a reasonable results because of lack of reliable cross section data or non reliable model to predict the final states of annihilated particles. Since most of the antiproton annihilation energy is carried away by recoiling nuclear fragments which are result of pions interactions with surrounding nucleons, it should be investigated if the toolkit verified for pions. Geant4 version 9.4.6.p01 was used. Dose calculation was done using 700 MeV pions hitting a water tank applying standards physic lists. We conclude Geant4 standard physics lists to predict the depth dose of Pion minus beam is not same for all investigated models. Since the nuclear fragments will deposit their energy in a small distance, they are the most important source of dose deposition in the annihilation vertex of antiproton beams.

Keywords: Monte Carlo, Pion, simulation, antiproton beam

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Authors: Kamal Donko

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In the frontier zone of Benin Republic, land seems to be a fundamental political resource as it is used as a tool for socio-political mobilization, blackmail, inclusion and exclusion, conquest and political control. This paper seeks to examine the complex and intriguing interlinks between land, identity and politics in central Benin. It aims to investigate what roles territorialisation and land ownership are playing in the electioneering process in central Benin. It employs ethnographic multi-sited approach to data collections including observations, interviews and focused group discussions. Research findings reveal a complex and intriguing relationship between land ownership and politics in central Benin. Land is found to be playing a key role in the electioneering process in the region. The study has also discovered many emerging socio-spatial patterns of controlling and maintaining political power in the zone which are tied to land politics. These include identity reconstruction and integration mechanism through intermarriages, socio-political initiatives and construction of infrastructure of sovereignty. It was also found that ‘Diaspora organizations’ and identity issues; strategic creation of administrative units; alliance building strategy; gerrymandering local political field, etc. These emerging socio-spatial patterns of territorialisation for maintaining political power affect migrant and native communities’ relationships. It was also found that ‘Diaspora organizations’ and identity issues; strategic creation of administrative units; alliance building strategy; gerrymandering local political field, etc. are currently affecting migrant’s and natives’ relationships. The study argues that territorialisation is not only about national boundaries and the demarcation between different nation states, but more importantly, it serves as a powerful tool of domination and political control at the grass root level. Furthermore, this study seems to provide another perspective from which the political situation in Africa can be studied. Investigating how the dynamics of land ownership is influencing politics at the grass root or micro level, this study is fundamental to understanding spatial issues in the frontier zone.

Keywords: land, migration, politics, territorialisation

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Authors: Paramjit Kaur, Harish Kundra

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In the recent years, swarm intelligence based firefly algorithm has become a great focus for the researchers to solve the real time optimization problems. Here, firefly algorithm is used for the application of satellite image classification. For experimentation, Alwar area is considered to multiple land features like vegetation, barren, hilly, residential and water surface. Alwar dataset is considered with seven band satellite images. Firefly Algorithm is based on the attraction of less bright fireflies towards more brightener one. For the evaluation of proposed concept accuracy assessment parameters are calculated using error matrix. With the help of Error matrix, parameters of Kappa Coefficient, Overall Accuracy and feature wise accuracy parameters of user’s accuracy & producer’s accuracy can be calculated. Overall results are compared with BBO, PSO, Hybrid FPAB/BBO, Hybrid ACO/SOFM and Hybrid ACO/BBO based on the kappa coefficient and overall accuracy parameters.

Keywords: image classification, firefly algorithm, satellite image classification, terrain classification

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Authors: Leonardo Coloma

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The world changes constantly, every second its properties vary due either natural factors or human intervention. As the most intelligent creatures on the planet, human beings have transformed the environment and paradoxically –have allowed ‘mother nature’ to lose species, accelerate the processes of climate change, the deterioration of the ozone layer, among others. The rapid population growth, the procurement, administration and distribution of resources, waste management, and technological advances are some of the factors that boost urban sprawl whose gray stain extends over the territory, facing challenges such as pollution, overpopulation and scarcity of resources. In Ecuador, these problems are added to the social, cultural, economic and political anomalies that have historically affected it. This fact can represent a greater delay when trying to solve global problems, without having paid attention to local inconveniences –smaller ones, but ones that could be the key to project smart solutions on bigger ones. This research aims to highlight the main characteristics of the development models adopted by two Amazonian cities, and analyze the impact of such urban growth on society; to finally define the parameters that would allow the development of an intelligent city in Ecuador, prepared for the challenges of the XXI Century. Contrasts in the climate, temperature, and landscape of Ecuadorian cities are fused with the cultural diversity of its people, generating a multiplicity of nuances of an indecipherable wealth. However, we strive to apply development models that do not recognize that wealth, not understanding them and ignoring that their proposals will vary according to where they are applied. Urban plans seem to take a bit of each of the new theories and proposals of development, which, in the encounter with the informal growth of cities, with those excluded and ‘isolated’ societies, generate absurd morphologies - where the uncertain becomes tangible. The desire to project smart cities is ever growing, but it is important to consider that this concept does not only have to do with the use of information and communication technologies. Its success is achieved when advances in science and technology allow the establishment of a better relationship between people and their context (natural and built). As a research methodology, urban analysis through mappings, diagrams and geographical studies, as well as the identification of sensorial elements when living the city, will make evident the shortcomings of the urban models adopted by certain populations of the Ecuadorian Amazon. Following the vision of previous investigations started since 2014 as part of ‘Centro de Acciones Urbanas,’ the results of this study will encourage the dialogue between the city (as a physical fact) and those who ‘make the city’ (people as its main actors). This research will allow the development of workshops and meetings with different professionals, organizations and individuals in general.

Keywords: Latin American cities, smart cities, urban development, urban morphology, urban sprawl

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Authors: Ahmed E. Al-Sabri, Mohamed A. Moslem, Sarfaraz Hadi

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To avoid the harmful effects of the chemical fungicides on the human and minimize the environmental pollution, an alternative eco-friendly control strategies should be developed. The extract of Commiphora myhrra L. was tested against twenty fungal genera isolated from the indoor air collected from different rooms in King Saud University, Kingdom of Saudi Arabia. Disc diffusion test was modified for use in this study and the collected data was statistically analyzed. Variable antifungal efficacy of different myrrh extract was recorded against the investigated fungal genera. The efficacy of the extract was increased as the concentration increased. The highest growth inhibition (74.6%) was against Acremonium strictum followed by Trichoderma psuedokoningii (70.6%). On contrast, the lowest efficacy (12.7%) was against Ulocladium consortiale. It could be concluded that myrrh extract is promised as a source of substances from which of safer and eco-friendly could be used as antimicrobial agents against a number of pathogenic fungi.

Keywords: antifungal, myrrh, antimicrobial, medicinal plant

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Authors: Tahraoui Boualem

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Researching the historical framework of international crime means examining the historical facts that have contributed to uncovering this serious crime affecting international interests, and the law by which the study of the subject of international crime is determined is international criminal law, which is a branch of public international law. In this context, the historical study of international crime means recognizing the existence of an international community governed by international law, which makes us acknowledge that ancient societies lacked such stable and recurring international relations. Therefore, an attempt to monitor international crime in those ancient societies is only to demonstrate a historical fact that those societies have known some features of this crime, and have contributed in one way or another to the development of international criminal law without defining its concept or legal nature. The international community has affirmed the principle of establishing peace, achieving security, and respecting human rights. As a basis for friendly relations between the people of the international community and in case of prejudice, such as the aggressors breaching the obligations imposed on them, whether in time of peace or war.

Keywords: historical framework, of international crime, peace or war., international law

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Authors: Pawel Martynowicz

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Vibrations are a crucial problem for slender structures such as towers, masts, chimneys, wind turbines, bridges, high buildings, etc., that is why most of them are equipped with vibration attenuation or fatigue reduction solutions. In this work, a slender structure (i.e., wind turbine tower-nacelle model) equipped with nonlinear, semiactive tuned vibration absorber(s) is analyzed. For this study purposes, magnetorheological (MR) dampers are used as semiactive actuators. Several optimal-based approaches to structural vibration attenuation are investigated against the standard ‘ground-hook’ law and passive tuned vibration absorber(s) implementations. The common approach to optimal control of nonlinear systems is offline computation of the optimal solution, however, so determined open loop control suffers from lack of robustness to uncertainties (e.g., unmodelled dynamics, perturbations of external forces or initial conditions), and thus perturbation control techniques are often used. However, proper linearization may be an issue for highly nonlinear systems with implicit relations between state, co-state, and control. The main contribution of the author is the development as well as numerical and experimental verification of the Pontriagin maximum-principle-based vibration control concepts that produce directly actuator control input (not the demanded force), thus force tracking algorithm that results in control inaccuracy is entirely omitted. These concepts, including one-step optimal control, quasi-optimal control, and optimal-based modified ‘ground-hook’ law, can be directly implemented in online and real-time feedback control for periodic (or semi-periodic) disturbances with invariant or time-varying parameters, as well as for non-periodic, transient or random disturbances, what is a limitation for some other known solutions. No offline calculation, excitations/disturbances assumption or vibration frequency determination is necessary, moreover, all of the nonlinear actuator (MR damper) force constraints, i.e., no active forces, lower and upper saturation limits, hysteresis-type dynamics, etc., are embedded in the control technique, thus the solution is optimal or suboptimal for the assumed actuator, respecting its limitations. Depending on the selected method variant, a moderate or decisive reduction in the computational load is possible compared to other methods of nonlinear optimal control, while assuring the quality and robustness of the vibration reduction system, as well as considering multi-pronged operational aspects, such as possible minimization of the amplitude of the deflection and acceleration of the vibrating structure, its potential and/or kinetic energy, required actuator force, control input (e.g. electric current in the MR damper coil) and/or stroke amplitude. The developed solutions are characterized by high vibration reduction efficiency – the obtained maximum values of the dynamic amplification factor are close to 2.0, while for the best of the passive systems, these values exceed 3.5.

Keywords: magnetorheological damper, nonlinear tuned vibration absorber, optimal control, real-time structural vibration attenuation, wind turbines

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Authors: Jeremy Bekker, Guy Salazar

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Introduction: Nature-exposure interventions on university campuses may serve as an effective addition to overburdened counseling and student support centers. Nature-exposure interventions can work as a preventative well-being enhancement measure on campuses, which can be used adjacently with existing health resources. Specifically, this paper analyzes how spending time in nature impacts psychological well-being, cognitive functioning, and physical health. The poster covers the core findings and recommendations of this paper, which has been previously published in the BYU undergraduate psychology journal Intuition. Research Goals and Method: The goal of this paper was to outline the potential benefits of nature exposure for students’ physical health, mental well-being, and academic success. Another objective of this paper was to outline potential research-based interventions that use campus green spaces to improve student outcomes. Given that the core objective of this paper was to identify and establish research-based nature exposure interventions that could be used on college campuses, a broad literature review focused on these areas. Specifically, the databases Scopus and PsycINFO were used to screen for research focused on psychological well-being, physical health, cognitive functioning, and nature exposure interventions. Outcomes: Nature exposure has been shown to help increase positive affect, life satisfaction, happiness, coping ability and subjective well-being. Further, nature exposure has been shown to decrease negative affect, lower mental distress, reduce cognitive load, and decrease negative psychological symptoms. Finally, nature exposure has been shown to lead to better physical health. Findings and Recommendations: Potential interventions include adding green space to university buildings and grounds, dedicating already natural environments as nature restoration areas, and providing means for outdoor excursions. Potential limitations and suggested areas for future research are also addressed. Many campuses already contain green spaces, defined as any part of an environment that is predominately made of natural elements, and these green spaces comprise an untapped resource that is relatively cheap and simple.

Keywords: nature exposure, preventative care, undergraduate mental health, well-being intervention

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Authors: Samira Ferhat, Redha Alouaoui, Leila Trifi, Abdelmalek Badis

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The objective of this work is the use of natural surfactant (biosurfactant) and synthetic (sodium dodecyl sulfate and tween 80) for environmental application. In fact the solubility of the polycyclic hydrocarbon (naphthalene) and the desorption of the heavy metals in the presence of surfactants. The microorganisms selected in this work are bacterial strain (Bacillus licheniformis) for the production of biosurfactant for use in this study. In the first part of this study, we evaluated the effectiveness of surfactants solubilization certain hydrocarbons few soluble in water such as polyaromatic (case naphthalene). Tests have shown that from the critical micelle concentration, decontamination is performed. The second part presents the results on the desorption of heavy metals (for copper) by the three surfactants, using concentrations above the critical micelle concentration. The comparison between the desorption of copper by the three surfactants, it is shown that the biosurfactant is more effective than tween 80 and sodium dodecyl sulfate.

Keywords: surfactants, biosurfactant, naphthalene, copper, critical micelle concentration, solubilization, desorption

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Authors: Ramazan Bakir, Gizem Kayar

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It is known that Alzheimer and Dementia are the two most common types of Neurodegenerative diseases and their visibility is getting accelerated for the last couple of years. As the population sees older ages all over the world, researchers expect to see the rate of this acceleration much higher. However, unfortunately, there is no known pharmacological cure for both, although some help to reduce the rate of cognitive decline speed. This is why we encounter with non-pharmacological treatment and tracking methods more for the last five years. Many researchers, including well-known associations and hospitals, lean towards using non-pharmacological methods to support cognitive function and improve the patient’s life quality. As the dementia symptoms related to mind, learning, memory, speaking, problem-solving, social abilities and daily activities gradually worsen over the years, many researchers know that cognitive support should start from the very beginning of the symptoms in order to slow down the decline. At this point, life of a patient and caregiver can be improved with some daily activities and applications. These activities include but not limited to basic word puzzles, daily cleaning activities, taking notes. Later, these activities and their results should be observed carefully and it is only possible during patient/caregiver and M.D. in-person meetings in hospitals. These meetings can be quite time-consuming, exhausting and financially ineffective for hospitals, medical doctors, caregivers and especially for patients. On the other hand, digital support systems are showing positive results for all stakeholders of healthcare systems. This can be observed in countries that started Telemedicine systems. The biggest potential of our system is setting the inter-user communication up in the best possible way. In our project, we propose Machine Learning based digitalization of validated traditional cognitive tests (e.g. MOCA, Afazi, left-right hemisphere), their analyses for high-quality follow-up and communication systems for all stakeholders. R. Bakir and G. Kayar are with Gefeasoft, Inc, R&D – Software Development and Health Technologies company. Emails: ramazan, gizem @ gefeasoft.com This platform has a high potential not only for patient tracking but also for making all stakeholders feel safe through all stages. As the registered hospitals assign corresponding medical doctors to the system, these MDs are able to register their own patients and assign special tasks for each patient. With our integrated machine learning support, MDs are able to track the failure and success rates of each patient and also see general averages among similarly progressed patients. In addition, our platform also supports multi-player technology which helps patients play with their caregivers so that they feel much safer at any point they are uncomfortable. By also gamifying the daily household activities, the patients will be able to repeat their social tasks and we will provide non-pharmacological reminiscence therapy (RT – life review therapy). All collected data will be mined by our data scientists and analyzed meaningfully. In addition, we will also add gamification modules for caregivers based on Naomi Feil’s Validation Therapy. Both are behaving positively to the patient and keeping yourself mentally healthy is important for caregivers. We aim to provide a therapy system based on gamification for them, too. When this project accomplishes all the above-written tasks, patients will have the chance to do many tasks at home remotely and MDs will be able to follow them up very effectively. We propose a complete platform and the whole project is both time and cost-effective for supporting all stakeholders.

Keywords: alzheimer’s, dementia, cognitive functionality, cognitive tests, serious games, machine learning, artificial intelligence, digitalization, non-pharmacological, data analysis, telemedicine, e-health, health-tech, gamification

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Authors: Margaret Nohilly, Fionnuala Tynan

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Wellbeing in not only a national priority in Ireland but in the international context. A review of the literature highlights the consistent efforts of researchers to define the concept of wellbeing. This study sought to explore the understating of Wellbeing in Irish primary schools. National Wellbeing Guidelines in the Irish context frame the concept of wellbeing through a mental health paradigm, which is but one aspect of wellbeing. This exploratory research sought the views of Irish primary school teachers on their understanding of the concept of wellbeing and the practical application of strategies to promote wellbeing both in the classroom and across the school. Teacher participants from four counties in the West of Ireland were invited to participate in focus group discussion and workshops through the Education Centre Network. The purpose of this process was twofold; firstly to explore teachers’ understanding of wellbeing in the primary school context and, secondly, for teachers to be co-creators in the development of practical strategies for classroom and whole school implementation. The voice of the teacher participants was central to the research design. The findings of this study indicate that the definition of wellbeing in the Irish context is too abstract a definition for teachers and the focus on mental health dominates the discourse in relation to wellbeing. Few teachers felt that they were addressing wellbeing adequately in their classrooms and across the school. The findings from the focus groups highlighted that while teachers are incorporating a range of wellbeing strategies including mindfulness and positive psychology, there is a clear disconnect between the national definition and the implementation of national curricula which causes them concern. The teacher participants requested further practical strategies to promote wellbeing at whole school and classroom level within the framework of the Irish Primary School Curriculum and enable them to become professionally confident in developing a culture of wellbeing. In conclusion, considering wellbeing is a national priority in Ireland, this research promoted the timely discussion the wellbeing guidelines and the development of a conceptual framework to define wellbeing in concrete terms for practitioners. The centrality of teacher voices ensured the strategies proposed by this research is both practical and effective. The findings of this research have prompted the development of a national resource which will support the implementation of wellbeing in the primary school at both national and international level.

Keywords: definition, wellbeing, strategies, curriculum

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Authors: Nan Hu, Chao Wang, Xi Li, Xuehai Zhou

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The field-programmable gate array (FPGA) has been widely adopted in the high-performance computing domain. In recent years, the embedded system-on-a-chip (SoC) contains coarse granularity multi-core CPU (central processing unit) and mobile GPU (graphics processing unit) that can be used as general-purpose accelerators. The motivation is that algorithms of various parallel characteristics can be efficiently mapped to the heterogeneous architecture coupled with these three processors. The CPU and GPU offload partial computationally intensive tasks from the FPGA to reduce the resource consumption and lower the overall cost of the system. However, in present common scenarios, the applications always utilize only one type of accelerator because the development approach supporting the collaboration of the heterogeneous processors faces challenges. Therefore, a systematic approach takes advantage of write-once-run-anywhere portability, high execution performance of the modules mapped to various architectures and facilitates the exploration of design space. In this paper, A servant-execution-flow model is proposed for the abstraction of the cooperation of the heterogeneous processors, which supports task partition, communication and synchronization. At its first run, the intermediate language represented by the data flow diagram can generate the executable code of the target processor or can be converted into high-level programming languages. The instantiation parameters efficiently control the relationship between the modules and computational units, including two hierarchical processing units mapping and adjustment of data-level parallelism. An embedded system of a three-dimensional waveform oscilloscope is selected as a case study. The performance of algorithms such as contrast stretching, etc., are analyzed with implementations on various combinations of these processors. The experimental results show that the heterogeneous computing system with less than 35% resources achieves similar performance to the pure FPGA and approximate energy efficiency.

Keywords: FPGA-CPU-GPU collaboration, design space exploration, heterogeneous computing, intermediate language, parameterized instantiation

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Authors: Chungkuk Jin, Moohyun Kim, Woo Chul Chung

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This paper presents global performance and dynamic behaviors of a discrete-pontoon-type floating bridge with mooring lines in time domain under wind and wave excitations. The structure is designed for long-distance and deep-water crossing and consists of the girder, columns, pontoons, and mooring lines. Their functionality and behaviors are investigated by using elastic-floater/mooring fully-coupled dynamic simulation computer program. Dynamic wind, first- and second-order wave forces, and current loads are considered as environmental loads. Girder’s dynamic responses and mooring tensions are analyzed under different analysis methods and environmental conditions. Girder’s lateral responses are highly influenced by the second-order wave and wind loads while the first-order wave load mainly influences its vertical responses.

Keywords: floating bridge, mooring line, pontoon, wave excitation

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Authors: Ana C. R. P. Ferreira, Adriano H. P. Pereira

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Given the frequent variation of mineral properties throughout the Araxá pyrochlore deposit, even if a good homogenization work has been carried out before feeding the processing plants, an operation with quality and performance’s high variety standard is expected. These results could be improved and standardized if the blend composition parameters that most influence the processing route are determined, and then the types of raw materials are grouped by them, finally presenting a great reference with operational settings for each group. Associating the physical and chemical parameters of a unit operation through benchmarking or even an optimal reference of metallurgical recovery and product quality reflects in the reduction of the production costs, optimization of the mineral resource, and guarantee of greater stability in the subsequent processes of the production chain that uses the mineral of interest. Conducting a comprehensive exploratory data analysis to identify which characteristics of the ore are most relevant to the process route, associated with the use of Machine Learning algorithms for grouping the raw material (ore) and associating these with reference variables in the process’ benchmark is a reasonable alternative for the standardization and improvement of mineral processing units. Clustering methods through Decision Tree and K-Means were employed, associated with algorithms based on the theory of benchmarking, with criteria defined by the process team in order to reference the best adjustments for processing the ore piles of each cluster. A clean user interface was created to obtain the outputs of the created algorithm. The results were measured through the average time of adjustment and stabilization of the process after a new pile of homogenized ore enters the plant, as well as the average time needed to achieve the best processing result. Direct gains from the metallurgical recovery of the process were also measured. The results were promising, with a reduction in the adjustment time and stabilization when starting the processing of a new ore pile, as well as reaching the benchmark. Also noteworthy are the gains in metallurgical recovery, which reflect a significant saving in ore consumption and a consequent reduction in production costs, hence a more rational use of the tailings dams and life optimization of the mineral deposit.

Keywords: mineral clustering, machine learning, process optimization, pyrochlore processing

Procedia PDF Downloads 138

Authors: J. Niyitegeka, G. Nshimirimana, A. Silverstein, J. Odhiambo, Y. Lin, T. Nkurunziza, R. Riviello, S. Rulisa, P. Banguti, H. Magge, M. Macharia, J. P. Dushime, R. Habimana, B. Hedt-Gauthier

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In low-resource settings, women needing an emergency cesarean section experiences various delays in both reaching and receiving care that is often linked to poor neonatal outcomes. In this study, we quantified different measures of delays and assessed the association between these delays and neonatal outcomes at three rural district hospitals in Rwanda. This retrospective study included 441 neonates and their mothers who underwent emergency cesarean sections in 2015 at Butaro, Kirehe and Rwinkwavu District Hospitals. Four possible delays were measured: Time from start of labor to district hospital admission, travel time from a health center to the district hospital, time from admission to surgical incision, and time from the decision for the emergency cesarean section to surgical incision. Neonatal outcomes were categorized as unfavorable (APGAR < 7 or death) and favorable (APGAR ≥ 7). We assessed the relationship between each type of delay and neonatal outcomes using multivariate logistic regression. In our study, 38.7% (108 out of 279) of neonates’ mothers labored for 12 to 24 hours before hospital admission and 44.7% (159 of 356) of mothers were transferred from health centers that required 30 to 60 minutes of travel time to reach the district hospital. 48.1% (178 of 370) of caesarean sections started within five hours after admission and 85.2% (288 of 338) started more than thirty minutes after the decision for the emergency cesarean section was made. Neonatal outcomes were significantly worse among mothers with more than 90 minutes of travel time from the health center to the district hospital compared to health centers attached to the hospital (OR = 5.12, p = 0.02). Neonatal outcomes were also significantly different depending on decision to incision intervals; neonates with cesarean deliveries starting more than thirty minutes after decision had better outcomes than those started immediately (OR = 0.32, p = 0.04). Interventions that decrease barriers to access to maternal health care services can improve neonatal outcome after emergency cesarean section. Triaging could explain the inverse relationship between time from decision to incision and neonatal outcome; this must be studied more in the future.

Keywords: Africa, emergency obstetric care, rural health delivery, maternal and child health

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Authors: Elena Kokkoni, Prasanna Kannappan, Ashkan Zehfroosh, Effrosyni Mavroudi, Kristina Strother-Garcia, James C. Galloway, Jeffrey Heinz, Rene Vidal, Herbert G. Tanner

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Early immobility affects the motor, cognitive, and social development. Current pediatric rehabilitation lacks the technology that will provide the dosage needed to promote mobility for young children at risk. The addition of socially assistive robots in early interventions may help increase the mobility dosage. The aim of this study is to examine the feasibility of an early intervention paradigm where non-walking infants experience independent mobility while socially interacting with robots. A dynamic environment is developed where both the child and the robot interact and learn from each other. The environment involves: 1) a range of physical activities that are goal-oriented, age-appropriate, and ability-matched for the child to perform, 2) the automatic functions that perceive the child’s actions through novel activity recognition algorithms, and decide appropriate actions for the robot, and 3) a networked visual data acquisition system that enables real-time assessment and provides the means to connect child behavior with robot decision-making in real-time. The environment was tested by bringing a two-year old boy with Down syndrome for eight sessions. The child presented delays throughout his motor development with the current being on the acquisition of walking. During the sessions, the child performed physical activities that required complex motor actions (e.g. climbing an inclined platform and/or staircase). During these activities, a (wheeled or humanoid) robot was either performing the action or was at its end point 'signaling' for interaction. From these sessions, information was gathered to develop algorithms to automate the perception of activities which the robot bases its actions on. A Markov Decision Process (MDP) is used to model the intentions of the child. A 'smoothing' technique is used to help identify the model’s parameters which are a critical step when dealing with small data sets such in this paradigm. The child engaged in all activities and socially interacted with the robot across sessions. With time, the child’s mobility was increased, and the frequency and duration of complex and independent motor actions were also increased (e.g. taking independent steps). Simulation results on the combination of the MDP and smoothing support the use of this model in human-robot interaction. Smoothing facilitates learning MDP parameters from small data sets. This paradigm is feasible and provides an insight on how social interaction may elicit mobility actions suggesting a new early intervention paradigm for very young children with motor disabilities. Acknowledgment: This work has been supported by NIH under grant #5R01HD87133.

Keywords: activity recognition, human-robot interaction, machine learning, pediatric rehabilitation

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Authors: D. S. Vardar, A. S. Kipcak, F. T. Senberber, E. M. Derun, S. Piskin, N. Tugrul

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Zinc borate is an important inorganic hydrate borate material, which can be use as a flame retardant agent and corrosion resistance material. This compound can loss its structural water content at higher than 290°C. Due to thermal stability; Zinc Borate can be used as flame reterdant at high temperature process of plastic and gum. In this study, the ultrasonic reaction of zinc borates were studied using hydrozincite (Zn5(CO3)2•(OH)6) and boric acid (H3BO3) raw materials. Before the synthesis raw materials were characterized by X-Ray Diffraction (XRD) and Fourier Transform Infrared Spectroscopy (FT-IR). Ultrasonic method is a new application on the zinc borate synthesis. The synthesis parameters were set to 90°C reaction temperature and 55 minutes of reaction time, with 1:1, 1:2, 1:3, 1:4 and 1:5 molar ratio of starting materials (Zn5(CO3)2•(OH)6 : H3BO3). After the zinc borate synthesis, the products analyzed by XRD and FT-IR. As a result, optimum molar ratio of 1:5 (Zn5(CO3)2•(OH)6:H3BO3) is determined for the synthesis of zinc borates with ultrasonic method.

Keywords: borate, ultrasonic method, zinc borate, zinc borate synthesis

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Authors: Marielcio Lacerda, Angelo Paulino, Elcio Shiguemori, Alvaro Damiao, Lamartine Guimaraes, Camila Anjos

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Accurate information about the terrain is extremely important in disaster management activities or conflict. This paper proposes the use of the Unmanned Aircraft Systems (UAS) at the identification of Airdrop Zones (AZs) and Helicopter Landing Zones (HLZs). In this paper we consider the AZs the zones where troops or supplies are dropped by parachute, and HLZs areas where victims can be rescued. The use of digital image processing enables the automatic generation of an orthorectified mosaic and an actual Digital Surface Model (DSM). This methodology allows obtaining this fundamental information to the terrain’s comprehension post-disaster in a short amount of time and with good accuracy. In order to get the identification and classification of AZs and HLZs images from DJI drone, model Phantom 4 have been used. The images were obtained with the knowledge and authorization of the responsible sectors and were duly registered in the control agencies. The flight was performed on May 24, 2017, and approximately 1,300 images were obtained during approximately 1 hour of flight. Afterward, new attributes were generated by Feature Extraction (FE) from the original images. The use of multispectral images and complementary attributes generated independently from them increases the accuracy of classification. The attributes of this work include the Declivity Map and Principal Component Analysis (PCA). For the classification four distinct classes were considered: HLZ 1 – small size (18m x 18m); HLZ 2 – medium size (23m x 23m); HLZ 3 – large size (28m x 28m); AZ (100m x 100m). The Decision Tree method Random Forest (RF) was used in this work. RF is a classification method that uses a large collection of de-correlated decision trees. Different random sets of samples are used as sampled objects. The results of classification from each tree and for each object is called a class vote. The resulting classification is decided by a majority of class votes. In this case, we used 200 trees for the execution of RF in the software WEKA 3.8. The classification result was visualized on QGIS Desktop 2.12.3. Through the methodology used, it was possible to classify in the study area: 6 areas as HLZ 1, 6 areas as HLZ 2, 4 areas as HLZ 3; and 2 areas as AZ. It should be noted that an area classified as AZ covers the classifications of the other classes, and may be used as AZ, HLZ of large size (HLZ3), medium size (HLZ2) and small size helicopters (HLZ1). Likewise, an area classified as HLZ for large rotary wing aircraft (HLZ3) covers the smaller area classifications, and so on. It was concluded that images obtained through small UAV are of great use in calamity situations since they can provide data with high accuracy, with low cost, low risk and ease and agility in obtaining aerial photographs. This allows the generation, in a short time, of information about the features of the terrain in order to serve as an important decision support tool.

Keywords: disaster management, unmanned aircraft systems, helicopter landing zones, airdrop zones, random forest

Procedia PDF Downloads 167

Authors: Sara R. Knigge, Sugat R. Tuladhar, Alexander Becker, Tobias Schilling, Birgit Glasmacher

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Commercially available mechanical or biological heart valve prostheses both tend to fail long-term due to thrombosis, calcific degeneration, infection, or immunogenic rejection. Moreover, these prostheses are non-viable and do not grow with the patients, which is a problem for young patients. As a result, patients often need to undergo redo-operations. Tissue-engineered (TE) heart valves based on degradable electrospun fiber scaffolds represent a promising approach to overcome these limitations. Such scaffolds need sufficient mechanical properties to withstand the hydrodynamic stress of intracardiac hemodynamics. Additionally, the scaffolds should be colonized by autologous or homologous cells to facilitate the in vivo remodeling of the scaffolds to a viable structure. This study investigates how process parameters of electrospinning and degradation affect the mechanical properties of electrospun scaffolds made of FDA-approved, biodegradable polymer polycaprolactone (PCL). Fiber mats were produced from a PCL/tetrafluoroethylene solution by electrospinning. The e-spinning process was varied in terms of scaffold thickness, fiber diameter, fiber orientation, and fiber interconnectivity. The morphology of the fiber mats was characterized with a scanning electron microscope (SEM). The mats were degraded in different solutions (cell culture media, SBF, PBS and 10 M NaOH-Solution). At different time points of degradation (2, 4 and 6 weeks), tensile and cyclic loading tests were performed. Fresh porcine pericardium and heart valves served as a control for the mechanical assessment. The progression of polymer degradation was quantified by SEM and differential scanning calorimetry (DSC). Primary Human aortic endothelial cells (HAECs) and Human induced pluripotent stem cell-derived endothelial cells (iPSC-ECs) were seeded on the fiber mats to investigate the cell colonization potential. The results showed that both the electrospinning parameters and the degradation significantly influenced the mechanical properties. Especially the fiber orientation has a considerable impact and leads to a pronounced anisotropic behavior of the scaffold. Preliminary results showed that the polymer became strongly more brittle over time. However, the embrittlement can initially only be detected in the mechanical test. In the SEM and DSC investigations, neither morphological nor thermodynamic changes are significantly detectable. Live/Dead staining and SEM imaging of the cell-seeded scaffolds showed that HAECs and iPSC-ECs were able to grow on the surface of the polymer. In summary, this study's results indicate a promising approach to the development of a TE aortic heart valve based on an electrospun scaffold.

Keywords: electrospun scaffolds, long-term polymer degradation, mechanical behavior of electrospun PCL, tissue engineered aortic heart valve

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Authors: Yasamin Rahimi, Ali Kamandi, Abbas Hoseini, Hesam Haddad

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Online/offline chat is a convenient approach in the electronic markets of second-hand products in which potential customers would like to have more information about the products to fill the information gap between buyers and sellers. Online peer in peer market is trying to create artificial intelligence-based systems that help customers ask more informative questions in an easier way. In this article, we introduce a method for the question/answer system that we have developed for the top-ranked electronic market in Iran called Divar. When it comes to secondhand products, incomplete product information in a purchase will result in loss to the buyer. One way to balance buyer and seller information of a product is to help the buyer ask more informative questions when purchasing. Also, the short time to start and achieve the desired result of the conversation was one of our main goals, which was achieved according to A/B tests results. In this paper, we propose and evaluate a method for suggesting questions and answers in the messaging platform of the e-commerce website Divar. Creating such systems is to help users gather knowledge about the product easier and faster, All from the Divar database. We collected a dataset of around 2 million messages in Persian colloquial language, and for each category of product, we gathered 500K messages, of which only 2K were Tagged, and semi-supervised methods were used. In order to publish the proposed model to production, it is required to be fast enough to process 10 million messages daily on CPU processors. In order to reach that speed, in many subtasks, faster and simplistic models are preferred over deep neural models. The proposed method, which requires only a small amount of labeled data, is currently used in Divar production on CPU processors, and 15% of buyers and seller’s messages in conversations is directly chosen from our model output, and more than 27% of buyers have used this model suggestions in at least one daily conversation.

Keywords: smart reply, spell checker, information retrieval, intent detection, question answering

Procedia PDF Downloads 179

Authors: Emma O’ Keeffe, Helen Hughes, Peter McLoughlin, Shiau Pin Tan, Nick McCarthy

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Biosecurity is emerging as one of the most important issues facing the agricultural and forestry community. This is as a result of increased invasion from new pests and diseases with the main protocol for dealing with these species being the use of synthetic pesticides. However, these chemicals have been shown to exhibit negative effects on the environment. Seaweeds represent a vast untapped resource of bio-molecules with a broad range of biological activities including pesticidal. This project investigated both the antifungal and antibacterial activity of seaweed species against two problematic root rot fungi, Armillaria mellea and Heterobasidion annosum and ten quarantine bacterial plant pathogens including Xanthomonas arboricola, Xanthomonas fragariae, and Erwinia amylovora. Four seaweed species were harvested from the South-East coast of Ireland including brown, red and green varieties. The powdered seaweeds were extracted using four different solvents by liquid extraction. The poisoned food technique was employed to establish the antifungal efficacy, and the standard disc diffusion assay was used to assess the antibacterial properties of the seaweed extracts. It was found that extracts of the green seaweed exhibited antifungal activity against H. annosum, with approximately 50% inhibition compared to the negative control. The protectant activities of the active extracts were evaluated on disks of Picea sitchensis, a plant species sensitive to infection from H. annosum and compared to the standard chemical control product urea. The crude extracts exhibited very similar activity to the 10% and 20% w/v concentrations of urea, demonstrating the ability of seaweed extracts to compete with commercially available products. Antibacterial activity was exhibited by a number of seaweed extracts with the red seaweed illustrating the strongest activity, with a zone of inhibition of 15.83 ± 0.41 mm exhibited against X. arboricola whilst the positive control (10 μg/disk of chloramphenicol) had a zone of 26.5 ± 0.71 mm. These results highlight the potential application of seaweed extracts in the forestry and agricultural industries for use as biopesticides. Further work is now required to identify the bioactive molecules that are responsible for this antifungal and antibacterial activity in the seaweed extracts, including toxicity studies to ensure the extracts are non-toxic to plants and humans.

Keywords: antibacterial, antifungal, biopesticides, seaweeds

Procedia PDF Downloads 162

Authors: Antoine Dumont, Weiji Hong, Zheng-Hong Lu

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Light emitting diodes (LEDs) are steadily becoming the new standard for luminescent display devices because of their energy efficiency and relatively low cost, and the purity of the light they emit. Our research focuses on the optical properties of the lead halide perovskite CsPbBr₃ and its family that is showing steadily improving performances in LEDs and solar cells. The objective of this work is to investigate CsPbBr₃ as an emitting layer made by physical vapor deposition instead of the usual solution-processed perovskites, for use in LEDs. The deposition in vacuum eliminates any risk of contaminants as well as the necessity for the use of chemical ligands in the synthesis of quantum dots. Initial results show the versatility of the dual-source evaporation method, which allowed us to create different phases in bulk form by altering the mole ratio or deposition rate of CsBr and PbBr₂. The distinct phases Cs₄PbBr₆, CsPbBr₃ and CsPb₂Br₅ – confirmed through XPS (x-ray photoelectron spectroscopy) and X-ray diffraction analysis – have different optical properties and morphologies that can be used for specific applications in optoelectronics. We are particularly focused on the blue shift expected from quantum dots (QDs) and the stability of the perovskite in this form. We already obtained proof of the formation of QDs through our dual source evaporation method with electron microscope imaging and photoluminescence testing, which we understand is a first in the community. We also incorporated the QDs in an LED structure to test the electroluminescence and the effect on performance and have already observed a significant wavelength shift. The goal is to reach 480nm after shifting from the original 528nm bulk emission. The hole transport layer (HTL) material onto which the CsPbBr₃ is evaporated is a critical part of this study as the surface energy interaction dictates the behaviour of the QD growth. A thorough study to determine the optimal HTL is in progress. A strong blue shift for a typically green emitting material like CsPbBr₃ would eliminate the necessity of using blue emitting Cl-based perovskite compounds and could prove to be more stable in a QD structure. The final aim is to make a perovskite QD LED with strong blue luminescence, fabricated through a dual-source evaporation technique that could be scalable to industry level, making this device a viable and cost-effective alternative to current commercial LEDs.

Keywords: material physics, perovskite, light emitting diode, quantum dots, high vacuum deposition, thin film processing

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Authors: Ujene Ikem Godspower

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Acts of terror perpetrated either by state or non-state actors are considered a social ill and impugn on the collective well-being of the society. As such, there is the need for social reparations, which is meant to ensure the healing of the social wounds resulting from the atrocities committed by errant individuals under different guises. In order to ensure social closure and effectively repair the damages done by anomic behaviors, society must ensure that justice is served and those whose rights and privileges have been denied and battered are given the necessary succour they deserve. With regards to the ongoing terrorism in the Northeast, the moves to rehabilitate and reintegrate Boko Haram members have commenced with the establishment of Operation Safe Corridor,1 and a proposed bill for the establishment of “National Agency for the Education, Rehabilitation, De-radicalisation and Integration of Repentant Insurgents in Nigeria”2. All of which Nigerians have expressed mixed feelings about. Some argue that the endeavor is lacking in ethical decency and justice and totally insults human reasoning. Terrorism and counterterrorism in Nigeria have been enmeshed in gross human rights violations both by the military and the terrorists, and this raises the concern of Nigeria’s ability to fairly and justiciably implement the deradicalization and reintegration efforts. On the other hand, there is the challenge of the community dwellers that are victims of terrorism and counterterrorism and their ability to forgive and welcome back their immediate-past tormentors even with the slightest sense of injustice in the process of terrorists reintegration and rehabilitation. With such efforts implemented in other climes, the Nigeria’s case poses a unique challenge and commands keen interests by stakeholders and the international community due to the aforementioned reasons. It is therefore pertinent to assess the communities’ level of involvement in the cycle of reintegration- hence, the objective of this paper. Methodologically as a part of my larger PhD thesis, this study intends to explore the three different local governments (Michika in Adamawa, Chibok in Borno, and Yunusari in Yobe), all based on the intensity of terrorists attacks. Twenty five in-depth interview will be conducted in the study locations above featuring religious leaders, Community (traditional) leaders, Internally displaced persons, CSOs management officials, and ex-Boko Haram insurgents who have been reintegrated. The data that will be generated from field work will be analyzed using the Nvivo-12 software package, which will help to code and create themes based on the study objectives. Furthermore, the data will be content-analyzed, employing verbatim quotations where necessary. Ethically, the study will take into consideration the basic ethical principles for research of this nature. It will strictly adhere to the principle of voluntary participation, anonymity, and confidentiality.

Keywords: boko haram, reintegration, rehabilitation, terrorism, victimology

Procedia PDF Downloads 236

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

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Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 73

Authors: Jana Ismail, Layla Fallatah, Maha Alshmaisi

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ChatGPT, a cutting-edge language model built on the GPT-3.5 architecture, has garnered attention for its profound natural language processing capabilities, holding promise for transformative applications in customer service and content creation. This study delves into ChatGPT's architecture, aiming to comprehensively understand its strengths and potential limitations. Through systematic experiments across diverse domains, such as general knowledge and creative writing, we evaluated the model's coherence, context retention, and task-specific accuracy. While ChatGPT excels in generating human-like responses and demonstrates adaptability, occasional inaccuracies and sensitivity to input phrasing were observed. The study emphasizes the impact of prompt design on output quality, providing valuable insights for the nuanced deployment of ChatGPT in conversational AI and contributing to the ongoing discourse on the evolving landscape of natural language processing in artificial intelligence.

Keywords: artificial Inelegance, chatGPT, open AI, NLP

Procedia PDF Downloads 68

Authors: Monika Saha, A. T. M. Hasan Zobeyer, Nasreen Jahan

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El Nino-Southern Oscillation (ENSO) is the interaction between atmosphere and ocean in tropical Pacific which causes inconsistent warm/cold weather in tropical central and eastern Pacific Ocean. Due to the impact of climate change, ENSO events are becoming stronger in recent times, and therefore it is very important to study the influence of ENSO in climate studies. Bangladesh, being in the low-lying deltaic floodplain, experiences the worst consequences due to flooding every year. To reduce the catastrophe of severe flooding events, non-structural measures such as flood forecasting can be helpful in taking adequate precautions and steps. Forecasting seasonal flood with a longer lead time of several months is a key component of flood damage control and water management. The objective of this research is to identify the possible strength of teleconnection between ENSO and river flow of Surma and examine the potential possibility of long lead flood forecasting in the wet season. Surma is one of the major rivers of Bangladesh and is a part of the Surma-Meghna river system. In this research, sea surface temperature (SST) has been considered as the ENSO index and the lead time is at least a few months which is greater than the basin response time. The teleconnection has been assessed by the correlation analysis between July-August-September (JAS) flow of Surma and SST of Nino 4 region of the corresponding months. Cumulative frequency distribution of standardized JAS flow of Surma has also been determined as part of assessing the possible teleconnection. Discharge data of Surma river from 1975 to 2015 is used in this analysis, and remarkable increased value of correlation coefficient between flow and ENSO has been observed from 1985. From the cumulative frequency distribution of the standardized JAS flow, it has been marked that in any year the JAS flow has approximately 50% probability of exceeding the long-term average JAS flow. During El Nino year (warm episode of ENSO) this probability of exceedance drops to 23% and while in La Nina year (cold episode of ENSO) it increases to 78%. Discriminant analysis which is known as 'Categoric Prediction' has been performed to identify the possibilities of long lead flood forecasting. It has helped to categorize the flow data (high, average and low) based on the classification of predicted SST (warm, normal and cold). From the discriminant analysis, it has been found that for Surma river, the probability of a high flood in the cold period is 75% and the probability of a low flood in the warm period is 33%. A synoptic parameter, forecasting index (FI) has also been calculated here to judge the forecast skill and to compare different forecasts. This study will help the concerned authorities and the stakeholders to take long-term water resources decisions and formulate policies on river basin management which will reduce possible damage of life, agriculture, and property.

Keywords: El Nino-Southern Oscillation, sea surface temperature, surma river, teleconnection, cumulative frequency distribution, discriminant analysis, forecasting index

Procedia PDF Downloads 138

Authors: Sudhanshu Sharma

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Lisinopril, 1-[N-{(s)-I-carboxy-3 phenyl propyl}-L-proline dehydrate is a lysine analog of enalaprilat, the active metabolite of enalapril. It is long-acting, non-sulhydryl angiotensin-converting enzyme (ACE) inhibitor that is used for the treatment of hypertension and congestive heart failure in daily dosage 10-80 mg. Pharmacological activity of lisinopril has been proved in various experimental and clinical studies. Owing to its importance and widespread use, efforts have been made towards the development of simple and reliable analytical methods. As per our literature survey, lisinopril in pharmaceutical formulations has been determined by various analytical methodologies like polaragraphy, potentiometry, and spectrophotometry, but most of these analytical methods are not too suitable for the Identification of lisinopril from clinical samples because of the interferences caused by the amino acids and amino groups containing metabolites present in biological samples. This report is an attempt in the direction of developing a simple and reliable method for on plate identification and quantification of lisinopril in pharmaceutical formulations as well as from human urine samples using silica gel H layers developed with a new mobile phase comprising of micellar solutions of N-cetyl-N, N, N-trimethylammonium bromide (CTAB). Micellar solutions have found numerous practical applications in many areas of separation science. Micellar liquid chromatography (MLC) has gained immense popularity and wider applicability due to operational simplicity, cost effectiveness, relatively non-toxicity and enhanced separation efficiency, low aggressiveness. Incorporation of aqueous micellar solutions as mobile phase was pioneered by Armstrong and Terrill as they accentuated the importance of TLC where simultaneous separation of ionic or non-ionic species in a variety of matrices is required. A peculiarity of the micellar mobile phases (MMPs) is that they have no macroscopic analogues, as a result the typical separations can be easily achieved by using MMPs than aqueous organic mobile phases. Previously MMPs were successfully employed in TLC based critical separations of aromatic hydrocarbons, nucleotides, vitamin K1 and K5, o-, m- and p- aminophenol, amino acids, separation of penicillins. The human urine analysis for identification of selected drugs and their metabolites has emerged as an important investigation tool in forensic drug analysis. Among all chromatographic methods available only thin layer chromatography (TLC) enables a simple fast and effective separation of the complex mixtures present in various biological samples and is recommended as an approved testing for forensic drug analysis by federal Law. TLC proved its applicability during successful separation of bio-active amines, carbohydrates, enzymes, porphyrins, and their precursors, alkaloid and drugs from urine samples.

Keywords: lisnopril, surfactant, chromatography, micellar solutions

Procedia PDF Downloads 353

Authors: E. A. Mlybari, M. S. Elbisy, A. H. Alshahri, O. M. Albarakati

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Sea level rise threatens to increase the impact of future storms and hurricanes on coastal communities. Accurate sea level change prediction and supplement is an important task in determining constructions and human activities in coastal and oceanic areas. In this study, support vector machines (SVM) is proposed to predict daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal parameter values of kernel function are determined using a genetic algorithm. The SVM results are compared with the field data and with back propagation (BP). Among the models, the SVM is superior to BPNN and has better generalization performance.

Keywords: tides, prediction, support vector machines, genetic algorithm, back-propagation neural network, risk, hazards

Procedia PDF Downloads 457

Authors: Nour El Houda Arabi, Aicha Iratni, Talaighil Razika, Bruno Capoen, Mohamed Bouazaoui

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In this paper, we report the effect of the calcination temperature and SiO2 addition on structural, optical and hydrophilicity of TiO2 films deposited by deep-coating sol-gel process. XRD investigation of the structural TiO2 films with increasing the temperature calcination, reveals that rutile phase will appear for the high temperature (>1000°C). However, the addition of SiO2 relate the densification of TiO2 films. Ellipsometric and UV-visible measure show that the refractive index grow with increasing temperature, against the film thickness decreases. On the other hand, the addition of SiO2 decreases the refractive index and increases the TiO2 film thickness. Finally, the hydrophilicity is assisted by contact angle measurement. It is found that addition of 50% of SiO2 to TiO2 is most effective for reducing the contact angle of water.

Keywords: physical properties, sol, gel, TiO2/SiO2 composite films

Procedia PDF Downloads 485