Search results for: artificial lung
1991 Analysis of Aspergillus fumigatus IgG Serologic Cut-Off Values to Increase Diagnostic Specificity of Allergic Bronchopulmonary Aspergillosis
Authors: Sushmita Roy Chowdhury, Steve Holding, Sujoy Khan
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The immunogenic responses of the lung towards the fungus Aspergillus fumigatus may range from invasive aspergillosis in the immunocompromised, fungal ball or infection within a cavity in the lung in those with structural lung lesions, or allergic bronchopulmonary aspergillosis (ABPA). Patients with asthma or cystic fibrosis are particularly predisposed to ABPA. There are consensus guidelines that have established criteria for diagnosis of ABPA, but uncertainty remains on the serologic cut-off values that would increase the diagnostic specificity of ABPA. We retrospectively analyzed 80 patients with severe asthma and evidence of peripheral blood eosinophilia ( > 500) over the last 3 years who underwent all serologic tests to exclude ABPA. Total IgE, specific IgE and specific IgG levels against Aspergillus fumigatus were measured using ImmunoCAP Phadia-100 (Thermo Fisher Scientific, Sweden). The Modified ISHAM working group 2013 criteria (obligate criteria: asthma or cystic fibrosis, total IgE > 1000 IU/ml or > 417 kU/L and positive specific IgE Aspergillus fumigatus or skin test positivity; with ≥ 2 of peripheral eosinophilia, positive specific IgG Aspergillus fumigatus and consistent radiographic opacities) was used in the clinical workup for the final diagnosis of ABPA. Patients were divided into 3 groups - definite, possible, and no evidence of ABPA. Specific IgG Aspergillus fumigatus levels were not used to assign the patients into any of the groups. Of 80 patients (males 48, females 32; mean age 53.9 years ± SD 15.8) selected for the analysis, there were 30 patients who had positive specific IgE against Aspergillus fumigatus (37.5%). 13 patients fulfilled the Modified ISHAM working group 2013 criteria of ABPA (‘definite’), while 15 patients were ‘possible’ ABPA and 52 did not fulfill the criteria (not ABPA). As IgE levels were not normally distributed, median levels were used in the analysis. Median total IgE levels of patients with definite and possible ABPA were 2144 kU/L and 2597 kU/L respectively (non-significant), while median specific IgE Aspergillus fumigatus at 4.35 kUA/L and 1.47 kUA/L respectively were significantly different (comparison of standard deviations F-statistic 3.2267, significance level p=0.040). Mean levels of IgG anti-Aspergillus fumigatus in the three groups (definite, possible and no evidence of ABPA) were compared using ANOVA (Statgraphics Centurion Professional XV, Statpoint Inc). Mean levels of IgG anti-Aspergillus fumigatus (Gm3) in definite ABPA was 125.17 mgA/L ( ± SD 54.84, with 95%CI 92.03-158.32), while mean Gm3 levels in possible and no ABPA were 18.61 mgA/L and 30.05 mgA/L respectively. ANOVA showed a significant difference between the definite group and the other groups (p < 0.001). This was confirmed using multiple range tests (Fisher's least significant difference procedure). There was no significant difference between the possible ABPA and not ABPA groups (p > 0.05). The study showed that a sizeable proportion of patients with asthma are sensitized to Aspergillus fumigatus in this part of India. A higher cut-off value of Gm3 ≥ 80 mgA/L provides a higher serologic specificity towards definite ABPA. Long-term studies would provide us more information if those patients with 'possible' APBA and positive Gm3 later develop clear ABPA, and are different from the Gm3 negative group in this respect. Serologic testing with clear defined cut-offs are a valuable adjunct in the diagnosis of ABPA.Keywords: allergic bronchopulmonary aspergillosis, Aspergillus fumigatus, asthma, IgE level
Procedia PDF Downloads 2001990 Mechanical Properties and Microstructure of Ultra-High Performance Concrete Containing Fly Ash and Silica Fume
Authors: Jisong Zhang, Yinghua Zhao
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The present study investigated the mechanical properties and microstructure of Ultra-High Performance Concrete (UHPC) containing supplementary cementitious materials (SCMs), such as fly ash (FA) and silica fume (SF), and to verify the synergistic effect in the ternary system. On the basis of 30% fly ash replacement, the incorporation of either 10% SF or 20% SF show a better performance compared to the reference sample. The efficiency factor (k-value) was calculated as a synergistic effect to predict the compressive strength of UHPC with these SCMs. The SEM of micrographs and pore volume from BJH method indicate a high correlation with compressive strength. Further, an artificial neural networks model was constructed for prediction of the compressive strength of UHPC containing these SCMs.Keywords: artificial neural network, fly ash, mechanical properties, ultra-high performance concrete
Procedia PDF Downloads 4071989 Knowledge of Artificial Insemination and Agribusiness Management for Social Innovation in Rural Populations
Authors: Yasser Y. Lenis, Daniela Garcia Gonzalez, Cristian Solarte Bacca, Diego F. Carrillo González, Amy Jo Montgomery, Dursun Barrios
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Introduction: Artificial insemination in bovines helps to promote genetic improvement and can positively impact the rural economy. The Colombian armed conflict has forced a large portion of the rural population to abandon their territory, affecting their education, family integration, and economics. Justification: The achievement of education in rural populations was one of the Millennium Development Goals (MDGs) made by the United Nations. During the last World Summit on Sustainable Development (WSSD), it was concluded that most of the world’s poor, illiterate and undernourished population lives in rural areas; therefore, access to education is considered one of the most significant challenges for governments in countries with developing economies. Objectives: To study the effects of training in artificial insemination and rural management on the perception of knowledge and the level of knowledge in rural residents affected by the armed conflict in Nariño, Colombia. Methods: The perception of knowledge and the theoretical-practical knowledge of 63 rural residents were evaluated on the topics of bovine agribusiness management, artificial insemination, and genetic improvement through the application of three surveys. 1) evaluated the perceived level of knowledge each rural resident had about each topic using the Likert scale, 2) evaluated the theoretical knowledge before training, and 3) evaluated the theoretical knowledge upon completion of training. Results/discussion: Of the surveyed rural residents, 54% stated that they knew how business management improved the performance of their bovine agribusiness, 54% answered the pre-training knowledge test correctly, while 83% correctly answered the post-training knowledge test. Only 6% of surveyed residents perceived that they had prior knowledge of artificial insemination and reproductive anatomy topics. Before training, 35% of surveyed residents answered correctly on these topics, while upon completion of training, 65% answered correctly. Regarding genetic improvement, 11% of participating rural residents stated that they knew this subject. The correct answers on this topic went from 57% to 89% before and post-training. Conclusion: Rural extension programs contribute to closing knowledge gaps in relation to the use of reproductive biotechnologies and bovine management in rural areas affected by armed conflict.Keywords: agribusiness, insemination, knowledge, reproduction
Procedia PDF Downloads 1671988 Statistical Modeling and by Artificial Neural Networks of Suspended Sediment Mina River Watershed at Wadi El-Abtal Gauging Station (Northern Algeria)
Authors: Redhouane Ghernaout, Amira Fredj, Boualem Remini
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Suspended sediment transport is a serious problem worldwide, but it is much more worrying in certain regions of the world, as is the case in the Maghreb and more particularly in Algeria. It continues to take disturbing proportions in Northern Algeria due to the variability of rains in time and in space and constant deterioration of vegetation. Its prediction is essential in order to identify its intensity and define the necessary actions for its reduction. The purpose of this study is to analyze the concentration data of suspended sediment measured at Wadi El-Abtal Hydrometric Station. It also aims to find and highlight regressive power relationships, which can explain the suspended solid flow by the measured liquid flow. The study strives to find models of artificial neural networks linking the flow, month and precipitation parameters with solid flow. The obtained results show that the power function of the solid transport rating curve and the models of artificial neural networks are appropriate methods for analysing and estimating suspended sediment transport in Wadi Mina at Wadi El-Abtal Hydrometric Station. They made it possible to identify in a fairly conclusive manner the model of neural networks with four input parameters: the liquid flow Q, the month and the daily precipitation measured at the representative stations (Frenda 013002 and Ain El-Hadid 013004 ) of the watershed. The model thus obtained makes it possible to estimate the daily solid flows (interpolate and extrapolate) even beyond the period of observation of solid flows (1985/86 to 1999/00), given the availability of the average daily liquid flows and daily precipitation since 1953/1954.Keywords: suspended sediment, concentration, regression, liquid flow, solid flow, artificial neural network, modeling, mina, algeria
Procedia PDF Downloads 941987 Classification of Barley Varieties by Artificial Neural Networks
Authors: Alper Taner, Yesim Benal Oztekin, Huseyin Duran
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In this study, an Artificial Neural Network (ANN) was developed in order to classify barley varieties. For this purpose, physical properties of barley varieties were determined and ANN techniques were used. The physical properties of 8 barley varieties grown in Turkey, namely thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters of grain, were determined and it was found that these properties were statistically significant with respect to varieties. As ANN model, three models, N-l, N-2 and N-3 were constructed. The performances of these models were compared. It was determined that the best-fit model was N-1. In the N-1 model, the structure of the model was designed to be 11 input layers, 2 hidden layers and 1 output layer. Thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters of grain were used as input parameter; and varieties as output parameter. R2, Root Mean Square Error and Mean Error for the N-l model were found as 99.99%, 0.00074 and 0.009%, respectively. All results obtained by the N-l model were observed to have been quite consistent with real data. By this model, it would be possible to construct automation systems for classification and cleaning in flourmills.Keywords: physical properties, artificial neural networks, barley, classification
Procedia PDF Downloads 1751986 Impact of the Fourth Industrial Revolution on Food Security in South Africa
Authors: Fiyinfoluwa Giwa, Nicholas Ngepah
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This paper investigates the relationship between the Fourth Industrial Revolution and food security in South Africa. The Ordinary Least Square was adopted from 2012 Q1 to 2021 Q4. The study used artificial intelligence investment and the food production index as the measure for the fourth industrial revolution and food security, respectively. Findings reveal a significant and positive coefficient of 0.2887, signifying a robust statistical relationship between AI adoption and the food production index. As a policy recommendation, this paper recommends the introduction of incentives for farmers and agricultural enterprises to adopt AI technologies -and the expansion of digital connectivity and access to technology in rural areas.Keywords: Fourth Industrial Revolution, food security, artificial intelligence investment, food production index, ordinary least square
Procedia PDF Downloads 701985 Artificial Neural Network to Predict the Optimum Performance of Air Conditioners under Environmental Conditions in Saudi Arabia
Authors: Amr Sadek, Abdelrahaman Al-Qahtany, Turkey Salem Al-Qahtany
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In this study, a backpropagation artificial neural network (ANN) model has been used to predict the cooling and heating capacities of air conditioners (AC) under different conditions. Sufficiently large measurement results were obtained from the national energy-efficiency laboratories in Saudi Arabia and were used for the learning process of the ANN model. The parameters affecting the performance of the AC, including temperature, humidity level, specific heat enthalpy indoors and outdoors, and the air volume flow rate of indoor units, have been considered. These parameters were used as inputs for the ANN model, while the cooling and heating capacity values were set as the targets. A backpropagation ANN model with two hidden layers and one output layer could successfully correlate the input parameters with the targets. The characteristics of the ANN model including the input-processing, transfer, neurons-distance, topology, and training functions have been discussed. The performance of the ANN model was monitored over the training epochs and assessed using the mean squared error function. The model was then used to predict the performance of the AC under conditions that were not included in the measurement results. The optimum performance of the AC was also predicted under the different environmental conditions in Saudi Arabia. The uncertainty of the ANN model predictions has been evaluated taking into account the randomness of the data and lack of learning.Keywords: artificial neural network, uncertainty of model predictions, efficiency of air conditioners, cooling and heating capacities
Procedia PDF Downloads 671984 Comprehensive Study of Data Science
Authors: Asifa Amara, Prachi Singh, Kanishka, Debargho Pathak, Akshat Kumar, Jayakumar Eravelly
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Today's generation is totally dependent on technology that uses data as its fuel. The present study is all about innovations and developments in data science and gives an idea about how efficiently to use the data provided. This study will help to understand the core concepts of data science. The concept of artificial intelligence was introduced by Alan Turing in which the main principle was to create an artificial system that can run independently of human-given programs and can function with the help of analyzing data to understand the requirements of the users. Data science comprises business understanding, analyzing data, ethical concerns, understanding programming languages, various fields and sources of data, skills, etc. The usage of data science has evolved over the years. In this review article, we have covered a part of data science, i.e., machine learning. Machine learning uses data science for its work. Machines learn through their experience, which helps them to do any work more efficiently. This article includes a comparative study image between human understanding and machine understanding, advantages, applications, and real-time examples of machine learning. Data science is an important game changer in the life of human beings. Since the advent of data science, we have found its benefits and how it leads to a better understanding of people, and how it cherishes individual needs. It has improved business strategies, services provided by them, forecasting, the ability to attend sustainable developments, etc. This study also focuses on a better understanding of data science which will help us to create a better world.Keywords: data science, machine learning, data analytics, artificial intelligence
Procedia PDF Downloads 751983 Aerobic Bioprocess Control Using Artificial Intelligence Techniques
Authors: M. Caramihai, Irina Severin
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This paper deals with the design of an intelligent control structure for a bioprocess of Hansenula polymorpha yeast cultivation. The objective of the process control is to produce biomass in a desired physiological state. The work demonstrates that the designed Hybrid Control Techniques (HCT) are able to recognize specific evolution bioprocess trajectories using neural networks trained specifically for this purpose, in order to estimate the model parameters and to adjust the overall bioprocess evolution through an expert system and a fuzzy structure. The design of the control algorithm as well as its tuning through realistic simulations is presented. Taking into consideration the synergism of different paradigms like fuzzy logic, neural network, and symbolic artificial intelligence (AI), in this paper we present a real and fulfilled intelligent control architecture with application in bioprocess control.Keywords: bioprocess, intelligent control, neural nets, fuzzy structure, hybrid techniques
Procedia PDF Downloads 4111982 Potential Field Functions for Motion Planning and Posture of the Standard 3-Trailer System
Authors: K. Raghuwaiya, S. Singh, B. Sharma, J. Vanualailai
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This paper presents a set of artificial potential field functions that improves upon; in general, the motion planning and posture control, with theoretically guaranteed point and posture stabilities, convergence and collision avoidance properties of 3-trailer systems in a priori known environment. We basically design and inject two new concepts; ghost walls and the Distance Optimization Technique (DOT) to strengthen point and posture stabilities, in the sense of Lyapunov, of our dynamical model. This new combination of techniques emerges as a convenient mechanism for obtaining feasible orientations at the target positions with an overall reduction in the complexity of the navigation laws. The effectiveness of the proposed control laws were demonstrated via simulations of two traffic scenarios.Keywords: artificial potential fields, 3-trailer systems, motion planning, posture, parking and collision, free trajectories
Procedia PDF Downloads 3731981 The Synopsis of the AI-Powered Therapy Web Platform ‘Free AI Therapist'
Authors: Arwa Alnowaiser, Hala Shoukri
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The ‘FreeAITherapist’ is an artificial intelligence application that uses the power of AI to offer advice and mental health counseling to its users through its chatbot services. The AI therapist is designed to understand users' issues, concerns, and problems and respond appropriately; it provides empathy and guidance and uses evidence-based therapeutic techniques. With its user-friendly platform, it ensures accessibility for individuals in need, regardless of their geographical location. This website was created in direct response to the growing demand for mental health support, aiming to provide a cost-effective and confidential solution. Through promising confidentiality, it considers user privacy and data security. The ‘FreeAITherapist’ strives to bridge the gap in mental health services, offering a reliable resource for individuals seeking guidance and counseling to improve their overall well-being.Keywords: artificial intelligence, mental health, AI therapist, website, counseling
Procedia PDF Downloads 361980 Review of Full Body Imaging and High-Resolution Automatic 3D Mapping Systems for Medical Application
Authors: Jurijs Salijevs, Katrina Bolocko
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The integration of artificial intelligence and neural networks has significantly changed full-body imaging and high-resolution 3D mapping systems, and this paper reviews research in these areas. With an emphasis on their use in the early identification of melanoma and other disorders, the goal is to give a wide perspective on the current status and potential future of these medical imaging technologies. Authors also examine methodologies such as machine learning and deep learning, seeking to identify efficient procedures that enhance diagnostic capabilities through the analysis of 3D body scans. This work aims to encourage further research and technological development to harness the full potential of AI in disease diagnosis.Keywords: artificial intelligence, neural networks, 3D scan, body scan, 3D mapping system, healthcare
Procedia PDF Downloads 901979 Artificial Intelligence-Based Thermal Management of Battery System for Electric Vehicles
Authors: Raghunandan Gurumurthy, Aricson Pereira, Sandeep Patil
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The escalating adoption of electric vehicles (EVs) across the globe has underscored the critical importance of advancing battery system technologies. This has catalyzed a shift towards the design and development of battery systems that not only exhibit higher energy efficiency but also boast enhanced thermal performance and sophisticated multi-material enclosures. A significant leap in this domain has been the incorporation of simulation-based design optimization for battery packs and Battery Management Systems (BMS), a move further enriched by integrating artificial intelligence/machine learning (AI/ML) approaches. These strategies are pivotal in refining the design, manufacturing, and operational processes for electric vehicles and energy storage systems. By leveraging AI/ML, stakeholders can now predict battery performance metrics—such as State of Health, State of Charge, and State of Power—with unprecedented accuracy. Furthermore, as Li-ion batteries (LIBs) become more prevalent in urban settings, the imperative for bolstering thermal and fire resilience has intensified. This has propelled Battery Thermal Management Systems (BTMs) to the forefront of energy storage research, highlighting the role of machine learning and AI not just as tools for enhanced safety management through accurate temperature forecasts and diagnostics but also as indispensable allies in the early detection and warning of potential battery fires.Keywords: electric vehicles, battery thermal management, industrial engineering, machine learning, artificial intelligence, manufacturing
Procedia PDF Downloads 811978 Using AI for Analysing Political Leaders
Authors: Shuai Zhao, Shalendra D. Sharma, Jin Xu
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This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.Keywords: comparative politics, political executives, leaders’ characteristics, artificial intelligence
Procedia PDF Downloads 811977 Educational Leadership and Artificial Intelligence
Authors: Sultan Ghaleb Aldaihani
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- The environment in which educational leadership takes place is becoming increasingly complex due to factors like globalization and rapid technological change. - This is creating a "leadership gap" where the complexity of the environment outpaces the ability of leaders to effectively respond. - Educational leadership involves guiding teachers and the broader school system towards improved student learning and achievement. 2. Implications of Artificial Intelligence (AI) in Educational Leadership: - AI has great potential to enhance education, such as through intelligent tutoring systems and automating routine tasks to free up teachers. - AI can also have significant implications for educational leadership by providing better information and data-driven decision-making capabilities. - Computer-adaptive testing can provide detailed, individualized data on student learning that leaders can use for instructional decisions and accountability. 3. Enhancing Decision-Making Processes: - Statistical models and data mining techniques can help identify at-risk students earlier, allowing for targeted interventions. - Probability-based models can diagnose students likely to drop out, enabling proactive support. - These data-driven approaches can make resource allocation and decision-making more effective. 4. Improving Efficiency and Productivity: - AI systems can automate tasks and change processes to improve the efficiency of educational leadership and administration. - Integrating AI can free up leaders to focus more on their role's human, interactive elements.Keywords: Education, Leadership, Technology, Artificial Intelligence
Procedia PDF Downloads 261976 Joint Space Hybrid Force/Position Control of 6-DoF Robot Manipulator Using Neural Network
Authors: Habtemariam Alemu
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It has been known that the performance of position and force control is highly affected by both robot dynamic and environment stiffness uncertainties. In this paper, joint space hybrid force and position control strategy with self-selecting matrix using artificial neural network compensator is proposed. The objective of the work is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. Simulation results for a 6 degree of freedom (6-DoF) manipulator and different types of environments showed the effectiveness of the suggested approach. 6-DoF Puma 560 family robot manipulator is chosen as industrial robot and its efficient dynamic model is designed using Matlab/SimMechanics library.Keywords: robot manipulator, force/position control, artificial neural network, Matlab/Simulink
Procedia PDF Downloads 5061975 Active Control Improvement of Smart Cantilever Beam by Piezoelectric Materials and On-Line Differential Artificial Neural Networks
Authors: P. Karimi, A. H. Khedmati Bazkiaei
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The main goal of this study is to test differential neural network as a controller of smart structure and is to enumerate its advantages and disadvantages in comparison with other controllers. In this study, the smart structure has been considered as a Euler Bernoulli cantilever beam and it has been tried that it be under control with the use of vibration neural network resulting from movement. Also, a linear observer has been considered as a reference controller and has been compared its results. The considered vibration charts and the controlled state have been recounted in the final part of this text. The obtained result show that neural observer has better performance in comparison to the implemented linear observer.Keywords: smart material, on-line differential artificial neural network, active control, finite element method
Procedia PDF Downloads 2061974 Machine Learning Techniques to Develop Traffic Accident Frequency Prediction Models
Authors: Rodrigo Aguiar, Adelino Ferreira
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Road traffic accidents are the leading cause of unnatural death and injuries worldwide, representing a significant problem of road safety. In this context, the use of artificial intelligence with advanced machine learning techniques has gained prominence as a promising approach to predict traffic accidents. This article investigates the application of machine learning algorithms to develop traffic accident frequency prediction models. Models are evaluated based on performance metrics, making it possible to do a comparative analysis with traditional prediction approaches. The results suggest that machine learning can provide a powerful tool for accident prediction, which will contribute to making more informed decisions regarding road safety.Keywords: machine learning, artificial intelligence, frequency of accidents, road safety
Procedia PDF Downloads 781973 Human Resource Management Challenges in Age of Artificial Intelligence: Methodology of Case Analysis
Authors: Olga Leontjeva
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In the age of Artificial Intelligence (AI), some organization management approaches need to be adapted or changed. Human Resource Management (HRM) is a part of organization management that is under the managers' focus nowadays, because AI integration into organization activities brings some HRM-connected challenges. The topic became more significant during the crises of many organizations in the world caused by the coronavirus pandemic (COVID-19). The paper presents an approach, which will be used for the study that is going to be focused on the various case analysis. The author of the future study will analyze the cases of the organizations from Latvia and Spain that are grouped by the size, type of activity and area of business. The information for the cases will be collected through structured interviews and online surveys. The main result presented is the questionnaire developed that will be used for the study as well as the definition and description of sampling. The first round of the survey will be based on convenience sampling that is the main limitation of the study. To conclude, the approach developed will help to collect valid data if the organizations participating in the survey are ready to share their cases in depth, so the researchers could draw the right conclusions and generalize compared organizations’ cases. The questionnaire developed for the survey is applicable for both written online data collection as well as for the interviews. The case analysis will help to identify some HRM challenges that are connected to AI integration into organization activities such as management of different generation employees and their training peculiarities.Keywords: age of artificial intelligence, case analysis, generation Y and Z employees, human resource management
Procedia PDF Downloads 1631972 Evaluation of Re-mineralization Ability of Nanohydroxyapatite and Coral Calcium with Different Concentrations on Initial Enamel Carious Lesions
Authors: Ali Abdelnabi, Nermeen Hamza
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Coral calcium is a boasting natural product and dietary supplement which is considered a source of alkaline calcium carbonate, this study is a comparative study, comparing the remineralization effect of the new product of coral calcium with that of nano-hydroxyapatite. Methodology: a total of 35 extracted molars were collected, examined and sectioned to obtain 70 sound enamel discs, all discs were numbered and examined by scanning electron microscope coupled with Energy Dispersive Analysis of X-rays(EDAX) for mineral content, subjected to artificial caries, and mineral content was re-measured, discs were divided into seven groups according to the remineralizing agent used, where groups 1 to 3 used 10%, 20%, 30% nanohydroxyapatite gel respectively, groups 4 to 6 used 10%, 20%, 30% coral calcium gel and group 7 with no remineralizing agent (control group). All groups were re-examined by EDAX after remineralization; data were calculated and tabulated. Results: All groups showed a statistically significant drop in calcium level after artificial caries; all groups showed a statistically significant rise in calcium content after remineralization except for the control group; groups 1 and 5 showed the highest increase in calcium level after remineralization. Conclusion: coral calcium can be considered a comparative product to nano-hydroxyapatite regarding the remineralization of enamel initial carious lesions.Keywords: artificial caries, coral calcium, nanohydroxyapatite, re-mineralization
Procedia PDF Downloads 1111971 Artificial Membrane Comparison for Skin Permeation in Skin PAMPA
Authors: Aurea C. L. Lacerda, Paulo R. H. Moreno, Bruna M. P. Vianna, Cristina H. R. Serra, Airton Martin, André R. Baby, Vladi O. Consiglieri, Telma M. Kaneko
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The modified Franz cell is the most widely used model for in vitro permeation studies, however it still presents some disadvantages. Thus, some alternative methods have been developed such as Skin PAMPA, which is a bio- artificial membrane that has been applied for skin penetration estimation of xenobiotics based on HT permeability model consisting. Skin PAMPA greatest advantage is to carry out more tests, in a fast and inexpensive way. The membrane system mimics the stratum corneum characteristics, which is the primary skin barrier. The barrier properties are given by corneocytes embedded in a multilamellar lipid matrix. This layer is the main penetration route through the paracellular permeation pathway and it consists of a mixture of cholesterol, ceramides, and fatty acids as the dominant components. However, there is no consensus on the membrane composition. The objective of this work was to compare the performance among different bio-artificial membranes for studying the permeation in skin PAMPA system. Material and methods: In order to mimetize the lipid composition`s present in the human stratum corneum six membranes were developed. The membrane composition was equimolar mixture of cholesterol, ceramides 1-O-C18:1, C22, and C20, plus fatty acids C20 and C24. The membrane integrity assay was based on the transport of Brilliant Cresyl Blue, which has a low permeability; and Lucifer Yellow with very poor permeability and should effectively be completely rejected. The membrane characterization was performed using Confocal Laser Raman Spectroscopy, using stabilized laser at 785 nm with 10 second integration time and 2 accumulations. The membrane behaviour results on the PAMPA system were statistically evaluated and all of the compositions have shown integrity and permeability. The confocal Raman spectra were obtained in the region of 800-1200 cm-1 that is associated with the C-C stretches of the carbon scaffold from the stratum corneum lipids showed similar pattern for all the membranes. The ceramides, long chain fatty acids and cholesterol in equimolar ratio permitted to obtain lipid mixtures with self-organization capability, similar to that occurring into the stratum corneum. Conclusion: The artificial biological membranes studied for Skin PAMPA showed to be similar and with comparable properties to the stratum corneum.Keywords: bio-artificial membranes, comparison, confocal Raman, skin PAMPA
Procedia PDF Downloads 5031970 A Comparison of Neural Network and DOE-Regression Analysis for Predicting Resource Consumption of Manufacturing Processes
Authors: Frank Kuebler, Rolf Steinhilper
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Artificial neural networks (ANN) as well as Design of Experiments (DOE) based regression analysis (RA) are mainly used for modeling of complex systems. Both methodologies are commonly applied in process and quality control of manufacturing processes. Due to the fact that resource efficiency has become a critical concern for manufacturing companies, these models needs to be extended to predict resource-consumption of manufacturing processes. This paper describes an approach to use neural networks as well as DOE based regression analysis for predicting resource consumption of manufacturing processes and gives a comparison of the achievable results based on an industrial case study of a turning process.Keywords: artificial neural network, design of experiments, regression analysis, resource efficiency, manufacturing process
Procedia PDF Downloads 5161969 The Comparison of Chromium Ions Release for Stainless Steel between Artificial Saliva and Breadfruit Leaf Extracts
Authors: Mirna Febriani
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The use of stainless steel wires in the field of dentistry is widely used, especially for orthodontic and prosthodontic treatment using stainless steel wire. The oral cavity is the ideal environment for corrosion, which can be caused by saliva. Prevention of corrosion on stainless steel wires can be done by using an organic or non-organic corrosion inhibitor. One of the organic inhibitors that can be used to prevent corrosion is the leaves of breadfruit. The method used for this research using Atomic Absorption Spectrophotometric test. The results showed that the difference of chromium ion releases on soaking in saliva and breadfruit leaf extracts on days 1, 3, 7 and 14. Statically calculation with independent T-test with p < 0,05 showed the significant difference. The conclusion of this study shows that breadfruit leaf extract can inhibit the corrosion rate of stainless steel wires.Keywords: chromium ion, stainless steel, artificial saliva, breadfruit leaf
Procedia PDF Downloads 1631968 Anticipation of Bending Reinforcement Based on Iranian Concrete Code Using Meta-Heuristic Tools
Authors: Seyed Sadegh Naseralavi, Najmeh Bemani
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In this paper, different concrete codes including America, New Zealand, Mexico, Italy, India, Canada, Hong Kong, Euro Code and Britain are compared with the Iranian concrete design code. First, by using Adaptive Neuro Fuzzy Inference System (ANFIS), the codes having the most correlation with the Iranian ninth issue of the national regulation are determined. Consequently, two anticipated methods are used for comparing the codes: Artificial Neural Network (ANN) and Multi-variable regression. The results show that ANN performs better. Predicting is done by using only tensile steel ratio and with ignoring the compression steel ratio.Keywords: adaptive neuro fuzzy inference system, anticipate method, artificial neural network, concrete design code, multi-variable regression
Procedia PDF Downloads 2761967 Study of Surface Water Quality in the Wadi El Harrach for Its Use in the Artificial Groundwater Recharge of the Mitidja, North Algeria
Authors: M. Meddi, A. Boufekane
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The Mitidja coastal groundwater which extends over an area of 1450 km2 is a strategic resource in the Algiers region. The high dependence of the regional economy on the use of this groundwater forces us to have recourse to its artificial recharge from the Wadi El Harrach in its upstream part. This system of artificial recharge has shown its effectiveness in the development of water resource mentioned in the succeeding works in several regions of the world. The objective of this study is to: Increase the reserves of water inputs by infiltration, raise the water level and its good quality in wells and boreholes, reduce losses to the sea, and address seawater intrusion by maintaining balance in the freshwater-saltwater interface in the downstream part of the groundwater basin. After analyzing the situation, it was noticed that a qualitative monitoring of the Wadi water for the groundwater recharge has to be done. For this purpose, we proceeded during three successive years (2010, 2011, and 2012) to the monthly sampling of water in the upstream part of the Wadi El Harrach for chemical analysis. The variation of the sediment transport concentration will be also measured. This monitoring aims to characterize the water quality and avoid clogging in the proposed recharge area. The results of these analyses showed the good chemical quality according to the analyses we performed in the laboratory during the three years, but they are too loaded with suspended matters. We noticed that these fine particles come from the grinding of limestone of sandpit located upstream of the area of the proposed recharge system. This problem can be solved by a water supply upstream of sandpit. For the recharge, we propose the method of using two wells for dual use, which means that it can be used for water supply and extraction. This solution is inexpensive in our case and could easily be used as wells are already drilled in the upstream part. This solution increases over time the piezometric level and also reduce groundwater contamination by saltwater in the downstream part.Keywords: water quality, artificial groundwater recharge, Mitidja, North Algeria
Procedia PDF Downloads 2781966 Modelling Biological Treatment of Dye Wastewater in SBR Systems Inoculated with Bacteria by Artificial Neural Network
Authors: Yasaman Sanayei, Alireza Bahiraie
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This paper presents a systematic methodology based on the application of artificial neural networks for sequencing batch reactor (SBR). The SBR is a fill-and-draw biological wastewater technology, which is specially suited for nutrient removal. Employing reactive dye by Sphingomonas paucimobilis bacteria at sequence batch reactor is a novel approach of dye removal. The influent COD, MLVSS, and reaction time were selected as the process inputs and the effluent COD and BOD as the process outputs. The best possible result for the discrete pole parameter was a= 0.44. In orderto adjust the parameters of ANN, the Levenberg-Marquardt (LM) algorithm was employed. The results predicted by the model were compared to the experimental data and showed a high correlation with R2> 0.99 and a low mean absolute error (MAE). The results from this study reveal that the developed model is accurate and efficacious in predicting COD and BOD parameters of the dye-containing wastewater treated by SBR. The proposed modeling approach can be applied to other industrial wastewater treatment systems to predict effluent characteristics. Note that SBR are normally operated with constant predefined duration of the stages, thus, resulting in low efficient operation. Data obtained from the on-line electronic sensors installed in the SBR and from the control quality laboratory analysis have been used to develop the optimal architecture of two different ANN. The results have shown that the developed models can be used as efficient and cost-effective predictive tools for the system analysed.Keywords: artificial neural network, COD removal, SBR, Sphingomonas paucimobilis
Procedia PDF Downloads 4061965 Artificial Intelligent-Based Approaches for Task Offloading, Resource Allocation and Service Placement of Internet of Things Applications: State of the Art
Authors: Fatima Z. Cherhabil, Mammar Sedrati, Sonia-Sabrina Bendib
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In order to support the continued growth, critical latency of IoT applications, and various obstacles of traditional data centers, mobile edge computing (MEC) has emerged as a promising solution that extends cloud data-processing and decision-making to edge devices. By adopting a MEC structure, IoT applications could be executed locally, on an edge server, different fog nodes, or distant cloud data centers. However, we are often faced with wanting to optimize conflicting criteria such as minimizing energy consumption of limited local capabilities (in terms of CPU, RAM, storage, bandwidth) of mobile edge devices and trying to keep high performance (reducing response time, increasing throughput and service availability) at the same time. Achieving one goal may affect the other, making task offloading (TO), resource allocation (RA), and service placement (SP) complex processes. It is a nontrivial multi-objective optimization problem to study the trade-off between conflicting criteria. The paper provides a survey on different TO, SP, and RA recent multi-objective optimization (MOO) approaches used in edge computing environments, particularly artificial intelligent (AI) ones, to satisfy various objectives, constraints, and dynamic conditions related to IoT applications.Keywords: mobile edge computing, multi-objective optimization, artificial intelligence approaches, task offloading, resource allocation, service placement
Procedia PDF Downloads 1071964 Comparison of ANN and Finite Element Model for the Prediction of Ultimate Load of Thin-Walled Steel Perforated Sections in Compression
Authors: Zhi-Jun Lu, Qi Lu, Meng Wu, Qian Xiang, Jun Gu
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The analysis of perforated steel members is a 3D problem in nature, therefore the traditional analytical expressions for the ultimate load of thin-walled steel sections cannot be used for the perforated steel member design. In this study, finite element method (FEM) and artificial neural network (ANN) were used to simulate the process of stub column tests based on specific codes. Results show that compared with those of the FEM model, the ultimate load predictions obtained from ANN technique were much closer to those obtained from the physical experiments. The ANN model for the solving the hard problem of complex steel perforated sections is very promising.Keywords: artificial neural network (ANN), finite element method (FEM), perforated sections, thin-walled Steel, ultimate load
Procedia PDF Downloads 3441963 Reference Architecture for Intelligent Enterprise Solutions
Authors: Shankar Kambhampaty, Harish Rohan Kambhampaty
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Data in IT systems in enterprises has been growing at a phenomenal pace. This has provided opportunities to run analytics to gather intelligence on key business parameters that enable them to provide better products and services to customers. While there are several artificial intelligence (AI/ML) and business intelligence (BI) tools and technologies available in the marketplace to run analytics, there is a need for an integrated view when developing intelligent solutions in enterprises. This paper progressively elaborates a reference model for enterprise solutions, builds an integrated view of data, information, and intelligence components, and presents a reference architecture for intelligent enterprise solutions. Finally, it applies the reference architecture to an insurance organization. The reference architecture is the outcome of experience and insights gathered from developing intelligent solutions for several organizations.Keywords: architecture, model, intelligence, artificial intelligence, business intelligence, AI, BI, ML, analytics, enterprise
Procedia PDF Downloads 1371962 Signal Restoration Using Neural Network Based Equalizer for Nonlinear channels
Authors: Z. Zerdoumi, D. Benatia, , D. Chicouche
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This paper investigates the application of artificial neural network to the problem of nonlinear channel equalization. The difficulties caused by channel distortions such as inter symbol interference (ISI) and nonlinearity can overcome by nonlinear equalizers employing neural networks. It has been shown that multilayer perceptron based equalizer outperform significantly linear equalizers. We present a multilayer perceptron based equalizer with decision feedback (MLP-DFE) trained with the back propagation algorithm. The capacity of the MLP-DFE to deal with nonlinear channels is evaluated. From simulation results it can be noted that the MLP based DFE improves significantly the restored signal quality, the steady state mean square error (MSE), and minimum Bit Error Rate (BER), when comparing with its conventional counterpart.Keywords: Artificial Neural Network, signal restoration, Nonlinear Channel equalization, equalization
Procedia PDF Downloads 492