Search results for: big data platforms

Authors: Fuad M. Alkoot

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DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN). 

Keywords: biometrics, genetic data, identity verification, k nearest neighbor

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Authors: R Palson Kennedy, P.Kiran Sai

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This article introduces machine learning (ML) researchers to the hurdles that geoscience problems present, as well as the opportunities for improvement in both ML and geosciences. This article presents a review from the data life cycle perspective to meet that need. Numerous facets of geosciences present unique difficulties for the study of intelligent systems. Geosciences data is notoriously difficult to analyze since it is frequently unpredictable, intermittent, sparse, multi-resolution, and multi-scale. The first half addresses data science’s essential concepts and theoretical underpinnings, while the second section contains key themes and sharing experiences from current publications focused on each stage of the data life cycle. Finally, themes such as open science, smart data, and team science are considered.

Keywords: Data science, intelligent system, machine learning, big data, data life cycle, recent development, geo science

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Authors: Peter E. Egielewa

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Communication through the smartphone connects people globally. However, since the last 10 years, there has been an increasing shift from the social engagement in the family to the digital mediated communication aided by the smartphone. The traditional family communication had largely been oral and relational, which the smartphone is now digitally mediating. The study employs mixed research method of quantitative and qualitative research design and deploys questionnaire to elicit responses from both parents and children of 50 purposively selected families from five villages in Southern Nigeria that are very active with smartphone use. Based on the Theory of Family Systems, preliminary findings show that the smartphone is becoming an addiction among Nigerian family members and has shifted the dynamics of family communication from relational to digital culture. The research concludes that smartphone use affects family communication negatively and recommends the moderation of smartphone use in the family and the search for alternative platforms for family communication that minimises smartphone addiction.

Keywords: digital, distorted communication, family, Nigeria, smartphone

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Authors: Elham Farahmand, Fatimah Ibrahim, Samira Hosseini, Ivan Djordjevic, Leo. H. Koole

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Compositions of different molar ratios of polymethylmethacrylate-co-methacrylic acid (PMMA-co-MAA) were synthesized via free- radical polymerization. Polymer coated surfaces have been produced on silicon wafers. Coated samples were analyzed by atomic force microscopy (AFM). The results have shown that the roughness of the surfaces have increased by increasing the molar ratio of monomer methacrylic acid (MAA). This study reveals that the gradual increase in surface roughness is due to the fact that carboxylic functional groups have been generated by MAA segments. Such surfaces can be desirable platforms for fabrication of the biosensors for detection of the viruses and diseases.

Keywords: polymethylmethacrylate-co-methacrylic acid (PMMA-co-MAA), polymeric material, atomic force microscopy, roughness, carboxylic functional groups

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Authors: Marc Bachelet, Abhijit Kumar Chatterjee, José Manuel Avila

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Data quality is a key component of any data-driven organization. Without data quality, organizations cannot effectively make data-driven decisions, which often leads to poor business performance. Therefore, it is important for an organization to ensure that the data they use is of high quality. This is where the concept of data mesh comes in. Data mesh is an organizational and architectural decentralized approach to data management that can help organizations improve the quality of data. The concept of data mesh was first introduced in 2020. Its purpose is to decentralize data ownership, making it easier for domain experts to manage the data. This can help organizations improve data quality by reducing the reliance on centralized data teams and allowing domain experts to take charge of their data. This paper intends to discuss how a set of elements, including data mesh, are tools capable of increasing data quality. One of the key benefits of data mesh is improved metadata management. In a traditional data architecture, metadata management is typically centralized, which can lead to data silos and poor data quality. With data mesh, metadata is managed in a decentralized manner, ensuring accurate and up-to-date metadata, thereby improving data quality. Another benefit of data mesh is the clarification of roles and responsibilities. In a traditional data architecture, data teams are responsible for managing all aspects of data, which can lead to confusion and ambiguity in responsibilities. With data mesh, domain experts are responsible for managing their own data, which can help provide clarity in roles and responsibilities and improve data quality. Additionally, data mesh can also contribute to a new form of organization that is more agile and adaptable. By decentralizing data ownership, organizations can respond more quickly to changes in their business environment, which in turn can help improve overall performance by allowing better insights into business as an effect of better reports and visualization tools. Monitoring and analytics are also important aspects of data quality. With data mesh, monitoring, and analytics are decentralized, allowing domain experts to monitor and analyze their own data. This will help in identifying and addressing data quality problems in quick time, leading to improved data quality. Data culture is another major aspect of data quality. With data mesh, domain experts are encouraged to take ownership of their data, which can help create a data-driven culture within the organization. This can lead to improved data quality and better business outcomes. Finally, the paper explores the contribution of AI in the coming years. AI can help enhance data quality by automating many data-related tasks, like data cleaning and data validation. By integrating AI into data mesh, organizations can further enhance the quality of their data. The concepts mentioned above are illustrated by AEKIDEN experience feedback. AEKIDEN is an international data-driven consultancy that has successfully implemented a data mesh approach. By sharing their experience, AEKIDEN can help other organizations understand the benefits and challenges of implementing data mesh and improving data quality.

Keywords: data culture, data-driven organization, data mesh, data quality for business success

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Authors: Lam Hong Lan

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The textile industry is said to be the second-largest polluter, responsible for 92 million tonnes of waste annually. There is an urgent need to practice the circular economy to increase the use and reuse around the world. By its nature, the pre-owned fashion business is considered part of the circular economy as it helps to eliminate waste and circulate products. Second-hand clothes and accessories used to be associated with a ‘cheap image’ that carried ‘old energy’ in Vietnam. This perception has been shifted, especially amongst the younger generation. Vietnamese consumer is spending more on products and services that increase self-esteem. The same consumer is moving away from a collectivist social identity towards a ‘me, not we’ outlook as they look for a way to express their individual identity. And pre-owned fashion is one of their solutions as it values money, can create a unique personal style for the wearer and links with sustainability. The design of this study is based on the second-hand shopping motivation theory. A semi-structured online survey with 100 consumers from one pre-owned clothing community and one pre-owned e-commerce site in Vietnam. The findings show that in contrast with Vietnamese older consumers (55+yo) who, in the previous study, generally associated pre-owned fashion with ‘low-cost’, ‘cheap image’ that carried ‘old energy’, young customers (20-30 yo) were actively promoted their pre-owned fashion items to the public via outlet’s social platforms and their social media. This cultural shift comes from the impact of global and local discourse around sustainable fashion and the growth of digital platforms in the pre-owned fashion business in the last five years, which has generally supported wider interest in pre-owned fashion in Vietnam. It can be summarised in three areas: (1) global and local celebrity influencers. A number of celebrities have been photographed wearing vintage items in music videos, photoshoots or at red carpet events. (2) E-commerce and intermediaries. International e-commerce sites – e.g., Vinted, TheRealReal – and/or local apps – e.g., Re.Loved – can influence attitudes and behaviors towards pre-owned consumption. (3) Eco-awareness. The increased online coverage of climate change and environmental pollution has encouraged customers to adopt a more eco-friendly approach to their wardrobes. While sustainable biomaterials and designs are still navigating their way into sustainability, sustainable consumerism via pre-owned fashion seems to be an immediate solution to lengthen the clothes lifecycle. This study has found that young consumers are primarily seeking value for money and/or a unique personal style from pre-owned/vintage fashion while using these purchases to promote their own “eco-awareness” via their social media networks. This is a good indication for fashion designers to keep in mind in their design process and for fashion enterprises in their business model’s choice to not overproduce fashion items.

Keywords: cultural shift, pre-owned fashion, sustainable consumption, sustainable fashion.

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Authors: A. Nagaletchimee Annamalai

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The study investigated on how a small group of pre-service teachers and lecturers used social media to interact and collaborate to complete their tasks. The study is a qualitative case study that explored the lecturers’ reflections and pre-service teachers’ interviews. The lecturers were given the option to choose Facebook or any other social media as their teaching and learning platforms. However, certain guidelines based on were given to lecturers to conduct their teaching and learning activities. The findings revealed that although Facebook was a popular social networking site, it was not a preferred educational platform. Lecturers preferred to use WhatsApp, Canvas, and email. The focus group interview found positive and negative experiences of the pre-service teachers. The study suggested several pedagogical implications and importantly highlighted the need for changes in curriculum to ensure lecturers leverage the potential of technology in education.

Keywords: social media, interactions, collaboration, online learning environment

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Authors: Ji Eun Shin, Byung Ho Jung, Dong Hoon Lim

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It is almost impossible to store or analyze big data increasing exponentially with traditional technologies. Hadoop is a new technology to make that possible. R programming language is by far the most popular statistical tool for big data analysis based on distributed processing with Hadoop technology. With RHadoop that integrates R and Hadoop environment, we implemented parallel multiple regression analysis with different sizes of actual data. Experimental results showed our RHadoop system was much faster as the number of data nodes increases. We also compared the performance of our RHadoop with lm function and big lm packages available on big memory. The results showed that our RHadoop was faster than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases.

Keywords: big data, Hadoop, parallel regression analysis, R, RHadoop

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels, so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to exponential growth of computation, this paper also proposes a key data extraction method, that only extracts part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: data augmentation, mutex task generation, meta-learning, text classification.

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Authors: Sifat Rahman Ahona, Rifat Tasnim, Naima Hassan

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Data aggregation is a helpful technique for reducing the data communication overhead in wireless sensor network. One of the important tasks of data aggregation is positioning of the aggregator points. There are a lot of works done on data aggregation. But, efficient positioning of the aggregators points is not focused so much. In this paper, authors are focusing on the positioning or the placement of the aggregation points in wireless sensor network. Authors proposed an algorithm to select the aggregators positions for a scenario where aggregator nodes are more powerful than sensor nodes.

Keywords: aggregation point, data communication, data aggregation, wireless sensor network

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Authors: Paula Simões, Isabel Natário

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This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context.

Keywords: spatial data analysis, spatial econometrics, Bayesian hierarchical models, count data

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Authors: Gueidi Afef, Gharsellaoui Hamza, Ben Ahmed Samir

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This paper deals with the secret of the continual progression data that new data management solutions have been emerged: The NoSQL databases. They crossed several areas like personalization, profile management, big data in real-time, content management, catalog, view of customers, mobile applications, internet of things, digital communication and fraud detection. Nowadays, these database management systems are increasing. These systems store data very well and with the trend of big data, a new challenge’s store demands new structures and methods for managing enterprise data. The new intelligent machine in the e-learning sector, thrives on more data, so smart machines can learn more and faster. The robotics are our use case to focus on our test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of robotics; we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.

Keywords: NoSQL databases, database management systems, robotics, big data

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Authors: Rattakorn Poonsuph

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Technological innovation plays a crucial role in virtual healthcare services. A growing number of telehealth platforms are concentrating on using digital tools to improve the quality and availability of care. As a result, telehealth represents an opportunity to redesign the way health services are delivered. The research objective is to discover a new business model for digital health services and related industries to participate with telehealth solutions. The business opportunity is valuable for healthcare investors as a startup company to further investigations or implement the telehealth platform. The paper presents a digital healthcare business model and business opportunities to related industries. These include digital healthcare services extending from a traditional business model and use cases of business opportunities to related industries. Although there are enormous business opportunities, telehealth is still challenging due to the patient adaption and digital transformation process within a healthcare organization.

Keywords: telehealth, Internet hospital, HealthTech, InsurTech

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Authors: C. B. Le, V. N. Pham

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In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: Eui-Jin Kim, Hasik Lee, Su-Jin Park, Dong-Kyu Kim

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Smart-card data are expected to provide information on activity pattern as an alternative to conventional person trip surveys. The focus of this study is to propose a method for training the person trip surveys to supplement the smart-card data that does not contain the purpose of each trip. We selected only available features from smart card data such as spatiotemporal information on the trip and geographic information system (GIS) data near the stations to train the survey data. XGboost, which is state-of-the-art tree-based ensemble classifier, was used to train data from multiple sources. This classifier uses a more regularized model formalization to control the over-fitting and show very fast execution time with well-performance. The validation results showed that proposed method efficiently estimated the trip purpose. GIS data of station and duration of stay at the destination were significant features in modeling trip purpose.

Keywords: activity pattern, data fusion, smart-card, XGboost

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Authors: Martin Pumera

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Graphene materials are being widely used in electrochemistry due to their versatility and excellent properties as platforms for biosensing. Here we present current trends in the electrochemical biosensing of pesticides and other toxic compounds. We explore two fundamentally different designs, (i) using graphene and other 2-D nanomaterials as an electrochemical platform and (ii) using these nanomaterials in the laboratory on chip design, together with paramagnetic beads. More specifically: (i) We explore graphene as transducer platform with very good conductivity, large surface area, and fast heterogeneous electron transfer for the biosensing. We will present the comparison of these materials and of the immobilization techniques. (ii) We present use of the graphene in the laboratory on chip systems. Laboratory on the chip had a huge advantage due to small footprint, fast analysis times and sample handling. We will show the application of these systems for pesticide detection and detection of other toxic compounds.

Keywords: graphene, 2D nanomaterials, biosensing, chip design

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Authors: Haojie Wang, Xun Li, Rui Yin

Abstract:

In order to solve the memorization overfitting in the model-agnostic meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to an exponential growth of computation, this paper also proposes a key data extraction method that only extract part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: mutex task generation, data augmentation, meta-learning, text classification.

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Authors: Lei Wang, Jiahao Zhou

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The development of heterogeneous computing clusters provides a strong computility guarantee for large-scale workflows (e.g., scientific computing, artificial intelligence (AI), etc.). However, the tasks within large-scale workflows have also gradually become heterogeneous due to different demands on computing resources, which leads to the addition of a task resource-restricted constraint to the workflow scheduling problem on heterogeneous computing platforms. In this paper, we propose a heterogeneous constrained minimum makespan scheduling algorithm based on the idea of greedy strategy, which provides an efficient solution to the heterogeneous workflow scheduling problem in a heterogeneous platform. In this paper, we test the effectiveness of our proposed scheduling algorithm by randomly generating heterogeneous workflows with heterogeneous computing platform, and the experiments show that our method improves 15.2% over the state-of-the-art methods.

Keywords: heterogeneous computing, workflow scheduling, constrained resources, minimal makespan

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Authors: Tiera Tanksley, Amanda M. McLeroy

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The advent of technology and social media has ushered in a new era of communication, providing platforms for news dissemination and cause advocacy. However, this digital landscape has also exposed a distressing phenomenon termed "Black death," or trauma porn. This paper delves into the profound effects of repeated exposure to traumatic content on Black youth via social media, exploring the psychological impacts and potential reinforcing of stereotypes. Employing Critical Race Technology Theory (CRTT), the study sheds light on algorithmic anti-blackness and its influence on Black youth's lives and educational experiences. Through ethnographic content analysis, the research investigates common manifestations of Black death encountered online by Black adolescents. Findings unveil distressing viral videos, traumatic images, racial slurs, and hate speech, perpetuating stereotypes. However, amidst the distress, the study identifies narratives of activism and social justice on social media platforms, empowering Black youth to engage in positive change. Coping mechanisms and community support emerge as significant factors in navigating the digital landscape. The study underscores the need for comprehensive interventions and policies informed by evidence-based research. By addressing algorithmic anti-blackness and promoting digital resilience, the paper advocates for a more empathetic and inclusive online environment. Understanding coping mechanisms and community support becomes imperative for fostering mental well-being among Black adolescents navigating social media. In education, the implications are substantial. Acknowledging the impact of Black death content, educators play a pivotal role in promoting media literacy and digital resilience. Creating inclusive and safe online spaces, educators can mitigate negative effects and encourage open discussions about traumatic content. The application of CRTT in educational technology emphasizes dismantling systemic biases and promoting equity. In conclusion, this study calls for educators to be cognizant of the impact of Black death content on social media. By prioritizing media literacy, fostering digital resilience, and advocating for unbiased technologies, educators contribute to an inclusive and just educational environment for all students, irrespective of their race or background. Addressing challenges related to Black death content proactively ensures the well-being and mental health of Black adolescents, fostering an empathetic and inclusive digital space.

Keywords: algorithmic anti-Blackness, digital resilience, media literacy, traumatic content

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Authors: Igor A. Podgorny

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TurboTax AnswerXchange is a social Q/A system supporting users working on federal and state tax returns. Content quality and popularity in the AnswerXchange can be predicted with propensity models using attributes of the question and answer. Using uplift modeling, we identify features of questions and answers that can be modified during the question-asking and question-answering experience in order to optimize the AnswerXchange content quality. We demonstrate that adding details to the questions always results in increased question popularity that can be used to promote good quality content. Responding to close-ended questions assertively improve content quality in the AnswerXchange in 90% of cases. Answering knowledge questions with web links increases the likelihood of receiving a negative vote from 60% of the askers. Our findings provide a rationale for employing the uplift modeling approach for AnswerXchange operations.

Keywords: customer relationship management, human-machine interaction, text mining, uplift modeling

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Authors: Milind Chaudhari, Suhail Balasinor

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Due to massive deforestation and an ever-increasing population, the organic content of the soil is depleting at a much faster rate. Due to this, there is a big chance that the entire food production in the world will drop by 40% in the next two decades. Vertical farming can help in aiding food production by leveraging big data and cloud computing to ensure plants are grown naturally by providing the optimum nutrients sunlight by analyzing millions of data points. This paper outlines the most important parameters in vertical farming and how a combination of big data and AI helps in calculating and analyzing these millions of data points. Finally, the paper outlines how different organizations are controlling the indoor environment by leveraging big data in enhancing food quantity and quality.

Keywords: big data, IoT, vertical farming, indoor farming

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Authors: Gaye Senbag

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In English language teaching, feedback is considered as one of the main components of writing instruction. Teachers put a lot of time and effort in order to provide learners with written feedback for effective language learning. At Anadolu University School of Foreign Languages (AUSFL) students are given written feedback for their each piece of writing through online platforms such as Edmodo and Turnitin, and traditional methods. However, little is known regarding how learners value and respond to teacher-provided feedback. As the perceptions of the students remarkably affect their learning, this study examines how they perceive the effectiveness of feedback provided by the teacher. Aiming to analyse it, 30 intermediate level (B1+ CEFR level) students were given a questionnaire, which includes Likert scale questions. The results will be discussed in detail.

Keywords: feedback, perceptions, writing, English Language Teaching (ELT)

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Authors: A. Anis, W. Bekheet, A. El Hakim

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Implementing a Road Safety Management System (SMS) in a crowded developing country such as Egypt is a necessity. Beginning a sustainable SMS requires a comprehensive reliable data system for all information pertinent to road crashes. In this paper, a survey for the available data in Egypt and validating it for using in an SMS in Egypt. The research provides some missing data, and refer to the unavailable data in Egypt, looking forward to the contribution of the scientific society, the authorities, and the public in solving the problem of missing or unreliable crash data. The required data for implementing an SMS in Egypt are divided into three categories; the first is available data such as fatality and injury rates and it is proven in this research that it may be inconsistent and unreliable, the second category of data is not available, but it may be estimated, an example of estimating vehicle cost is available in this research, the third is not available and can be measured case by case such as the functional and geometric properties of a facility. Some inquiries are provided in this research for the scientific society, such as how to improve the links among stakeholders of road safety in order to obtain a consistent, non-biased, and reliable data system.

Keywords: road safety management system, road crash, road fatality, road injury

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Authors: Adriana Piatti-Crocker

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Drawing on archival documentation, digital platforms, academic journals, and reports, this research will explore the diffusion of a protest movement in Latin America. Starting in Argentina in 2015, this paper will explain how the hashtag #NiUnaMenos (“Not One Woman Less”), created to combat violence against women and girls, led to the spread of a regionwide movement. A year after its introduction, hundreds of thousands of activists mobilized on the streets of major cities in Latin America. Movements arose to protest against specific circumstances and contexts under the hashtag #NiUnaMenos, but the main goal of all of these protests was to fight against misogynist violence. Moreover, unlike previous social movements, the use of social media, such as Facebook, Instagram, Whatsapp, and Twitter, changed the depth and scope of these protests and led to an unprecedented speed in helping transmit their messages, strategies, identities, and goals.

Keywords: social protests, #NiUnaMenos ( Not one woman less), diffusion of social protests, protests and mysoginist violence

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Authors: Oualid Walid Ben Ali

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Big Data has become one of the buzzwords today. The recent explosion of digital data has led the organization, either private or public, to a new era towards a more efficient decision making. At some point, business decided to use that concept in order to learn what make their clients tick with phrases like ‘sales funnel’ analysis, ‘actionable insights’, and ‘positive business impact’. So, it stands to reason that Big Data was viewed through green (read: money) colored lenses. Somewhere along the line, however someone realized that collecting and processing data doesn’t have to be for business purpose only, but also could be used for other purposes to assist law enforcement or to improve policing or in road safety. This paper presents briefly, how Big Data have been used in the fields of policing order to improve the decision making process in the daily operation of the police. As example, we present a big-data driven system which is sued to accurately dispatch the patrol cars in a geographic environment. The system is also used to allocate, in real-time, the nearest patrol car to the location of an incident. This system has been implemented and applied in the Emirate of Abu Dhabi in the UAE.

Keywords: big data, big data analytics, patrol car allocation, dispatching, GIS, intelligent, Abu Dhabi, police, UAE

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Authors: Faiza Mahi, Fatima Debbat, Mohamed Fayçal Khelfi

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This paper proposes a control approach using Fuzzy Lo system. More precisely, the study focuses on the improvement of users service in terms of analysis and control of a transportation system their waiting times in the exchange platforms of passengers. Many studies have been developed in the literature for such problematic, and many control tools are proposed. In this paper we focus on the use of fuzzy logic technique to control the system during its evolution in order to minimize the arrival gap of connected transportation means at the exchange points of passengers. An example of illustration is worked out and the obtained results are reported. an important area of research is the modeling and simulation ordering system. We describe an approach to analysis using Fuzzy Logic. The hybrid simulator developed in toolbox Matlab consists calculation of waiting time transportation mode.

Keywords: Fuzzy logic, Hybrid system, Waiting Time, Transportation system, Control

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Authors: Hannah Yael Ephraim

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During reproductive age (between 15 and 44), women are particularly susceptible to psychiatric illness. Depression and anxiety disorders are especially common for women during reproductive age. Women of reproductive age are also at greater risk for multiple physical conditions during this time. Existing literature focuses on the impact of mental health on physical health, showing that people with anxiety and depression repeatedly show greater physical health risk among those with developing chronic medical illness. However, there is limited research on the impact physical health has on mental health in women of reproductive age, a large and vulnerable population. For this reason, the current study seeks to ask the following questions: are women of reproductive age with a diagnosis of a chronic physical condition more likely to experience symptoms of mental illness than women without a diagnosis of a chronic physical condition? Does the type of physical illness relate to signs and symptoms of depression and anxiety? A quasi-experimental research design was implemented to compare the mental health outcomes of women with the diagnosis of chronic medical conditions and women without the diagnosis of a chronic medical condition. Quantitative data was collected through an anonymous ten-minute Qualtrics survey. The survey was sent out through multiple online platforms. The sample includes two groups of women: one group with the diagnosis of a chronic medical illness, and one group without a diagnosis and/or symptoms (N = 541). Participants identify as a woman and are between the ages of 15 and 44. A comparison of women with a diagnosis of a chronic physical condition and those without a diagnosis will be conducted to explore differences in depression and anxiety symptoms between women with and without a chronic medical diagnosis. The impact race, SES, and occupation will also be addressed in relation to anxiety and/or depression in women of reproductive age. This study will further the understanding of the relationship between mental illness in women of reproductive age with chronic medical conditions. The results of this study will have implications for the integration of mental health care in women’s health centers and perhaps training of clinicians and physicians providing psychological and medical care to women of reproductive age.

Keywords: mental health, physical health, reproductive age, women

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Authors: Haytam Kasem

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The problem of quick and easy reversible attachment has become of great importance in different fields of technology. For the reason, during the last decade, a new emerging field of adhesion science has been developed. Essentially inspired by some animals and insects, which during their natural evolution have developed fantastic biological attachment systems allowing them to adhere and run on walls and ceilings of uneven surfaces. Potential applications of engineering bio-inspired solutions include climbing robots, handling systems for wafers in nanofabrication facilities, and mobile sensor platforms, to name a few. However, despite the efforts provided to apply bio-inspired patterned adhesive-surfaces to the biomedical field, they are still in the early stages compared with their conventional uses in other industries mentioned above. In fact, there are some critical issues that still need to be addressed for the wide usage of the bio-inspired patterned surfaces as advanced biomedical platforms. For example, surface durability and long-term stability of surfaces with high adhesive capacity should be improved, but also the friction and adhesion capacities of these bio-inspired microstructures when contacting rough surfaces. One of the well-known prototypes for bio-inspired attachment systems is biomimetic wall-shaped hierarchical microstructure for gecko-like attachments. Although physical background of these attachment systems is widely understood, the influence of counter-face roughness and its relationship with the friction force generated when sliding against wall-shaped hierarchical microstructure have yet to be fully analyzed and understood. To elucidate the effect of the counter-face roughness on the friction of biomimetic wall-shaped hierarchical microstructure we have replicated the isotropic topography of 12 different surfaces using replicas made of the same epoxy material. The different counter-faces were fully characterized under 3D optical profilometer to measure roughness parameters. The friction forces generated by spatula-shaped microstructure in contact with the tested counter-faces were measured on a home-made tribometer and compared with the friction forces generated by the spatulae in contact with a smooth reference. It was found that classical roughness parameters, such as average roughness Ra and others, could not be utilized to explain topography-related variation in friction force. This has led us to the development of an integrated roughness parameter obtained by combining different parameters which are the mean asperity radius of curvature (R), the asperity density (η), the deviation of asperities high (σ) and the mean asperities angle (SDQ). This new integrated parameter is capable of explaining the variation of results of friction measurements. Based on the experimental results, we developed and validated an analytical model to predict the variation of the friction force as a function of roughness parameters of the counter-face and the applied normal load, as well.

Keywords: friction, bio-mimetic micro-structure, counter-face roughness, analytical model

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Authors: Xu Du, Aparna S. Varde

Abstract:

In this research, we propose to conduct diagnostic and predictive analysis about the key factors and consequences of urban population relocation. To achieve this goal, urban simulation models extract the urban development trends as land use change patterns from a variety of data sources. The results are treated as part of urban big data with other information such as population change and economic conditions. Multiple data mining methods are deployed on this data to analyze nonlinear relationships between parameters. The result determines the driving force of population relocation with respect to urban sprawl and urban sustainability and their related parameters. Experiments so far reveal that data mining methods discover useful knowledge from the multicity urban data. This work sets the stage for developing a comprehensive urban simulation model for catering to specific questions by targeted users. It contributes towards achieving sustainability as a whole.

Keywords: data mining, environmental modeling, sustainability, urban planning

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Authors: Aref Ghafouri, Mohammad javad Mollakazemi, Farhad Asadi

Abstract:

In this paper, model order reduction method is used for approximation in linear and nonlinearity aspects in some experimental data. This method can be used for obtaining offline reduced model for approximation of experimental data and can produce and follow the data and order of system and also it can match to experimental data in some frequency ratios. In this study, the method is compared in different experimental data and influence of choosing of order of the model reduction for obtaining the best and sufficient matching condition for following the data is investigated in format of imaginary and reality part of the frequency response curve and finally the effect and important parameter of number of order reduction in nonlinear experimental data is explained further.

Keywords: frequency response, order of model reduction, frequency matching condition, nonlinear experimental data

Procedia PDF Downloads 400