Search results for: heterogeneous massive data

Authors: Munirah Stapah Salleh, Ahmad Rosly Abbas, Sazalina Zakaria, Nur Iffika Ruslan, Nurfaziera Rahim

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One of the areas that require a massive amount of energy is the airport. Hence, several airports have increased their reliance on renewable energy, specifically solar photovoltaic (PV) systems, to solve the issue. The interest regarding the installations of airport-based solar farms caught much attention. This, at the same time, helps to minimize the reliance on conventional energy sources that are fossil-based. However, many concerns were raised on the solar PV systems, especially on the effect of potential glare occurrence to the pilots during their flies. This paper will be discussing both the positive and negative impact of the large scale solar power plant on airports and aviation. Installing the large scale solar have negative impacts on airport and aviation, such as physical collision hazards, potential interference, or voltage problems with aircraft navigational and surveillance equipment as well as potential glare. On the positive side, it helps to lower environmental footprint, acquiring less energy from the utility provider, which are traditionally highly relying on other energy sources that have larger effects on the environment, and, last but not least, reduce the power supply uncertainty.

Keywords: solar photovoltaic systems, large scale solar, airport, glare effects

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Authors: Caroline Alice Rouget-Virbel, F. Dean Toste

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Heterobimetallic systems have been precedented in a wide array of bioinorganic and heterogeneous catalytic settings, in which cooperative bond-breaking and bond-forming events mediated by neighboring metal sites have been proposed but are challenging to study and characterize. Heterodinuclear transition-metal catalysis has recently emerged as a promising strategy to tackle challenging chemical transformations, including C−C and C−X couplings as well as small molecule activation. It has been shown that these reactions can traverse nontraditional mechanisms, reactivities, and selectivities when homo- and heterobimetallic systems are employed. Moreover, stoichiometric studies of transmetallation from gold complexes have demonstrated that R transfer from PPh3–Au(I)R to Cp- and Cp*-ligated group 8/9 complexes is a viable elementary step. With these considerations in mind, we hypothesized that heterobimetallic Au–M complexes could serve as a viable and tunable catalyst platform to explore mechanisms and reactivity. In this work, heterobimetallic complexes containing Au(I) centers tethered to Ir(III) and Rh(III) piano stool moieties were synthesized and characterized. Preliminary application of these complexes to a catalytic allylic arylation reaction demonstrates bimetallic cooperativity relative to their monomeric metal components.

Keywords: heterobimetallic, catalysis, gold, rhodium

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Authors: Laura C. Solzbacher, Caja Thimm

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Journalists worldwide have been confronted with a variety of ethical challenges over the last years. Because of massive changes in media technology and the public sphere, especially online journalism has trouble to uphold the fundamental values of journalism. In particular, the increasing amount of fake news and hate speech puts journalists under more and more pressure. In order to understand better how journalists judge this development and how they adapt in their daily work, a survey with journalists in Germany was carried out. 303 professional journalists participated in an online questionnaire. Results show that 65% underline that economic pressure grows and nearly the same number describe a change in the role of journalists in society. Furthermore, 61% agree that they put more time into research to secure their work against accusations of fabricating fake news. Interestingly, over 60% see a change in the role of journalists in society. The majority (85%) confirms that print journalism has to give way for online platforms and that the influence of social media for journalism grows (75%). Half of the surveyed advocate for more personalized public activism on part of journalists, such as appearance in talk shows and public talks. The results of the study will be discussed in light of the ongoing debate on ethical standards as a condition for a sustainable and trustworthy digital public sphere.

Keywords: ethics, fake news, journalism, public sphere

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Authors: David Ming Liu, Benjamin Hirsch, Bashir Aden

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Syndromic surveillance is to detect or predict disease outbreaks through the analysis of medical sources of data. Using social media data like tweets to do syndromic surveillance becomes more and more popular with the aid of open platform to collect data and the advantage of microblogging text and mobile geographic location features. In this paper, a Syndromic Surveillance Framework is presented with machine learning kernel using tweets data analytics. Influenza and the three cities Abu Dhabi, Al Ain and Dubai of United Arabic Emirates are used as the test disease and trial areas. Hospital cases data provided by the Health Authority of Abu Dhabi (HAAD) are used for the correlation purpose. In our model, Latent Dirichlet allocation (LDA) engine is adapted to do supervised learning classification and N-Fold cross validation confusion matrix are given as the simulation results with overall system recall 85.595% performance achieved.

Keywords: Syndromic surveillance, Tweets, Machine Learning, data mining, Latent Dirichlet allocation (LDA), Influenza

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Authors: Milena Trojanowska

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Disability is a phenomenon for which meanings and definitions have evolved over the decades. This became the trigger to start a project to answer the question of what disability constitutes in the course of an incurable chronic disease. The chosen research group are people living with multiple sclerosis.The contextual phase of the research was participant observation at the Polish Multiple Sclerosis Society, the largest NGO in Poland supporting people living with MS and their relatives. The research techniques used in the project are (in order of implementation): group interviews with people living with MS and their relatives, narrative interviews, asynchronous technique, participant observation during events organised for people living with MS and their relatives.The researcher is currently conducting follow-up interviews, as inaccuracies in the respondents' narratives were identified during the data analysis. Interviews and supplementary research techniques were used over the four years of the research, and the researcher also benefited from experience gained from 12 years of working with NGOs (diaries, notes). The research was carried out in Poland with the participation of people living in this country only.The research has been based on grounded theory methodology in a constructivist perspectivedeveloped by Kathy Charmaz. The goal was to follow the idea that research must be reliable, original, and useful. The aim was to construct an interpretive theory that assumes temporality and the processualityof social life. TheAtlas.ti software was used to collect research material and analyse it. It is a program from the CAQDAS(Computer-Assisted Qualitative Data Analysis Software) group.Several key factors influencing the construction of a disability identity by people living with multiple sclerosis was identified:-course of interaction with significant relatives,- the expectation of identification with disability (expressed by close relatives),- economic profitability (pension, allowances),- institutional advantages (e.g. parking card),- independence and autonomy (not equated with physical condition, but access to adapted infrastructure and resources to support daily functioning),- the way a person with MS construes the meaning of disability,- physical and mental state,- medical diagnosis of illness.In addition, it has been shown that making an assumption about the experience of disability in the course of MS is a form of cognitive reductionism leading to further phenomenon such as: the expectation of the person with MS to construct a social identity as a person with a disability (e.g. giving up work), the occurrence of institutional inequalities. It can also be a determinant of the choice of a life strategy that limits social and individual functioning, even if this necessity is not influenced by the person's physical or psychological condition.The results of the research are important for the development of knowledge about the phenomenon of disability. It indicates the contextuality and complexity of the disability phenomenon, which in the light of the research is a set of different phenomenon of heterogeneous nature and multifaceted causality. This knowledge can also be useful for institutions and organisations in the non-governmental sector supporting people with disabilities and people living with multiple sclerosis.

Keywords: disability, multiple sclerosis, grounded theory, poland

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Authors: Yuyun Wabula, B. J. Dewancker

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In the past decade, the social networking app has been growing very rapidly. Geolocation data is one of the important features of social media that can attach the user's location coordinate in the real world. This paper proposes the use of geolocation data from the Twitter social media application to gain knowledge about urban dynamics, especially on human mobility behavior. This paper aims to explore the relation between geolocation Twitter with the existence of people in the urban area. Firstly, the study will analyze the spread of people in the particular area, within the city using Twitter social media data. Secondly, we then match and categorize the existing place based on the same individuals visiting. Then, we combine the Twitter data from the tracking result and the questionnaire data to catch the Twitter user profile. To do that, we used the distribution frequency analysis to learn the visitors’ percentage. To validate the hypothesis, we compare it with the local population statistic data and land use mapping released by the city planning department of Makassar local government. The results show that there is the correlation between Twitter geolocation and questionnaire data. Thus, integration the Twitter data and survey data can reveal the profile of the social media users.

Keywords: geolocation, Twitter, distribution analysis, human mobility

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Authors: Rezaei Hachesu Peyman, Oliyaee Azadeh, Salahzadeh Zahra, Alizadeh Somayyeh, Safaei Naser

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Coronary Artery Disease (CAD) is one major cause of disability in adults and one main cause of death in developed. In this study, data mining techniques including Decision Trees, Artificial neural networks (ANNs), and Support Vector Machine (SVM) analyze CAD data. Data of 4948 patients who had suffered from heart diseases were included in the analysis. CAD is the target variable, and 24 inputs or predictor variables are used for the classification. The performance of these techniques is compared in terms of sensitivity, specificity, and accuracy. The most significant factor influencing CAD is chest pain. Elderly males (age > 53) have a high probability to be diagnosed with CAD. SVM algorithm is the most useful way for evaluation and prediction of CAD patients as compared to non-CAD ones. Application of data mining techniques in analyzing coronary artery diseases is a good method for investigating the existing relationships between variables.

Keywords: classification, coronary artery disease, data-mining, knowledge discovery, extract

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Authors: Sudipto Shanker Dasgupta

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One of the largest mining companies wanted to look at health analytics for their driverless trucks. These trucks were the key to their supply chain logistics. The automated trucks had multi-level sub-assemblies which would send out sensor information. The use case that was worked on was to capture the sensor signal from the truck subcomponents and analyze the health of the trucks from repair and replacement purview. Open source software was used to stream the data into a clustered Hadoop setup in Amazon Web Services cloud and Apache Spark SQL was used to analyze the data. All of this was achieved through a 10 node amazon 32 core, 64 GB RAM setup real-time analytics was achieved on ‘300 million records’. To check the scalability of the system, the cluster was increased to 100 node setup. This talk will highlight how Open Source software was used to achieve the above use case and the insights on the high data throughput on a cloud set up.

Keywords: streaming analytics, data science, big data, Hadoop, high throughput, sensor data

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Authors: Sheldon Liu, Gordon Wang, Lei Liu, Xuefeng Liu

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Anomaly detection, also referred to as one-class classification, plays a crucial role in identifying product images that deviate from the expected distribution. This study introduces Data-centric Anomaly Detection with Diffusion Models (DCADDM), presenting a systematic strategy for data collection and further diversifying the data with image generation via diffusion models. The algorithm addresses data collection challenges in real-world scenarios and points toward data augmentation with the integration of generative AI capabilities. The paper explores the generation of normal images using diffusion models. The experiments demonstrate that with 30% of the original normal image size, modeling in an unsupervised setting with state-of-the-art approaches can achieve equivalent performances. With the addition of generated images via diffusion models (10% equivalence of the original dataset size), the proposed algorithm achieves better or equivalent anomaly localization performance.

Keywords: diffusion models, anomaly detection, data-centric, generative AI

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Authors: Riham Barbar

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This paper studies the dynamic effects of charitable giving in a Ramsey model à la Becker and Foias (1994), such that heterogeneity is reduced to two types of agents: rich and poor. It is assumed that rich show a great concern for poor and enjoy giving. The introduction of charitable giving in this paper is inspired from the notion of Zakat (borrowed from the Islamic Economics) and is defined according to the warm-glow of Andreoni (1990). In this framework, we prove the existence of a steady state where only the patient agent holds capital. Furthermore, we show that local indetermincay appears. While moderate values of charitable-giving elasticity makes the appearance of endogenous fluctuations due to self-fulfilling expectations more likely, high values of this elasticity stabilizes endogenous fluctuations, by narrowing down the range of parameter values compatible with local indeterminacy and may rule out expectations-driven fluctuations if it exceeds certain threshold. Finally, cycles of period two emerge. However, charitable-giving makes it less likely for these cycles to emerge.

Keywords: charitable giving, warm-glow, bifurcations, heterogeneous agents, indeterminacy, self-fulfilling expectations, endogenous fluctuations

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Authors: Carolyn Hatch, Laurent Simon

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Urban mobility and the transportation industry are undergoing a transformation, shifting from an auto production-consumption model that has dominated since the early 20th century towards new forms of personal and shared multi-modality [1]. This is shaped by key forces such as climate change, which has induced a shift in production and consumption patterns and efforts to decarbonize and improve transport services through, for instance, the integration of vehicle automation, electrification and mobility sharing [2]. Advanced innovation practices and platforms for experimentation and validation of new mobility products and services that are increasingly complex and multi-stakeholder-oriented are shaping this new world of mobility. Transportation hubs – such as airports - are emblematic of these disruptive forces playing out in the mobility industry. Airports are emerging as the core of innovation ecosystems on and around contemporary mobility issues, and increasingly recognized as complex public/private nodes operating in many societal dimensions [3,4]. These include urban development, sustainability transitions, digital experimentation, customer experience, infrastructure development and data exploitation (for instance, airports generate massive and often untapped data flows, with significant potential for use, commercialization and social benefit). Yet airport innovation practices have not been well documented in the innovation literature. This paper addresses this gap by proposing a model of airport innovation that aims to equip airport stakeholders to respond to these new and complex innovation needs in practice. The methodology involves: 1 – a literature review bringing together key research and theory on airport innovation management, open innovation and innovation ecosystems in order to evaluate airport practices through an innovation lens; 2 – an international benchmarking of leading airports and their innovation practices, including such examples as Aéroports de Paris, Schipol in Amsterdam, Changi in Singapore, and others; and 3 – semi-structured interviews with airport managers on key aspects of organizational practice, facilitated through a close partnership with the Airport Council International (ACI), a major stakeholder in this research project. Preliminary results find that the most successful airports are those that have shifted to a multi-stakeholder, platform ecosystem model of innovation. The recent entrance of new actors in airports (Google, Amazon, Accor, Vinci, Airbnb and others) have forced the opening of organizational boundaries to share and exchange knowledge with a broader set of ecosystem players. This has also led to new forms of governance and intermediation by airport actors to connect complex, highly distributed knowledge, along with new kinds of inter-organizational collaboration, co-creation and collective ideation processes. Leading airports in the case study have demonstrated a unique capacity to force traditionally siloed activities to “think together”, “explore together” and “act together”, to share data, contribute expertise and pioneer new governance approaches and collaborative practices. In so doing, they have successfully integrated these many disruptive change pathways and forced their implementation and coordination towards innovative mobility outcomes, with positive societal, environmental and economic impacts. This research has implications for: 1 - innovation theory, 2 - urban and transport policy, and 3 - organizational practice - within the mobility industry and across the economy.

Keywords: airport management, ecosystem, innovation, mobility, platform, transport hubs

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Authors: Elizabeta Krstić Vukelja

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Digitization of personal data is a consequence of the development of information and communication technologies that create a new work environment with many advantages and challenges, but also potential threats to privacy and personal data protection. Regulation (EU) 2016/679 of the European Parliament and of the Council is becoming a law and obligation that should address the issues of personal data protection and information security. The existence of the Regulation leads to the conclusion that national legislation in the field of virtual environment, protection of the rights of EU citizens and processing of their personal data is insufficiently effective. In the health system, special emphasis is placed on the processing of special categories of personal data, such as health data. The healthcare industry is recognized as a particularly sensitive area in which a large amount of medical data is processed, the digitization of which enables quick access and quick identification of the health insured. The protection of the individual requires quality IT solutions that guarantee the technical protection of personal categories. However, the real problems are the technical and human nature and the spatial limitations of the application of the Regulation. Some conclusions will be drawn by analyzing the implementation of the basic principles of the Regulation on the example of the Croatian health care system and comparing it with similar activities in other EU member states.

Keywords: regulation, healthcare system, personal dana protection, quality data assurance

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Authors: Nagi Mekhiel

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Many applications use vector operations by applying single instruction to multiple data that map to different locations in conventional memory. Transferring data from memory is limited by access latency and bandwidth affecting the performance gain of vector processing. We present a memory system that makes all of its content available to processors in time so that processors need not to access the memory, we force each location to be available to all processors at a specific time. The data move in different orbits to become available to other processors in higher orbits at different time. We use this memory to apply parallel vector operations to data streams at first orbit level. Data processed in the first level move to upper orbit one data element at a time, allowing a processor in that orbit to apply another vector operation to deal with serial code limitations inherited in all parallel applications and interleaved it with lower level vector operations.

Keywords: Memory Organization, Parallel Processors, Serial Code, Vector Processing

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Authors: David Percy

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Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

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Authors: Tammy Wee, Detlev Remy, Arif Perdana

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In the recent years, data analytics has become the buzzword in the hospitality industry. The hospitality industry is another example of a data-rich industry that has yet fully benefited from the insights of data analytics. Effective use of data analytics can change how hotels operate, market and position themselves competitively in the hospitality industry. However, at the moment, the data obtained by individual hotels remain under-utilized. This research is a preliminary research on data analytics in the hospitality industry, using an in-depth face-to-face interview on one hotel as a start to a multi-level research. The main case study of this research, hotel A, is a chain brand of international hotel that has been systematically gathering and collecting data on its own customer for the past five years. The data collection points begin from the moment a guest book a room until the guest leave the hotel premises, which includes room reservation, spa booking, and catering. Although hotel A has been gathering data intelligence on its customer for some time, they have yet utilized the data to its fullest potential, and they are aware of their limitation as well as the potential of data analytics. Currently, the utilization of data analytics in hotel A is limited in the area of customer service improvement, namely to enhance the personalization of service for each individual customer. Hotel A is able to utilize the data to improve and enhance their service which in turn, encourage repeated customers. According to hotel A, 50% of their guests returned to their hotel, and 70% extended nights because of the personalized service. Apart from using the data analytics for enhancing customer service, hotel A also uses the data in marketing. Hotel A uses the data analytics to predict or forecast the change in consumer behavior and demand, by tracking their guest’s booking preference, payment preference and demand shift between properties. However, hotel A admitted that the data they have been collecting was not fully utilized due to two challenges. The first challenge of using data analytics in hotel A is the data is not clean. At the moment, the data collection of one guest profile is meaningful only for one department in the hotel but meaningless for another department. Cleaning up the data and getting standards correctly for usage by different departments are some of the main concerns of hotel A. The second challenge of using data analytics in hotel A is the non-integral internal system. At the moment, the internal system used by hotel A do not integrate with each other well, limiting the ability to collect data systematically. Hotel A is considering another system to replace the current one for more comprehensive data collection. Hotel proprietors recognized the potential of data analytics as reported in this research, however, the current challenges of implementing a system to collect data come with a cost. This research has identified the current utilization of data analytics and the challenges faced when it comes to implementing data analytics.

Keywords: data analytics, hospitality industry, customer relationship management, hotel marketing

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Authors: Djelloul Benatiallah, Ali Benatiallah, Abdelkader Harouz

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Geographic Information Systems (GIS) include various methods and computer techniques to model, capture digitally, store, manage, view and analyze. Geographic information systems have the characteristic to appeal to many scientific and technical field, and many methods. In this article we will present a complete and operational geographic information system, following the theoretical principles of data management and adapting to spatial data, especially data concerning the monitoring of drinking water supply wells (DWS) Adrar region. The expected results of this system are firstly an offer consulting standard features, updating and editing beneficiaries and geographical data, on the other hand, provides specific functionality contractors entered data, calculations parameterized and statistics.

Keywords: GIS, DWS, drilling, Adrar

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Authors: S. Habte, K. Ouazzane, P. Patel, S. Patel

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The dynamic and highly competitive nature of the consumer electronics retail industry means that businesses in this industry are experiencing different decision making challenges in relation to pricing, inventory control, consumer satisfaction and product offerings. To overcome the challenges facing retailers and create opportunities, we propose a generic data warehousing solution which can be applied to a wide range of consumer electronics retailers with a minimum configuration. The solution includes a dimensional data model, a template SQL script, a high level architectural descriptions, ETL tool developed using C#, a set of APIs, and data access tools. It has been successfully applied by ASK Outlets Ltd UK resulting in improved productivity and enhanced sales growth.

Keywords: consumer electronics, data warehousing, dimensional data model, generic, retail industry

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Authors: Lei Ren, Michael Hartnett, Stephen Nash

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The abundant measured surface current from HF radar system in coastal area is assimilated into model to improve the modeling forecasting ability. A simple sequential data assimilation scheme, Direct Insertion (DI), is applied to update model forecast states. The influence of Direct Insertion data assimilation over time is analyzed at one reference point. Vector maps of surface current from models are compared with HF radar measurements. Root-Mean-Squared-Error (RMSE) between modeling results and HF radar measurements is calculated during the last four days with no data assimilation.

Keywords: data assimilation, CODAR, HF radar, surface current, direct insertion

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Authors: M. A. Hasan, M. R. Islam, R. A. Tarefder

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This study investigates how the site specific traffic data differs from the Mechanistic Empirical Pavement Design Software default values. Two Weigh-in-Motion (WIM) stations were installed in Interstate-40 (I-40) and Interstate-25 (I-25) to developed site specific data. A computer program named WIM Data Analysis Software (WIMDAS) was developed using Microsoft C-Sharp (.Net) for quality checking and processing of raw WIM data. A complete year data from November 2013 to October 2014 was analyzed using the developed WIM Data Analysis Program. After that, the vehicle class distribution, directional distribution, lane distribution, monthly adjustment factor, hourly distribution, axle load spectra, average number of axle per vehicle, axle spacing, lateral wander distribution, and wheelbase distribution were calculated. Then a comparative study was done between measured data and AASHTOWare default values. It was found that the measured general traffic inputs for I-40 and I-25 significantly differ from the default values.

Keywords: AASHTOWare, traffic, weigh-in-motion, axle load distribution

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Authors: Angga Hidayah Ramadhan, Luciana Andrawina, M. Azani Hasibuan

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Data will be as a core of the decision if it has a good treatment or process, which is process that data into information, and information into knowledge to make a wisdom or decision. Today, many companies have not realize it include XYZ University Admission Directorate as executor of National Admission called Seleksi Masuk Bersama (SMB) that during the time, the workers only uses their feeling to make a decision. Whereas if it done, then that company can analyze the data to make a right decision to get a pin sales from student candidate or registrant that follow SMB as many as possible. Therefore, needs Knowledge Management System (KMS) with Geographic Information System (GIS) use 5C4C that can process that company data becomes more useful and can help make decisions. This information system can process data into information based on the pin sold data with 5C (Contextualized, Categorize, Calculation, Correction, Condensed) and convert information into knowledge with 4C (Comparing, Consequence, Connection, Conversation) that has been several steps until these data can be useful to make easier to take a decision or wisdom, resolve problems, communicate, and quicker to learn to the employees have not experience and also for ease of viewing/visualization based on spatial data that equipped with GIS functionality that can be used to indicate events in each province with indicator that facilitate in this system. The system also have a function to save the tacit on the system then to be proceed into explicit in expert system based on the problems that will be found from the consequences of information. With the system each team can make a decision with same ways, structured, and the important is based on the actual event/data.

Keywords: 5C4C, data, information, knowledge

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Authors: Bouafia-Chergui Souâd, Chabani Malika, Bensmaili Aicha

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Water treatment and especially, medicament pollutants are nowadays important problems. Degradation of oxytetracycline was carried out using combined process of low-frequency ultrasound (US), ultraviolet irradiation and a catalyst. The effectiveness of the coupled processes has been evaluated by studying the effects of various operating parameters including initial OTC concentration, solution pH and catalyst mass. For the photolysis process, the monochromatic ultraviolet light wavelength utilized was 365 nm. The sonolysis experiments were performed with ultrasound at a frequency of 40 kHz. The heterogeneous photocatalysis was studied in the presence of TiO2. The processes were employed individually, and simultaneously to examine the details of the processes and to investigate the contribution of each process. Low UV intensity (12W), low pH and high mass of TiO2 conditions enhanced the sono-photocatalytic degradation of OTC. The results showed that the individual contribution sonochemical and photochemical reactions are very low, however, their coupling increases the degradation rate of 8 times compared to photolysis and 2 times compared to sonolysis. There is a synergistic effect between the two modes of radiation, UV and U.S. leading to 82.04% degradation yield. An application of these combined processes on the treatment of a real pharmaceutical wastewater was examined.

Keywords: sonolysis, photocatalysis, combined process, antibiotic

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Authors: C. Mekala, Indumathi M. Nambi

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Nitrogen pollution in groundwater arising from wastewater and fertilizer application through vadose zone is a major problem and it causes a prime risk to groundwater based drinking water supplies. Nitrogenous compounds namely ammonium, nitrate and nitrite fate and transport in soil subsurface were studied experimentally. The major process like sorption, leaching, biotransformation involving microbial growth kinetics, and biological clogging due to biomass growth were assessed and modeled with advection-dispersion reaction equations for ammonium, nitrate and acetate in a saturated, heterogeneous soil medium. The transport process was coupled with freundlich sorption and monod inhibition kinetics for immobile bacteria and permeability reduction due to biomass growth will be verified and validated with the numerical model. This proposed mathematical model will be very helpful in the development of a management model for a sustainable and safe wastewater reuse strategies such as irrigation and groundwater recharge.

Keywords: nitrogen species transport, transformation, biological clogging, biokinetic parameters, contaminant transport model, saturated soil

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Authors: K. Abdul Halim, E. L. Yong

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Palm oil industry is a major revenue earner in Malaysia, despite the growth of the industry is synonymous with a massive production of agro-industrial wastewater. Through the oil extraction processes, palm oil mill effluent (POME) contributes to the largest liquid wastes generated. Due to the high amount of organic compound, POME can cause inland water pollution if discharged untreated into the water course as well as affect the aquatic ecosystem. For more than 20 years, Malaysia adopted the conventional biological treatment known as lagoon system that apply biological treatment. Besides having difficulties in complying with the standard, a large build up area is needed and retention time is higher. Although anaerobic digester is more favorable, this process comes along with enormous volumes of sludge and methane gas, demanding attention from the mill operators. In order to reduce the sludge production, denitrifiers are to be removed first. Sulfate reducing bacteria has shown the capability to inhibit the growth of methanogens. This is expected to substantially reduce both the sludge and methane production in anaerobic digesters. In this paper, the effectiveness of sulfate reducing bacteria in minimizing sludge and methane will be examined.

Keywords: methane reduction, palm oil mill effluent, sludge minimization, sulfate reducing bacteria, sulfate reduction

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Authors: Shravani Itkelwar, Deepak Tewari, Bhaskar Natarajan

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The energy consumption data plays a vital role in energy efficiency policy design, implementation, and impact assessment. Any demand-side energy management intervention's success relies on the availability of accurate, comprehensive, granular, and up-to-date data on energy consumption. The Building sector, including residential and commercial, is one of the largest consumers of energy in India after the Industrial sector. With economic growth and increasing urbanization, the building sector is projected to grow at an unprecedented rate, resulting in a 5.6 times escalation in energy consumption till 2047 compared to 2017. Therefore, energy efficiency interventions will play a vital role in decoupling the floor area growth and associated energy demand, thereby increasing the need for robust data. In India, multiple institutions are involved in the collection and dissemination of data. This paper focuses on energy consumption data management in the building sector in India for both residential and commercial segments. It evaluates the robustness of data available through administrative and survey routes to estimate the key performance indicators and identify critical data gaps for making informed decisions. The paper explores several issues in the data, such as lack of comprehensiveness, non-availability of disaggregated data, the discrepancy in different data sources, inconsistent building categorization, and others. The identified data gaps are justified with appropriate examples. Moreover, the paper prioritizes required data in order of relevance to policymaking and groups it into "available," "easy to get," and "hard to get" categories. The paper concludes with recommendations to address the data gaps by leveraging digital initiatives, strengthening institutional capacity, institutionalizing exclusive building energy surveys, and standardization of building categorization, among others, to strengthen the management of building sector energy consumption data.

Keywords: energy data, energy policy, energy efficiency, buildings

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Authors: Elizabeth Latham

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Billions of people worldwide profess some feeling of psychological or spiritual connection with the divine. The majority of them attribute this personal connection to the God of the Christian Bible. The objective of this research was to discover what could be known about the exact social nature of these relationships and to see if they mimic the interactions recounted in the bible; if a worldwide majority believes that the Christian Bible is a true account of God’s interactions with mankind, it is reasonable to assume that the interactions between God and the aforementioned people would be similar to the ones in the bible. This analysis required the employment of an unusual method of biblical analysis: Game Theory. Because the research focused on documented social interaction between God and man in scripture, it was important to go beyond text-analysis methods. We used stories from the New Revised Standard Version of the bible to set up “games” using economics-style matrices featuring each player’s motivations and possible courses of action, modeled after interactions in the Old and New Testaments between the Judeo-Christian God and some mortal person. We examined all relevant interactions for the objectives held by each party and their strategies for obtaining them. These findings were then compared to similar “games” created based on interviews with people subscribing to different levels of Christianity who ranged from barely-practicing to clergymen. The range was broad so as to look for a correlation between scriptural knowledge and game-similarity to the bible. Each interview described a personal experience someone believed they had with God and matrices were developed to describe each one as social interaction: a “game” to be analyzed quantitively. The data showed that in most cases, the social features of God-man interactions in the modern lives of people were like those present in the “games” between God and man in the bible. This similarity was referred to in the study as “biblical faith” and it alone was a fascinating finding with many implications. The even more notable finding, however, was that the amount of game-similarity present did not correlate with the amount of scriptural knowledge. Each participant was also surveyed on family background, political stances, general education, scriptural knowledge, and those who had biblical faith were not necessarily the ones that knew the bible best. Instead, there was a high degree of correlation between biblical faith and family religious observance. It seems that to have a biblical psychological relationship with God, it is more important to have a religious family than to have studied scripture, a surprising insight with massive implications on the practice and preservation of religion.

Keywords: bible, Christianity, game theory, social psychology

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Authors: Silvina Caíno-Lores, Jesús Carretero

Abstract:

Large scale computing infrastructures have been widely developed with the core objective of providing a suitable platform for high-performance and high-throughput computing. These systems are designed to support resource-intensive and complex applications, which can be found in many scientific and industrial areas. Currently, large scale data-intensive applications are hindered by the high latencies that result from the access to vastly distributed data. Recent works have suggested that improving data locality is key to move towards exascale infrastructures efficiently, as solutions to this problem aim to reduce the bandwidth consumed in data transfers, and the overheads that arise from them. There are several techniques that attempt to move computations closer to the data. In this survey we analyse the different mechanisms that have been proposed to provide data locality for large scale high-performance and high-throughput systems. This survey intends to assist scientific computing community in understanding the various technical aspects and strategies that have been reported in recent literature regarding data locality. As a result, we present an overview of locality-oriented techniques, which are grouped in four main categories: application development, task scheduling, in-memory computing and storage platforms. Finally, the authors include a discussion on future research lines and synergies among the former techniques.

Keywords: data locality, data-centric computing, large scale infrastructures, cloud computing

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Authors: Harold P. Villota, Alejandro Osorio B.

Abstract:

The energy meteorology is an area for study energy complementarity and the use of renewable sources in interconnected systems. Due to diversify the energy matrix in Colombia with wind sources, is necessary to know the data bases about this one. However, the time series given by 260 automatic weather stations have empty, and no apply data, so the purpose is to fill the time series selecting two years to characterize, impute and use like base to complete the data between 2005 and 2020.

Keywords: complementarity, wind speed, renewable, colombia, characteri, characterization, imputation

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Authors: Sijia Hu

Abstract:

Tensile creep tests were performed along the normal direction on the as-solutioned (AS) and as-aged (AA) samples of a commercial ZK60 alloy in this work. The results showed that the AA sample obtained a stronger 0.2% proof stress but a poorer creep resistance in comparison to the AS sample. It was revealed that the creep deformation in the AS sample was attributed to basal slip and twinning, while the creep behavior of the AA sample was controlled by basal slip, pyramidal slip and twinning. Besides, the reasons for the poorer creep resistance of the AA sample were unveiled. Pyramidal dislocations towards various moving directions were found to accelerate the creep deformation, and basal dislocations kinking at twin boundaries were found to induce heavy stress concentration. Furthermore, massive dynamic precipitates, including beta 1 prime and beta 2 prime types, were formed in the AS sample during the creep exposure, contributing to the superior creep resistance. But in the AA sample, plentiful beta 1 prime and beta 2 prime phases generated during the prior peak-aging treatment dissolved into the matrix fast and only beta 1 prime re-precipitated during the creep process. With the decreased area fractions of precipitates, the age-hardening effect slacked off and failed to enhance the creep resistance.

Keywords: Mg alloy, creep, precipitation, microstructure

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Authors: Hyun-Woo Cho

Abstract:

Unexpected events may occur with serious impacts on industrial process. This work utilizes a data representation technique to model and to analyze process data pattern for the purpose of diagnosis. In this work, the use of triangular representation of process data is evaluated using simulation process. Furthermore, the effect of using different pre-treatment techniques based on such as linear or nonlinear reduced spaces was compared. This work extracted the fault pattern in the reduced space, not in the original data space. The results have shown that the non-linear technique based diagnosis method produced more reliable results and outperforms linear method.

Keywords: process monitoring, data analysis, pattern modeling, fault, nonlinear techniques

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Authors: Radyah Ivan, Khanferyan Roman

Abstract:

Milk and dairy products are an important component of a balanced diet. Dairy products represent a heterogeneous food group of solid, semi-solid and liquid, fermented or non-fermented foods, each differing in nutrients such as fat and micronutrient content. Deficiency of milk and dairy products contributes a impact on the main health parameters of the various age groups of the population. The goal of this study was to analyze of the frequency of the consumption of milk and various groups of dairy products by students and its association with their body mass index (BMI), body composition and other physiological parameters. 388 full-time students of the Medical Institute of RUDN University (185 male and 203 female, average age was 20.4+2.2 and 21.9+1.7 y.o., respectively) took part in the cross-sectional study. Anthropometric measurements, estimation of BMI and body composition were analyzed by bioelectrical impedance analysis. The frequency of consumption of the milk and various groups of dairy products was studied using a modified questionnaire on the frequency of consumption of products. Due to the questionnaire data on the frequency of consumption of the diary products, it have been demonstrated that only 11% of respondents consume milk daily, 5% - cottage cheese, 4% and 1% - fermented natural and with fillers milk products, respectively, hard cheese -4%. The study demonstrated that about 16% of the respondents did not consume milk at all over the past month, about one third - cottage cheese, 22% - natural sour-milk products and 18% - sour-milk products with various fillers. hard cheeses and pickled cheeses didn’t consume 9% and 26% of respondents, respectively. We demonstrated the gender differences in the characteristics of consumer preferences were revealed. Thus female students are less likely to use cream, sour cream, soft cheese, milk comparing to male students. Among female students the prevalence of persons with overweight was higher (25%) than among male students (19%). A modest inverse relationship was demonstrated between daily milk intake, BMI, body composition parameters and diary products consumption (r=-0.61 and r=-0.65). The study showed daily insufficient milk and dairy products consumption by students and due to this it have been demonstrated the relationship between the low and rare consumption of diary products and main parameters of indicators of physical activity and health indicators.

Keywords: frequency of consumption, milk, dairy products, physical development, nutrition, body mass index.

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