Search results for: hardy cross networks accuracy

Authors: Mohamed Mokhtar, Mostafa F. Shaaban

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Among the challenges facing solar photovoltaic (PV) systems in the United Arab Emirates (UAE), dust accumulation on solar panels is considered the most severe problem that faces the growth of solar power plants. The accumulation of dust on the solar panels significantly degrades output from these panels. Hence, solar PV panels have to be cleaned manually or using costly automated cleaning methods. This paper focuses on initiating cleaning actions when required to reduce maintenance costs. The cleaning actions are triggered only when the dust level exceeds a threshold value. The amount of dust accumulated on the PV panels is estimated using an artificial neural network (ANN). Experiments are conducted to collect the required data, which are used in the training of the ANN model. Then, this ANN model will be fed by the output power from solar panels, ambient temperature, and solar irradiance, and thus, it will be able to estimate the amount of dust accumulated on solar panels at these conditions. The model was tested on different case studies to confirm the accuracy of the developed model.

Keywords: machine learning, dust, PV panels, renewable energy

Procedia PDF Downloads 137

Authors: Paul Idoko Ukanu, Sunday Abba, Balogun Sadiya

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The study of the finger prints patterns among the Igala ethnic groups was done in order to see their association gender. A cross sectional study was conducted and a total of 602 subjects participated in this study, 322 females and 280 males, which were mainly secondary school students between the age ranges of 13-19 years. The subjects fingerprint pattern was obtained by allowing them place the tip of each finger on the stamp pad, which is then imprinted on the questionnaire, this was done for both the left and right hand. Female had higher arch, whorl and loop finger print pattern in most of the right fingers than the males, the differences were statistically significant for the right index, right ring finger and right little finger, but were statistically insignificant for right thumb and right middle finger as p = 0.207 and 0.726, respectively. The result also revealed that males had higher arch finger print pattern in the right index and right little finger than the females, which was statistically significant (p = 0.001), and also a high whorl finger print pattern than the females in the right middle and ring finger.

Keywords: arch, loop, whorl, fingers

Procedia PDF Downloads 141

Authors: Anas H. Aljemely, Jianping Xuan

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Rolling bearing fault diagnosis plays a pivotal issue in the rotating machinery of modern manufacturing. In this research, a raw vibration signal and improved deep learning method for bearing fault diagnosis are proposed. The multi-dimensional scales of raw vibration signals are selected for evaluation condition monitoring system, and the deep learning process has shown its effectiveness in fault diagnosis. In the proposed method, employing an Exponential linear unit (ELU) layer in a convolutional neural network (CNN) that conducts the identical function on positive data, an exponential nonlinearity on negative inputs, and a particular convolutional operation to extract valuable features. The identification results show the improved method has achieved the highest accuracy with a 100-dimensional scale and increase the training and testing speed.

Keywords: bearing fault prognostics, developed CNN model, multiple-scale evaluation, deep learning features

Procedia PDF Downloads 200

Authors: Lu Si, Jie Yu, Shasha Li, Jun Ma, Lei Luo, Qingbo Wu, Yongqi Ma, Zhengji Liu

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Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical.

Keywords: instance selection, data reduction, MapReduce, kNN

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Authors: Serhat Tüzün, Tufan Demirel

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Decision Support Systems (DSS) have been investigated by researchers and technologists for more than 35 years. This paper analyses the developments in the architecture and software of these systems, provides a systematic analysis for different Web-based DSS approaches and Intelligent Decision-making Technologies (IDT), with the suggestion for future studies. Decision Support Systems literature begins with building model-oriented DSS in the late 1960s, theory developments in the 1970s, and the implementation of financial planning systems and Group DSS in the early and mid-80s. Then it documents the origins of Executive Information Systems, online analytic processing (OLAP) and Business Intelligence. The implementation of Web-based DSS occurred in the mid-1990s. With the beginning of the new millennia, intelligence is the main focus on DSS studies. Web-based technologies are having a major impact on design, development and implementation processes for all types of DSS. Web technologies are being utilized for the development of DSS tools by leading developers of decision support technologies. Major companies are encouraging its customers to port their DSS applications, such as data mining, customer relationship management (CRM) and OLAP systems, to a web-based environment. Similarly, real-time data fed from manufacturing plants are now helping floor managers make decisions regarding production adjustment to ensure that high-quality products are produced and delivered. Web-based DSS are being employed by organizations as decision aids for employees as well as customers. A common usage of Web-based DSS has been to assist customers configure product and service according to their needs. These systems allow individual customers to design their own products by choosing from a menu of attributes, components, prices and delivery options. The Intelligent Decision-making Technologies (IDT) domain is a fast growing area of research that integrates various aspects of computer science and information systems. This includes intelligent systems, intelligent technology, intelligent agents, artificial intelligence, fuzzy logic, neural networks, machine learning, knowledge discovery, computational intelligence, data science, big data analytics, inference engines, recommender systems or engines, and a variety of related disciplines. Innovative applications that emerge using IDT often have a significant impact on decision-making processes in government, industry, business, and academia in general. This is particularly pronounced in finance, accounting, healthcare, computer networks, real-time safety monitoring and crisis response systems. Similarly, IDT is commonly used in military decision-making systems, security, marketing, stock market prediction, and robotics. Even though lots of research studies have been conducted on Decision Support Systems, a systematic analysis on the subject is still missing. Because of this necessity, this paper has been prepared to search recent articles about the DSS. The literature has been deeply reviewed and by classifying previous studies according to their preferences, taxonomy for DSS has been prepared. With the aid of the taxonomic review and the recent developments over the subject, this study aims to analyze the future trends in decision support systems.

Keywords: decision support systems, intelligent decision-making, systematic analysis, taxonomic review

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Authors: Ryan Messina, Mehedi Hasan

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This research is to examine the impacts of using data to generate performance requirements for automation in visual inspections using machine vision. These situations are intended for design and how projects can smooth the transfer of tacit knowledge to using an algorithm. We have proposed a framework when specifying machine vision systems. This framework utilizes varying levels of automation as contingency planning to reduce data processing complexity. Using data assists in extracting tacit knowledge from those who can perform the manual tasks to assist design the system; this means that real data from the system is always referenced and minimizes errors between participating parties. We propose using three indicators to know if the project has a high risk of failing to meet requirements related to accuracy and reliability. All systems tested achieved a better integration into operations after applying the framework.

Keywords: automation, contingency planning, continuous engineering, control theory, machine vision, system requirements, system thinking

Procedia PDF Downloads 200

Authors: Adel Bougamouza, Boualam Remini, Abd El Hadi Ammari, Feteh Sakhraoui

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The assessment of sediments being carried by a river is importance for planning and designing of various water resources projects. In this study, Artificial Neural Network Techniques are used to estimate the daily suspended sediment concentration for the corresponding daily discharge flow in the upstream of Foum El Gherza dam, Biskra, Algeria. The FFNN, GRNN, and RBNN models are established for estimating current suspended sediment values. Some statistics involving RMSE and R2 were used to evaluate the performance of applied models. The comparison of three AI models showed that the RBNN model performed better than the FFNN and GRNN models with R2 = 0.967 and RMSE= 5.313 mg/l. Therefore, the ANN model had capability to improve nonlinear relationships between discharge flow and suspended sediment with reasonable precision.

Keywords: artificial neural network, Oued Abiod watershed, feedforward network, generalized regression network, radial basis network, sediment concentration

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Authors: Birgit A. A. Solem, Kristin Bentsen

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Scholarly attention in entrepreneurship is currently directed towards understanding entrepreneurial venture creation as a process -the journey of new economic activities from nonexistence to existence often studied through flow- or network models. To complement existing research on entrepreneurial venture creation with more interactivity-based research of organized activities, this study examines two pop-up stores as anchor events involving on-site activities of fifteen participating entrepreneurs launching their new ventures. The pop-up stores were arranged in two middle-sized Norwegian cities and contained different brand stores that brought together actors of sub-networks and communities executing venture creation activities. The pop-up stores became on-site arenas for the entrepreneurs to create, maintain, and rejuvenate their networks, at the same time as becoming venues for temporal coordination of activities involving existing and potential customers in their venture creation. In this work, we apply a conceptual framework based on frequently addressed dilemmas within entrepreneurship theory (discovery/creation, causation/effectuation) to further shed light on the broad aspect of on-site anchor event activities and their venture creation outcomes. The dilemma-based concepts are applied as an analytic toolkit to pursue answers regarding the nature of anchor event activities typically found within entrepreneurial venture creation and how these anchor event activities affect entrepreneurial venture creation outcomes. Our study combines researcher participation with 200 hours of observation and twenty in-depth interviews. Data analysis followed established guidelines for hermeneutic analysis and was intimately intertwined with ongoing data collection. Data was coded and categorized in NVivo 12 software, and iterated several times as patterns were steadily developing. Our findings suggest that core anchor event activities typically found within entrepreneurial venture creation are; a concept- and product experimentation with visitors, arrangements to socialize (evening specials, auctions, and exhibitions), store-in-store concepts, arranged meeting places for peers and close connection with municipality and property owners. Further, this work points to four main entrepreneurial venture creation outcomes derived from the core anchor event activities; (1) venture attention, (2) venture idea-realization, (3) venture collaboration, and (4) venture extension. Our findings show that, depending on which anchor event activities are applied, the outcomes vary. Theoretically, this study offers two main implications. First, anchor event activities are both discovered and created, following the logic of causation, at the same time as being experimental, based on “learning by doing” principles of effectuation during the execution. Second, our research enriches prior studies on venture creation as a process. In this work, entrepreneurial venture creation activities and outcomes are understood through pop-up stores as on-site anchor event arenas, particularly suitable for interactivity-based research requested by the entrepreneurship field. This study also reveals important managerial implications, such as that entrepreneurs should allow themselves to find creative physical venture creation arenas (e.g., pop-up stores, showrooms), as well as collaborate with partners when discovering and creating concepts and activities based on new ideas. In this way, they allow themselves to both strategically plan for- and continually experiment with their venture.

Keywords: anchor event, interactivity-based research, pop-up store, entrepreneurial venture creation

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Authors: Ijeoma Chinyere Ukonu

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This research seeks to investigate how the globalisation of fast food has affected students’ food choice. A mixed method approach was used in this research; basically involving quantitative and qualitative methods. The quantitative method uses a self-completion questionnaire to randomly sample one hundred and four students; while the qualitative method uses a semi structured interview technique to survey four students on their knowledge and choice to consume fast food. A cross tabulation of variables and the Kruskal Wallis nonparametric test were used to analyse the quantitative data; while the qualitative data was analysed through deduction of themes, and trends from the interview transcribe. The findings revealed that globalisation has amplified the evolution of fast food, popularising it among students. Its global presence has affected students’ food choice and preference. Price, convenience, taste, and peer influence are some of the major factors affecting students’ choice of fast food. Though, students are familiar with the health effect of fast food and the significance of using food information labels for healthy choice making, their preference of fast food is more than homemade food.

Keywords: fast food, food choice, globalisation, students

Procedia PDF Downloads 286

Authors: Aadityaa Singla

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The big five personality traits are as follows: openness, conscientiousness, extraversion, agreeableness, and neuroticism. In order to get an insight into their personality, many flocks to these categories, which each have different meanings/characteristics. This information is important not only to individuals but also to career professionals and psychologists who can use this information for candidate assessment or job recruitment. The links between AI and psychology have been well studied in cognitive science, but it is still a rather novel development. It is possible for various AI classification models to accurately predict a personality question via ten input questions. This would contrast with the hundred questions that normal humans have to answer to gain a complete picture of their five personality traits. In order to approach this problem, various AI classification models were used on a dataset to predict what a user may answer. From there, the model's prediction was compared to its actual response. Normally, there are five answer choices (a 20% chance of correct guess), and the models exceed that value to different degrees, proving their significance. By utilizing an MLP classifier, decision tree, linear model, and K-nearest neighbors, they were able to obtain a test accuracy of 86.643, 54.625, 47.875, and 52.125, respectively. These approaches display that there is potential in the future for more nuanced predictions to be made regarding personality.

Keywords: machine learning, personally, big five personality traits, cognitive science

Procedia PDF Downloads 143

Authors: M. A. Aleem, M. S. Awan

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Flat spiral coil for electromagnetic forming system has been modelled in FEMM 4.2 software. Copper strip was chosen as the material for designing the actuator coil. Relationship between height to width ratio (S-factor) of the copper strip and coil’s performance has been studied. Magnetic field intensities, eddy currents, and Lorentz force were calculated for the coils that were designed using six different 'S-factor' values (0.65, 0.75, 1.05, 1.25, 1.54 and 1.75), keeping the cross-sectional area of strip the same. Results obtained through simulation suggest that actuator coil with S-factor ~ 1 shows optimum forming performance as it exerts maximum Lorentz force (84 kN) on work piece. The same coils were fabricated and used for electromagnetic sheet forming experiments. Aluminum 6061 sheets of thickness 1.5 mm have been formed using different voltage levels of capacitor bank. Smooth forming profiles were obtained with dome heights 28, 35 and 40 mm in work piece at 800, 1150 and 1250 V respectively.

Keywords: FEM modelling, electromagnetic forming, spiral coil, Lorentz force

Procedia PDF Downloads 283

Authors: Ahd Aljarf, Saad Amin

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Images are important source of information used as evidence during any investigation process. Their clarity and accuracy is essential and of the utmost importance for any investigation. Images are vulnerable to losing blocks and having noise added to them either after alteration or when the image was taken initially, therefore, having a high performance image processing system and it is implementation is very important in a forensic point of view. This paper focuses on improving the quality of the forensic images. For different reasons packets that store data can be affected, harmed or even lost because of noise. For example, sending the image through a wireless channel can cause loss of bits. These types of errors might give difficulties generally for the visual display quality of the forensic images. Two of the images problems: noise and losing blocks are covered. However, information which gets transmitted through any way of communication may suffer alteration from its original state or even lose important data due to the channel noise. Therefore, a developed system is introduced to improve the quality and clarity of the forensic images.

Keywords: image filtering, image reconstruction, image processing, forensic images

Procedia PDF Downloads 361

Authors: Seyed Mohammadbagher Jafari, Elahe Mahdizadeh, Masomeh Ghahremani

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Nowadays almost all organizations depend on information systems to run their businesses. Investment on information systems and their maintenance to keep them always in best situation to support firm business is one of the main issues for every organization. The new concept of cloud computing was developed as a technical and economic model to address this issue. In cloud computing the computing resources, including networks, applications, hardwares and services are configured as needed and are available at the moment of request. However, migration to cloud is not an easy task and there are many issues that should be taken into account. This study tries to provide a comprehensive model to assess a firm readiness to implement cloud computing. By conducting a systematic literature review, four dimensions of readiness were extracted which include technological, human, organizational and environmental dimensions. Every dimension has various criteria that have been discussed in details. This model provides a framework for cloud computing readiness assessment. Organizations that intend to migrate to cloud can use this model as a tool to assess their firm readiness before making any decision on cloud implementation.

Keywords: cloud computing, human readiness, organizational readiness, readiness assessment model

Procedia PDF Downloads 390

Authors: Takanori Tanaka, Daisuke Kitao, Daisuke Ikeda

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The aurora is an attractive phenomenon but it is difficult to understand the whole mechanism of it. An approach of data-intensive science might be an effective approach to elucidate such a difficult phenomenon. To do that we need labeled data, which shows when and what types of auroras, have appeared. In this paper, we propose an image retrieval system for auroral all-sky images, some of which include discrete and diffuse aurora, and the other do not any aurora. The proposed system retrieves images which are similar to the query image by using a popular image recognition method. Using 300 all-sky images obtained at Tromso Norway, we evaluate two methods of image recognition methods with or without our original color filtering method. The best performance is achieved when SIFT with the color filtering is used and its accuracy is 81.7% for discrete auroras and 86.7% for diffuse auroras.

Keywords: data-intensive science, image classification, content-based image retrieval, aurora

Procedia PDF Downloads 445

Authors: Alaba M. Dare, Idris A. Ayinde, Adebayo M. Shittu, Sam O. Sam-Wobo

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Households' source of water is one of the core development indicators recently gaining pre-eminence in Nigeria. This study examined rural households' sources of water, Willingness to Pay (WTP) and factors influencing mean WTP. A cross-sectional survey which involved the use of questionnaire was used. A dichotomous choice (DC) with follow up was used as elicitation method. A multi-stage random sampling technique was used to select 437 rural households. Descriptive statistics and Tobit model were used for data estimation. The result revealed that about 70% fetched from unimproved water sources. Most (74.4%) respondents showed WTP for improved water sources. Age (p < 0.01), sex (p < 0.01), education (p < 0.01), occupation (p < 0.01), income (p < 0.01), price of water (P < 0.01), quantity of water (p < 0.01), household size (p < 0.01) and distance (p < 0.01) to existing water sources significantly influenced rural households' WTP for these services. The inference from this study showed that rural dweller sources of water is highly primitive and deplorable. Governments and stakeholders should prioritize the provision of rural water at an affordable price by rural dwellers.

Keywords: households, source of water, willingness to pay (WTP), tobit model

Procedia PDF Downloads 374

Authors: Çiğdem Bakır, Mecit Yüzkat

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With the increasing emphasis on water quality worldwide, the search for and expanding the market for new and intelligent monitoring systems has increased. The current method is the laboratory process, where samples are taken from bodies of water, and tests are carried out in laboratories. This method is time-consuming, a waste of manpower, and uneconomical. To solve this problem, we used machine learning methods to detect water pollution in our study. We created decision trees with the Orange3 software we used in our study and tried to determine all the factors that cause water pollution. An automatic prediction model based on water quality was developed by taking many model inputs such as water temperature, pH, transparency, conductivity, dissolved oxygen, and ammonia nitrogen with machine learning methods. The proposed approach consists of three stages: preprocessing of the data used, feature detection, and classification. We tried to determine the success of our study with different accuracy metrics and the results. We presented it comparatively. In addition, we achieved approximately 98% success with the decision tree.

Keywords: decision tree, water quality, water pollution, machine learning

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Authors: Shouyong Zhou, Meisheng Li, Yijiang Zhao

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The environmentally acceptable disposal of oily wastewater is a current challenge to many industries. The membrane separation technologies, which is no phase change, without pharmaceutical dosing, reprocessing costs low, less energy consumption, etc., have been widely applied in oily wastewater treatment. In our lab, a kind of low cost ceramic microfiltration membranes with a separation layer of attapulgite nanofibers (attapulgite nanofiber-like microfiltration membranes) has been prepared and applied in the purification of cellulase fermentation broth and TiO2 nanoparticles system successfully. In this paper, this new attapulgite nanofiber-like microfiltration membrane was selected to try to separate water from oily wastewater. The oil-in water emulsion was obtained from mixing 1 g/L engine oil, 0.5 g/L Tween-80, 0.5 g/L Span-80 and distilled water at mild speed in blender for 2 min. The particle size distribution of the oil-in-water emulsion was controlled. The maximum steady flux and COD rejection for a 0.2 um attapulgite nanofiber-like microfiltration membrane can reach about 450 L. m-2. h-1 and 98% at 0.2 MPa. The results obtained in this work indicated that the attapulgite microfiltration membrane may represent a feasible pretreatment for oily wastewater.

Keywords: attapulgite, microfiltration membrane, oily wastewater, cross-flow filtration

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Authors: Abdulrahman Abdulrahman, Abir Abdulrahman

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Free over-fall is an instrument for measuring discharge in open channels by measuring end depth. A comprehensive researchers investigated theoretically and experimentally brink phenomenon with various approaches for different cross-sectional shapes. Anderson's method, based on Boussinq's approximation and energy approach was used to derive a pressure distribution factor at end depth. Applying the one-dimensional momentum equation and the principles of limit slope analysis, a relevant analytical solution may be derived for brink depth ratio (EDR) in prismatic rectangular channel. Also relationships between end depth ratio and slope ratio for a given non-dimensional normal or critical depth with upstream supercritical flow regime are presented. Simple indirect procedure is used to estimate the end depth discharge ratio (EDD) for subcritical and supercritical flow using measured end depth. The comparison of this analysis with all previous theoretical and experimental studies showed an excellent agreement.

Keywords: analytical solution, brink depth, end depth, flow measurement, free over fall, hydraulics, rectangular channel

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Authors: Yassir AbdelRazig, Amine Ghanem

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Construction projects are information intensive in nature and involve many activities that are related to each other. Wireless technologies can be used to improve the accuracy and timeliness of data collected from construction sites and shares it with appropriate parties. Nonetheless, the construction industry tends to be conservative and shows hesitation to adopt new technologies. A main concern for owners, contractors or any person in charge on a job site is the cost of the technology in question. Wireless technologies are not cheap. There are a lot of expenses to be taken into consideration, and a study should be completed to make sure that the importance and savings resulting from the usage of this technology is worth the expenses. This research attempts to assess the effectiveness of using the appropriate wireless technologies based on criteria such as performance, reliability, and risk. The assessment is based on a utility function model that breaks down the selection issue into alternatives attribute. Then the attributes are assigned weights and single attributes are measured. Finally, single attribute are combined to develop one single aggregate utility index for each alternative.

Keywords: analytic hierarchy process, decision theory, utility function, wireless technologies

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Authors: Matthias Haenisch, Marc Godau, Julia Barreiro, Dominik Maxelon

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In times of ubiquitous digitalization and the increasing entanglement of humans and technologies in musical practices in the 21st century, it is to be asked, how popular musicians learn in the (post)digital Age. Against the backdrop of the increasing interest in transferring informal learning practices into formal settings of music education the interdisciplinary research association »MusCoDA – Musical Communities in the (Post)Digital Age« (University of Erfurt/University of Applied Sciences Clara Hoffbauer Potsdam, funded by the German Ministry of Education and Research, pursues the goal to derive an empirical model of collective songwriting practices from the study of informal lelearningf songwriters and bands that can be translated into pedagogical concepts for music education in schools. Drawing on concepts from Community of Musical Practice and Actor Network Theory, lelearnings considered not only as social practice and as participation in online and offline communities, but also as an effect of heterogeneous networks composed of human and non-human actors. Learning is not seen as an individual, cognitive process, but as the formation and transformation of actor networks, i.e., as a practice of assembling and mediating humans and technologies. Based on video stimulated recall interviews and videography of online and offline activities, songwriting practices are followed from the initial idea to different forms of performance and distribution. The data evaluation combines coding and mapping methods of Grounded Theory Methodology and Situational Analysis. This results in network maps in which both the temporality of creative practices and the material and spatial relations of human and technological actors are reconstructed. In addition, positional analyses document the power relations between the participants that structure the learning process of the field. In the area of online informal lelearninginitial key research findings reveal a transformation of the learning subject through the specific technological affordances of TikTok and Instagram and the accompanying changes in the learning practices of the corresponding online communities. Learning is explicitly shaped by the material agency of online tools and features and the social practices entangled with these technologies. Thus, any human online community member can be invited to directly intervene in creative decisions that contribute to the further compositional and structural development of songs. At the same time, participants can provide each other with intimate insights into songwriting processes in progress and have the opportunity to perform together with strangers and idols. Online Lelearnings characterized by an increase in social proximity, distribution of creative agency and informational exchange between participants. While it seems obvious that traditional notions not only of lelearningut also of the learning subject cannot be maintained, the question arises, how exactly the observed informal learning practices and the subject that emerges from the use of social media as online learning technologies can be transferred into contexts of formal learning

Keywords: informal learning, postdigitality, songwriting, actor-network theory, community of musical practice, social media, TikTok, Instagram, apps

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Authors: Francesco Colcerasa, Fabio Pisani

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We contribute to the subjective well-being literature testing the relationship between life satisfaction and inequality of opportunity in health, measured through the Human Opportunity Index calculated at the national level using individual socio-economic data from the cross-country European Social Survey sample. We compute several indexes of opportunity inequality in health, each obtained according to a different combination of circumstances (gender, immigrant status, parents’ education). We find a robust and significant relationship where life satisfaction is higher in correspondence with low levels of health opportunity inequality. The result is twofold. On the one hand, the importance of the well-being of other types of inequality than income inequality emerges. On the other hand, the socioeconomic roots of inequality in health are investigated, suggesting that circumstances at birth have a role in future well-being. Several rationales for the nexus between life satisfaction and inequality of opportunity in health are possible, which we investigate by splitting the sample. Among others, we find a prominent role of pro-social preferences – formalized as interest towards own offspring (which can be interpreted as intergenerational justice) – as a mediating factor of the relationship.

Keywords: Inequality of opportunity, subjective wellbeing, health, health inequality, inequality of opportunity in health

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Authors: Retno Ambarwati Sigit Lestari

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Microalga is one of the organisms that can be considered ideal and potential for raw material of bioenergy production, because the content of lipids in microalga is relatively high. Microalga is an aquatic organism that produces complex organic compounds from inorganic molecules using carbon dioxide as a carbon source, and sunlight for energy supply. Microalga-CO₂ fixation has potential advantages over other carbon captures and storage approaches, such as wide distribution, high photosynthetic rate, good environmental adaptability, and ease of operation. The rates of growth and CO₂ capture of microalga are influenced by CO₂ concentration and light intensity. This study quantitatively investigates the effects of CO₂ concentration on the rates of growth and CO₂ capture of a type of microalga, cultivated in bioreactors. The works include laboratory experiments as well as mathematical modelling. The mathematical models were solved numerically and the accuracy of the model was tested by the experimental data. It turned out that the mathematical model proposed can well quantitatively describe the growth and CO₂ capture of microalga, in which the effects of CO₂ concentration can be observed.

Keywords: Microalga, CO2 concentration, photobioreactor, mathematical model

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Authors: Man-Hong Lai, Dinusha S. Gunawardena, Kok-Sing Lim, Harith Ahmad

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In this paper, we have reported birefringence manipulation in regenerated high-birefringent fiber Bragg grating (RPMG) by using CO2 laser annealing method. The results indicate that the birefringence of RPMG remains unchanged after CO2 laser annealing followed by a slow cooling process, but reduced after the fast cooling process (~5.6×10-5). After a series of annealing procedures with different cooling rates, the obtained results show that slower the cooling rate, higher the birefringence of RPMG. The volume, thermal expansion coefficient (TEC) and glass transition temperature (Tg) change of stress applying part in RPMG during the cooling process are responsible for the birefringence change. Therefore, these findings are important to the RPMG sensor in high and dynamic temperature environment. The measuring accuracy, range and sensitivity of RPMG sensor are greatly affected by its birefringence value. This work also opens up a new application of CO2 laser for fiber annealing and birefringence modification.

Keywords: birefringence, CO2 laser annealing, regenerated gratings, thermal stress

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Authors: Mecheri Zeid Belmecheri, Maamar Ahfir, Izzet Kale

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Automatic cardiac auscultation is still a subject of research in order to establish an objective diagnosis. Recorded heart sounds as Phonocardiogram signals (PCG) can be used for automatic segmentation into components that have clinical meanings. These are the first sound, S1, the second sound, S2, and the systolic and diastolic components, respectively. In this paper, an automatic method is proposed for the robust segmentation of heart sounds. This method is based on calculating an intermediate sawtooth-shaped signal from the length variation of the recorded Phonocardiogram (PCG) signal in the time domain and, using its positive derivative function that is a binary signal in training a Recurrent Neural Network (RNN). Results obtained in the context of a large database of recorded PCGs with their simultaneously recorded ElectroCardioGrams (ECGs) from different patients in clinical settings, including normal and abnormal subjects, show a segmentation testing performance average of 76 % sensitivity and 94 % specificity.

Keywords: heart sounds, PCG segmentation, event detection, recurrent neural networks, PCG curve length

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Authors: Niloufar Yadgari

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GANs are a potent form of deep learning models that have found success in various fields. They are part of the larger group of generative techniques, which aim to produce authentic data using a probabilistic model that learns distributions from actual samples. In clinical settings, GANs have demonstrated improved abilities in capturing spatially intricate, nonlinear, and possibly subtle disease impacts in contrast to conventional generative techniques. This review critically evaluates the current research on how GANs are being used in imaging studies of different neurological conditions like Alzheimer's disease, brain tumors, aging of the brain, and multiple sclerosis. We offer a clear explanation of different GAN techniques for each use case in neuroimaging and delve into the key hurdles, unanswered queries, and potential advancements in utilizing GANs in this field. Our goal is to connect advanced deep learning techniques with neurology studies, showcasing how GANs can assist in clinical decision-making and enhance our comprehension of the structural and functional aspects of brain disorders.

Keywords: GAN, pathology, generative adversarial network, neuro imaging

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Authors: Manoj Reghu, Prabhu Rajagopal, C. V. Krishnamurthy, Krishnan Balasubramaniam

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A thorough understanding of guided ultrasonic wave behavior in structures is essential for the application of existing Non Destructive Evaluation (NDE) technologies, as well as for the development of new methods. However, the analysis of guided wave phenomena is challenging because of their complex dispersive and multimodal nature. Although numerical solution procedures have proven to be very useful in this regard, the increasing complexity of features and defects to be considered, as well as the desire to improve the accuracy of inspection often imposes a large computational cost. Hybrid models that combine numerical solutions for wave scattering with faster alternative methods for wave propagation have long been considered as a solution to this problem. However usually such models require modification of the base code of the solution procedure. Here we aim to develop Generic Hybrid models that can be directly applied to any two different solution procedures. With this goal in mind, a Numerical Hybrid model and an Analytical-Numerical Hybrid model has been developed. The concept and implementation of these Hybrid models are discussed in this paper.

Keywords: guided ultrasonic waves, Finite Element Method (FEM), Hybrid model

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Authors: Athena Nguyen, Rojin Belganeh

Abstract:

Detecting and identifying differences in like polymer materials are key factors in failure and deformulation analysis, and reverse engineering. Pyrolysis-GC/MS is an easy solid sample introduction technique which expands the application areas of gas chromatography and mass spectrometry. The Micro furnace pyrolyzer is directly interfaced with the GC injector preventing any potential of cold spot, carryover, and cross contamination. In this presentation, the analysis of the differences in three polystyrene samples is demonstrated. Although the three samples look very similar by Evolve gas analysis (EGA) and Flash pyrolysis, there are indications of small levels of other materials. By performing Thermal desorption-GC/MS, the additive compounds between samples show the differences. EGA, flash pyrolysis, and thermal desorption analysis are the different modes of operations of the micro-furnace pyrolyzer enabling users to perform multiple analytical techniques.

Keywords: Gas chromatography/Mass spectrometry, pyrolysis, pyrolyzer, thermal desorption-GC/MS

Procedia PDF Downloads 183

Authors: Mohadese Oghbaee, Borna Firouzi

Abstract:

The emergence of the Internet and more specifically the formation of social media has provided the ground for paying attention to new types of content dissemination. In recent years, Social media users share information, communicate with others, and exchange opinions on social events in this space. Many of the information published in this space are suspicious and produced with the intention of deceiving others. These contents are often called "fake news". Fake news, by disrupting the circulation of the concept and similar concepts such as fake news with correct information and misleading public opinion, has the ability to endanger the security of countries and deprive the audience of the basic right of free access to real information; Competing governments, opposition elements, profit-seeking individuals and even competing organizations, knowing about this capacity, act to distort and overturn the facts in the virtual space of the target countries and communities on a large scale and influence public opinion towards their goals. This process of extensive de-truthing of the information space of the societies has created a wave of harm and worries all over the world. The formation of these concerns has led to the opening of a new path of research for the timely containment and reduction of the destructive effects of fake news on public opinion. In addition, the expansion of this phenomenon has the potential to create serious and important problems for societies, and its impact on events such as the 2016 American elections, Brexit, 2017 French elections, 2019 Indian elections, etc., has caused concerns and led to the adoption of approaches It has been dealt with. In recent years, a simple look at the growth trend of research in "Scopus" shows an increasing increase in research with the keyword "false information", which reached its peak in 2020, namely 524 cases, reached, while in 2015, only 30 scientific-research contents were published in this field. Considering that one of the capabilities of social media is to create a context for the dissemination of news and information, both true and false, in this article, the classification of strategies for spreading fake news in social networks was investigated in social events. To achieve this goal, thematic analysis research method was chosen. In this way, an extensive library study was first conducted in global sources. Then, an in-depth interview was conducted with 18 well-known specialists and experts in the field of news and media in Iran. These experts were selected by purposeful sampling. Then by analyzing the data using the theme analysis method, strategies were obtained; The strategies achieved so far (research is in progress) include unrealistically strengthening/weakening the speed and content of the event, stimulating psycho-media movements, targeting emotional audiences such as women, teenagers and young people, strengthening public hatred, calling the reaction legitimate/illegitimate. events, incitement to physical conflict, simplification of violent protests and targeted publication of images and interviews were introduced.

Keywords: fake news, social network, social events, thematic analysis

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Authors: Zeeshan Hamid, Asher Ramish

Abstract:

The purpose of this paper is to focus on security and safety issues facing by pharmaceutical industry globally when counterfeit drugs are in question. Hence, there is an intense need to secure and authenticate pharmaceutical products in the emerging counterfeit product market. This paper will elaborate the application of radio frequency identification (RFID) in pharmaceutical industry and to identify its key benefits for patient’s care. The benefits are: help to co-ordinate the stream of supplies, accuracy in chains of supplies, maintaining trustworthy information, to manage the operations in appropriate and timely manners and finally deliver the genuine drug to patient. It is discussed that how RFID supported supply chain information sharing (SCIS) helps to combat against counterfeit drugs. And a solution how to tag pharmaceutical products; since, some products prevent RFID implementation in this industry. In this paper, a proposed model for pharma industry distribution suggested to combat against the counterfeit drugs when they are in supply chain.

Keywords: supply chain, RFID, pharmaceutical industry, counterfeit drugs, patients care

Procedia PDF Downloads 309

Authors: M. Pepe, S. Ackermann, L. Fregonese, C. Achille

Abstract:

3D models obtained with advanced survey techniques such as close-range photogrammetry and laser scanner are nowadays particularly appreciated in Cultural Heritage and Archaeology fields. In order to produce high quality models representing archaeological evidences and anthropological artifacts, the appearance of the model (i.e. color) beyond the geometric accuracy, is not a negligible aspect. The integration of the close-range photogrammetry survey techniques with the laser scanner is still a topic of study and research. By combining point cloud data sets of the same object generated with both technologies, or with the same technology but registered in different moment and/or natural light condition, could construct a final point cloud with accentuated color dissimilarities. In this paper, a methodology to uniform the different data sets, to improve the chromatic quality and to highlight further details by balancing the point color will be presented.

Keywords: color models, cultural heritage, laser scanner, photogrammetry

Procedia PDF Downloads 276