Search results for: data block

Authors: Seung-Min Yang, Seong-Han Park, Sang-Hoon Lee, Seung-Wan Yoo, Chan-Soo Kim, Nong-Moon Hwang

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The theory of charged nanoparticles suggested that in many Chemical Vapour Depositions (CVD) processes, Charged Nanoparticles (CNPs) are generated in the gas-phase and become a building block of thin films and nanowires. Recently, the nanoparticle-based crystallization has become a big issue since the growth of nanorods or crystals by the building block of nanoparticles was directly observed by transmission electron microscopy observations in the liquid cell. In an effort to confirm charged gas-phase nuclei, that might be generated under conventional processing conditions of thin films and nanowires during CVD, we performed an in-situ measurement using differential mobility analyser and particle beam mass spectrometer. The size distribution and number density of CNPs were affected by process parameters such as precursor flow rate and working temperature. It was shown that many films and nanostructures, which have been believed to grow by individual atoms or molecules, actually grow by the building blocks of such charged nuclei. The electrostatic interaction between CNPs and the growing surface induces the self-assembly into films and nanowires. In addition, the charge-enhanced atomic diffusion makes CNPs liquid-like quasi solid. As a result, CNPs tend to land epitaxial on the growing surface, which results in the growth of single crystalline nanowires with a smooth surface.

Keywords: chemical vapour deposition, charged nanoparticle, electrostatic force, nanostructure evolution, differential mobility analyser, particle beam mass spectrometer

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Authors: Kamal KC, Zhendong Yin, Dasen Li, Zhilu Wu

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Intra-plant disease classification based on leaf images is a challenging computer vision task due to similarities in texture, color, and shape of leaves with a slight variation of leaf spot; and external environmental changes such as lighting and background noises. Deep convolutional neural network (DCNN) has proven to be an effective tool for binary classification. In this paper, two methods for binary classification of diseased plant leaves using DCNN are presented; model created from scratch and transfer learning. Our main contribution is a thorough evaluation of 4 networks created from scratch and transfer learning of 5 pre-trained models. Training and testing of these models were performed on a plant leaf images dataset belonging to 16 distinct classes, containing a total of 22,265 images from 8 different plants, consisting of a pair of healthy and diseased leaves. We introduce a deep CNN model, Optimized MobileNet. This model with depthwise separable CNN as a building block attained an average test accuracy of 99.77%. We also present a fine-tuning method by introducing the concept of a convolutional block, which is a collection of different deep neural layers. Fine-tuned models proved to be efficient in terms of accuracy and computational cost. Fine-tuned MobileNet achieved an average test accuracy of 99.89% on 8 pairs of [healthy, diseased] leaf ImageSet.

Keywords: deep convolution neural network, depthwise separable convolution, fine-grained classification, MobileNet, plant disease, transfer learning

Procedia PDF Downloads 158

Authors: Zhang Yuqi

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Service facilities are the boosters for the cultivation and development of innovative functions in innovative cluster areas. At the same time, reasonable service facilities planning can better link the internal functional blocks. This paper takes Zhejiang University Zijin Co-Maker Town as the research object, based on the combination of network data mining and field research and verification, combined with the needs of its internal innovative groups. It studies the distribution characteristics and existing problems of service facilities and then proposes a targeted planning suggestion. The main conclusions are as follows: (1) From the perspective of view, the town is rich in general life-supporting services, but lacking of provision targeted and distinctive service facilities for innovative groups; (2) From the perspective of scale structure, small-scale street shops are the main business form, lack of large-scale service center; (3) From the perspective of spatial structure, service facilities layout of each functional block is too fragile to fit the characteristics of 2aggregation- distribution' of innovation and entrepreneurial activities; (4) The goal of optimizing service facilities planning should be guided for fostering function of innovation and entrepreneurship and meet the actual needs of the innovation and entrepreneurial groups.

Keywords: the cultivation of innovative function, Zhejiang University Zijin Co-Maker Town, service facilities, network data mining, space optimization advice

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Authors: Julio Albuja, David Zaldumbide

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Data analysis is an important step before taking a decision about money. The aim of this work is to analyze the factors that influence the final price of the houses through data mining algorithms. To our best knowledge, previous work was researched just to compare results. Furthermore, before using the data of the data set, the Z-Transformation were used to standardize the data in the same range. Hence, the data was classified into two groups to visualize them in a readability format. A decision tree was built, and graphical data is displayed where clearly is easy to see the results and the factors' influence in these graphics. The definitions of these methods are described, as well as the descriptions of the results. Finally, conclusions and recommendations are presented related to the released results that our research showed making it easier to apply these algorithms using a customized data set.

Keywords: algorithms, data, decision tree, transformation

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Authors: Mengdi Zhang, Zhenji Gao, Ning Kang, Rongmei Liu

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Management and application of geological big data is an important part of China's national big data strategy. With the implementation of a national big data strategy, geological big data management becomes more and more critical. At present, there are still a lot of technology barriers as well as cognition chaos in many aspects of geological big data management and application, such as data sharing, intellectual property protection, and application technology. Therefore, it’s a key task to make better use of new technologies for deeper delving and wider application of geological big data. In this paper, we briefly introduce the basic principle of blockchain technology at the beginning and then make an analysis of the application dilemma of geological data. Based on the current analysis, we bring forward some feasible patterns and scenarios for the blockchain application in geological big data and put forward serval suggestions for future work in geological big data management.

Keywords: blockchain, intellectual property protection, geological data, big data management

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Authors: Zsolt Szabolcsi

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Political communication in Hungary has undergone a significant change in the 2010s. One element of the transformation is the Flashnews. This media product was launched in March 2015 and since then 40-50 blocks are broadcasted, daily, on 5 channels. Flashnews blocks are condensed news sessions, containing the summary of political narratives. It starts with the introduction of the narrator, then, usually four news topics are presented and, finally, the narrator concludes the block. The block lasts only one minute and, therefore, it provides a blink session into the main narratives of political communication at the time. Beyond its rapid pace, what makes its avoidance difficult is that these blocks are always in the first position in the commercial break of a non-political media product. Although it is only one minute long, its significance is high. The content of the Flashnews reflects the main governmental narratives and, therefore, the Flashnews is part of the agenda-setting capacity of political communication. It reaches media consumers who have limited knowledge and interest in politics, and their use of media products is not politically related. For this audience, the Flashnews pops up in the same way as commercials. Due to its structure and appearance, the impact of Flashnews seems to be similar to commercials, imbedded into the break of media products. It activates existing knowledge constructs, builds up associational links and maintains their presence in a way that the recipient is not aware of the phenomenon. The research aims to examine the extent to which the Flashnews and the main news narratives are identical in their content. This aim is realized with the content analysis of the two news products by examining the Flashnews and the evening news during main sport events from 2016 to 2018. The initial hypothesis of the research is that Flashnews is a contribution to the news management technique for an effective articulation of political narratives in public service media channels.

Keywords: flashnews, political communication, political marketing, news management

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Authors: Tamanna Jethava, Rahul Joshi

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Frequent Item sets play an essential role in many data Mining tasks that try to find interesting patterns from the database. Typically it refers to a set of items that frequently appear together in transaction dataset. There are several mining algorithm being used for frequent item set mining, yet most do not scale to the type of data we presented with today, so called “BIG DATA”. Big Data is a collection of large data sets. Our approach is to work on the frequent item set mining over the large dataset with scalable and speedy way. Big Data basically works with Map Reduce along with HDFS is used to find out frequent item sets from Big Data on large cluster. This paper focuses on using pre-processing & mining algorithm as hybrid approach for big data over Hadoop platform.

Keywords: frequent item set mining, big data, Hadoop, MapReduce

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Authors: Hussaini Garba Mohammed

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Background/Significance: The lack of data gathering is affecting NGOs world-wide in general to have good data information about educational and health related issues among communities in any country and around the world. For example, HIV/AIDS smoking (Tuberculosis diseases) and COVID-19 virus carriers is becoming a serious public health problem, especially among old men and women. But there is no full details data survey assessment from communities, villages, and rural area in some countries to show the percentage of victims and patients, especial with this world COVID-19 virus among the people. These data are essential to inform programming targets, strategies, and priorities in getting good information about data gathering in any society.

Keywords: reliable information, data assessment, data mining, data communication

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Authors: Liang Zhao, Jili Zhang, Chongquan Zhong

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Energy consumption data, in particular those involving public buildings, are impacted by many factors: the building structure, climate/environmental parameters, construction, system operating condition, and user behavior patterns. Traditional methods for data analysis are insufficient. This paper delves into the data mining technology to determine its application in the analysis of building energy consumption data including energy consumption prediction, fault diagnosis, and optimal operation. Recent literature are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.

Keywords: data mining, data analysis, prediction, optimization, building operational performance

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Authors: Shahnewaz Khan

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Machine learning (ML) techniques are often used in projects for creating data-driven applications. These tasks typically demand additional research and analysis. The proper technique and strategy must be chosen to ensure the success of data-driven projects. Otherwise, even exerting a lot of effort, the necessary development might not always be possible. In this post, an effort to examine the workflow of data-driven software development projects and its implementation process in order to describe how to manage a project successfully. Which will assist in minimizing the added workload.

Keywords: data, data-driven projects, data science, NLP, software project

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Authors: M. Naidoo

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Artificial intelligence often requires large amounts of good quality data. Within important fields, such as healthcare, the training of AI systems predominately relies on health and personal data; however, the usage of this data is complicated by various layers of law and ethics that seek to protect individuals’ privacy rights. This research seeks to establish the challenges AI and data sciences pose to (i) informational rights, (ii) privacy rights, and (iii) data protection. To solve some of the issues presented, various methods are suggested, such as embedding values in technological development, proper balancing of rights and interests, and others.

Keywords: artificial intelligence, data science, law, policy

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Authors: Jing Zhao, Yoshiharu Ishikawa, Chuan Xiao, Kento Sugiura

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In order to analyze large-scale scientific data, research on data exploration and visualization has gained popularity. In this paper, we focus on the exploration and visualization of scientific simulation data, and define a spatial V-Optimal histogram for data summarization. We propose histogram construction algorithms based on a general binary hierarchical partitioning as well as a more specific one, the l-grid partitioning. For effective data summarization and efficient data visualization in scientific data analysis, we propose an optimal algorithm as well as a heuristic algorithm for histogram construction. To verify the effectiveness and efficiency of the proposed methods, we conduct experiments on the massive evacuation simulation data.

Keywords: simulation data, data summarization, spatial histograms, exploration, visualization

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Authors: Ana-María Gómez-Carvajal, Hernando Santamaría-García, Mateo Bernal, Mario Valderrama, Daniela Lizarazo, Juliana Restrepo, María Fernanda Barreto, Angélica Parra, Paula Torres, Diana Matallana, Jaime Silva, José Santamaría-García, Sandra Baez

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Higher levels of oxytocin are associated with better performance on social cognition tasks. However, higher levels of oxytocin have also been associated with increased levels of envy and schadenfreude. Considering these antecedents, this study aims to explore social emotions (i.e., envy and schadenfreude) and other components of social cognition (i.e. ToM and empathy), in women in the puerperal period and their respective partners, compared to a control group of men and women without children or partners. Control women should be in the luteal phase of the menstrual cycle or taking oral contraceptives as they allow oxytocin levels to remain stable. We selected this population since increased levels of oxytocin are present in both mothers and fathers of newborn babies. Both groups were matched by age, sex, and education level. Twenty-two parents of newborns (11 women, 11 men) and 15 controls (8 women, 7 men) performed an experimental task designed to trigger schadenfreude and envy. In this task, each participant was shown a real-life photograph and a description of two target characters matched in age and gender with the participant. The task comprised two experimental blocks. In the first block, participants read 15 sentences describing fortunate events involving either character. After reading each sentence, participants rated the event in terms of how much envy they felt for the character (1=no envy, 9=extreme envy). In the second block, participants read and reported the intensity of their pleasure (schadenfreude, 1=no pleasure, 9=extreme pleasure) in response to 15 unfortunate events happening to the characters. Five neutral events were included in each block. Moreover, participants were assessed with ToM and empathy tests. Potential confounding variables such as general cognitive functioning, stress levels, hours of sleep and depression symptoms were also measured. Results showed that parents of newborns showed increased levels of envy and schadenfreude. These effects are not explained by any confounding factor. Moreover, no significant differences were found in ToM or empathy tests. Our results offer unprecedented evidence of specific differences in envy and schadenfreude levels in parents of newborns. Our findings support previous studies showing a negative relationship between oxytocin levels and negative social emotions. Further studies should assess the direct relationship between oxytocin levels in parents of newborns and the performance in social emotions tasks.

Keywords: envy, empathy, oxytocin, schadenfreude, social emotions, theory of mind

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Authors: Bry X., Trottier C., Mortier F., Cornu G., Verron T.

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We address component-based regularization of a Multivariate Generalized Linear Model (MGLM). A set of random responses Y is assumed to depend, through a GLM, on a set X of explanatory variables, as well as on a set T of additional covariates. X is partitioned into R conceptually homogeneous blocks X1, ... , XR , viewed as explanatory themes. Variables in each Xr are assumed many and redundant. Thus, Generalised Linear Regression (GLR) demands regularization with respect to each Xr. By contrast, variables in T are assumed selected so as to demand no regularization. Regularization is performed searching each Xr for an appropriate number of orthogonal components that both contribute to model Y and capture relevant structural information in Xr. We propose a very general criterion to measure structural relevance (SR) of a component in a block, and show how to take SR into account within a Fisher-scoring-type algorithm in order to estimate the model. We show how to deal with mixed-type explanatory variables. The method, named THEME-SCGLR, is tested on simulated data.

Keywords: Component-Model, Fisher Scoring Algorithm, GLM, PLS Regression, SCGLR, SEER, THEME

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Authors: Vahid Bairami Rad

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Extracting knowledge from spatial data like GIS data is important to reduce the data and extract information. Therefore, the development of new techniques and tools that support the human in transforming data into useful knowledge has been the focus of the relatively new and interdisciplinary research area ‘knowledge discovery in databases’. Thus, we introduce a set of database primitives or basic operations for spatial data mining which are sufficient to express most of the spatial data mining algorithms from the literature. This approach has several advantages. Similar to the relational standard language SQL, the use of standard primitives will speed-up the development of new data mining algorithms and will also make them more portable. We introduced a database-oriented framework for spatial data mining which is based on the concepts of neighborhood graphs and paths. A small set of basic operations on these graphs and paths were defined as database primitives for spatial data mining. Furthermore, techniques to efficiently support the database primitives by a commercial DBMS were presented.

Keywords: spatial data base, knowledge discovery database, data mining, spatial relationship, predictive data mining

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Authors: B. Suraj Aravind, M. H. M. Krishna Prasad

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There exist emerging applications of data streams that require association rule mining, such as network traffic monitoring, web click streams analysis, sensor data, data from satellites etc. Data streams typically arrive continuously in high speed with huge amount and changing data distribution. This raises new issues that need to be considered when developing association rule mining techniques for stream data. This paper proposes to introduce an improved data stream association rule mining algorithm by eliminating the limitation of resources. For this, the concept of cloud computing is used. Inclusion of this may lead to additional unknown problems which needs further research.

Keywords: data stream, association rule mining, cloud computing, frequent itemsets

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Authors: Muhammad Sufyan, Muhammad D. Gogi, Muhammad Arshad, Ahmad Nawaz, Muhammad Usman

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Cotton (Gossypium hirsutum) universally known as silver fiber and is one of the most important cash crop of Pakistan. A wide array of pests constraints cotton production among which sucking insect pests cause serious losses. Mostly new chemistry insecticides used to control a wide variety of insect pests including sucking insect pests. In the present study efficacy of different neonicotinoids was evaluated against sucking insect pests of cotton in the field and in laboratory for red and dusky cotton bug. The experiment was conducted at Entomology Research Station, University of Agriculture Faisalabad, in a Randomized Complete Block Design (RCBD). Field trial was conducted to evaluate the efficacy of Confidence Ultra (Imidacloprid) 70% SL, Confidor (Imidacloprid) 20% SL, Kendo (Lambda cyhalothrin) 24.7 SC, Actara (Thiamethoxam) 25% WG, Forcast (Tebufenozide+ Emamectin benzoate) 8.8 EW and Timer (Emamectin benzoate) 1.9 EC at their recommended doses. The data was collected on per leaf basis of thrips, aphid, jassid and whitefly before 24 hours of spray. The post treatment data was recorded after 24, 48 and 72 hours. The fresh, non-infested and untreated cotton leaves was collected from the field and brought to the laboratory to assess the efficacy of neonicotinoids against red and dusky cotton bug. After data analysis all the insecticides were found effective against sucking pests. Confidence Ultra was highly effective against the aphid, jassid, and whitefly and gave maximum mortality, while showed non-significant results against thrips. In case of aphid plot which was treated with Kando 24.7 SC showed significant mortality after 72 hours of pesticide application. Similar trends were found in laboratory conditions with all these treatments by making different concentrations and had significant impact on dusky cotton bug and red cotton bug population after 24, 48 and 72 hours after application.

Keywords: cotton, laboratory and field conditions, neonicotinoids, sucking insect pests

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Authors: Olusegun Raphael Adeyemi, Samuel Oluwaseun Osunleti, Abiddin Adekunle Bashiruddin

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Field trials were conducted in 2020 and 2021 at the Teaching and Research Farm of the Federal University of Agriculture Abeokuta, Ogun State, Nigeria to evaluate the effect of biochar application under different weeding regimes on growth and yield of okra. Treatments were laid out in split- plot in a randomized complete block design with three replications. Main plot treatments were three levels of biochar namely 0t/ha, 10t/ha and 20t/ha while sub-plots treatments consisted of four weeding regimes (weeding at 3, 6 and 9 WAS, weeding at 3 and 6 WAS, weeding at 3 WAS and weedy check as control). Data collected on growth and yield of okra, and weed parameters were subjected to analysis of variance and treatment means were separated using least significant difference at p < 0.05. Results showed that biochar applied at 20 t/ha increased okra yield by 47.5% compared to the control. Weeding at 3, 6 and 9 WAS gave the highest okra yield. Uncontrolled weed infestation throughout crop growth resulted in 87.3% yield reduction in okra. It is concluded that weed suppression , growth and yield of okra can be enhanced by the application of biochar at 20t/ha and weeding at 3, 6 and 9 WAS hence recommended.

Keywords: biochar, okra, weeding, weed competition

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Authors: Tosin Ige

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Ethics must be a condition of the world, like logic. (Ludwig Wittgenstein, 1889-1951). As important as data mining is, it possess a significant threat to ethics, privacy, and legality, since data mining makes it difficult for an individual or consumer (in the case of a company) to control the accessibility and usage of his data. This research focuses on Current issues and the latest research and development on Privacy preserving data mining methods as at year 2022. It also discusses some advances in those techniques while at the same time highlighting and providing a new technique as a solution to an existing technique of privacy preserving data mining methods. This paper also bridges the wide gap between Data mining and the Web Application Programing Interface (web API), where research is urgently needed for an added layer of security in data mining while at the same time introducing a seamless and more efficient way of data mining.

Keywords: data, privacy, data mining, association rule, privacy preserving, mining technique

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Authors: A. Alexandru, C. A. Alexandru, D. Coardos, E. Tudora

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Big Data (BD) is associated with a new generation of technologies and architectures which can harness the value of extremely large volumes of very varied data through real time processing and analysis. It involves changes in (1) data types, (2) accumulation speed, and (3) data volume. This paper presents the main concepts related to the BD paradigm, and introduces architectures and technologies for BD and BD sets. The integration of BD with the Hadoop Framework is also underlined. BD has attracted a lot of attention in the public sector due to the newly emerging technologies that allow the availability of network access. The volume of different types of data has exponentially increased. Some applications of BD in the public sector in Romania are briefly presented.

Keywords: big data, big data analytics, Hadoop, cloud

Procedia PDF Downloads 282

Authors: Olusegun Raphael Adeyemi, Samuel Oluwaseun Osunleti, Abiddin Adekunle Bashiruddin

Abstract:

Field trials were conducted in 2020 and 2021 at the Teaching and Research Farm of the Federal University of Agriculture Abeokuta, Ogun State, Nigeria, to evaluate the effect of biochar application under different weeding regimes on the growth and yield of okra. Treatments were laid out in a split- plot in a randomized complete block design with three replications. Main plot treatments were three levels of biochar, namely 0t/ha, 10t/ha and 20t/ha while sub-plot treatments consisted of four weeding regimes (weeding at 3, 6 and 9 WAS, weeding at 3 and 6 WAS, weeding at 3 WAS and weedy check as control). Data collected on growth and yield of okra and weed parameters were subjected to analysis of variance, and treatment means were separated using the least significant difference at p < 0.05. Results showed that biochar applied at 20 t/ha increased okra yield by 47.5% compared to the control. Weeding at 3, 6 and 9 WAS gave the highest okra yield. Uncontrolled weed infestation throughout crop growth resulted in an 87.3% yield reduction in okra. It is concluded that weed suppression, growth and yield of okra can be enhanced by the application of biochar at 20t/ha and weeding at 3, 6 and 9 WAS hence recommended.

Keywords: biochar, okra, weeding, weed competition, yield

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Authors: Aïcha Ben Salem, Faouzi Boufares, Sebastiao Correia

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The subject covered in this paper aims at assisting the user in its quality approach. The goal is to better extract, mix, interpret and reuse data. It deals with the semantic schema recognition of a data source. This enables the extraction of data semantics from all the available information, inculding the data and the metadata. Firstly, it consists of categorizing the data by assigning it to a category and possibly a sub-category, and secondly, of establishing relations between columns and possibly discovering the semantics of the manipulated data source. These links detected between columns offer a better understanding of the source and the alternatives for correcting data. This approach allows automatic detection of a large number of syntactic and semantic anomalies.

Keywords: schema recognition, semantic data profiling, meta-categorisation, semantic dependencies inter columns

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Authors: O. J. G. Somsen, G. P. M. Wagemakers

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Magnetic signature detection provides sensitive detection of metal objects, especially in the natural environment. Our group is developing a tabletop setup for magnetic signatures of various small and model objects. A particular issue is the separation of permanent and induced magnetization. While the latter depends only on the composition and shape of the object, the former also depends on the magnetization history. With common deperming techniques, a significant permanent signature may still remain, which confuses measurements of the induced component. We investigate a basic technique of separating the two. Measurements were done by moving the object along an aluminum rail while the three field components are recorded by a detector attached near the center. This is done first with the rail parallel to the Earth magnetic field and then with anti-parallel orientation. The reversal changes the sign of the induced- but not the permanent magnetization so that the two can be separated. Our preliminary results on a small iron block show excellent reproducibility. A considerable permanent magnetization was indeed present, resulting in a complex asymmetric signature. After separation, a much more symmetric induced signature was obtained that can be studied in detail and compared with theoretical calculations.

Keywords: magnetic signature, data analysis, magnetization, deperming techniques

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Authors: Winfred Okoe Addy, Jean Jacques Dominique Beraud

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Access control systems (ACs) are some of the most important components in safety areas. Inaccuracies of regulatory frameworks make personal policies and remedies more appropriate than standard models or protocols. This problem is exacerbated by the increasing complexity of software, such as integrated Big Data (BD) software for controlling large volumes of encrypted data and resources embedded in a dedicated BD production system. This paper proposes a general access control strategy system for the diffusion of Big Data domains since it is crucial to secure the data provided to data consumers (DC). We presented a general access control circulation strategy for the Big Data domain by describing the benefit of using designated access control for BD units and performance and taking into consideration the need for BD and AC system. We then presented a generic of Big Data access control system to improve the dissemination of Big Data.

Keywords: access control, security, Big Data, domain

Procedia PDF Downloads 109

Authors: D. Serero, L. Couton, J. D. Parisse, R. Leroy

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In urban planning, an increasing number of cities require wind analysis to verify comfort of public spaces and around buildings. These studies are made using computer fluid dynamic simulation (CFD). However, this technique is often based on wind information taken from meteorological stations located at several kilometers of the spot of analysis. The approximated input data on project surroundings produces unprecise results for this type of analysis. They can only be used to get general behavior of wind in a zone but not to evaluate precise wind speed. This paper presents another approach to this problem, based on collecting wind data and generating an urban wind cartography using connected ultrasound anemometers. They are wireless devices that send immediate data on wind to a remote server. Assembled in array, these devices generate geo-localized data on wind such as speed, temperature, pressure and allow us to compare wind behavior on a specific site or building. These Netatmo-type anemometers communicate by wifi with central equipment, which shares data acquired by a wide variety of devices such as wind speed, indoor and outdoor temperature, rainfall, and sunshine. Beside its precision, this method extracts geo-localized data on any type of site that can be feedback looped in the architectural design of a building or a public place. Furthermore, this method allows a precise calibration of a virtual wind tunnel using numerical aeraulic simulations (like STAR CCM + software) and then to develop the complete volumetric model of wind behavior over a roof area or an entire city block. The paper showcases connected ultrasonic anemometers, which were implanted for an 18 months survey on four study sites in the Grand Paris region. This case study focuses on Paris as an urban environment with multiple historical layers whose diversity of typology and buildings allows considering different ways of capturing wind energy. The objective of this approach is to categorize the different types of wind in urban areas. This, particularly the identification of the minimum and maximum wind spectrum, helps define the choice and performance of wind energy capturing devices that could be implanted there. The localization on the roof of a building, the type of wind, the altimetry of the device in relation to the levels of the roofs, the potential nuisances generated. The method allows identifying the characteristics of wind turbines in order to maximize their performance in an urban site with turbulent wind.

Keywords: computer fluid dynamic simulation in urban environment, wind energy harvesting devices, net-zero energy building, urban wind behavior simulation, advanced building skin design methodology

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Authors: Michael Gidey Gebru

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Most of Data Envelopment Analysis models operate in a static environment with input and output parameters that are chosen by deterministic data. However, due to ambiguity brought on shifting market conditions, input and output data are not always precisely gathered in real-world scenarios. Fuzzy numbers can be used to address this kind of ambiguity in input and output data. Therefore, this work aims to expand crisp Data Envelopment Analysis into Data Envelopment Analysis with fuzzy environment. In this study, the input and output data are regarded as fuzzy triangular numbers. Then, the Data Envelopment Analysis model with fuzzy environment is solved using a multi-objective method to gauge the Decision Making Units' efficiency. Finally, the developed Data Envelopment Analysis model is illustrated with an application on real data 50 educational institutions.

Keywords: efficiency, Data Envelopment Analysis, fuzzy, higher education, input, output

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Authors: Adamu S. Salawu, Ibrahim O. Isah

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Direct solution to several forms of fourth-order ordinary differential equations is not easily obtained without first reducing them to a system of first-order equations. Thus, numerical methods are being developed with the underlying techniques in the literature, which seeks to approximate some classes of fourth-order initial value problems with admissible error bounds. Multistep methods present a great advantage of the ease of implementation but with a setback of several functions evaluation for every stage of implementation. However, hybrid methods conventionally show a slightly higher order of truncation for any k-step linear multistep method, with the possibility of obtaining solutions at off mesh points within the interval of solution. In the light of the foregoing, we propose the continuous form of a hybrid multistep method with Chebyshev polynomial as a basis function for the numerical integration of fourth-order initial value problems of ordinary differential equations. The basis function is interpolated and collocated at some points on the interval [0, 2] to yield a system of equations, which is solved to obtain the unknowns of the approximating polynomial. The continuous form obtained, its first and second derivatives are evaluated at carefully chosen points to obtain the proposed block method needed to directly approximate fourth-order initial value problems. The method is analyzed for convergence. Implementation of the method is done by conducting numerical experiments on some test problems. The outcome of the implementation of the method suggests that the method performs well on problems with oscillatory or trigonometric terms since the approximations at several points on the solution domain did not deviate too far from the theoretical solutions. The method also shows better performance compared with an existing hybrid method when implemented on a larger interval of solution.

Keywords: Chebyshev polynomial, collocation, hybrid multistep method, initial value problems, interpolation

Procedia PDF Downloads 103

Authors: Arwin Malabanan, Carl Chester Ragudo, Jerome Tadiosa, John Dee Mangoba, Eric Augustus Tingatinga, Romeo Eliezer Longalong

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Concrete hollow blocks (CHB) are the most commonly used masonry block for walls in residential houses, school buildings and public buildings in the Philippines. During the recent 2013 Bohol earthquake (Mw 7.2), it has been proven that CHB walls are very vulnerable to severe external action like strong ground motion. In this paper, a numerical model of CHB structures is proposed, and seismic behavior of CHB houses is presented. In modeling, the Rigid Body Spring-Discrete Element method (RBS-DEM)) is used wherein masonry blocks are discretized into rigid elements and connected by nonlinear springs at preselected contact points. The shear and normal stiffness of springs are derived from the material properties of CHB unit incorporating the grout and mortar fillings through the volumetric transformation of the dimension using material ratio. Numerical models of reinforced and unreinforced walls are first subjected to linearly-increasing in plane loading to observe the different failure mechanisms. These wall models are then assembled to form typical model masonry houses and then subjected to the El Centro and Pacoima earthquake records. Numerical simulations show that the elastic, failure and collapse behavior of the model houses agree well with shaking table tests results. The effectiveness of the method in replicating failure patterns will serve as a basis for the improvement of the design and provides a good basis of strengthening the structure.

Keywords: concrete hollow blocks, discrete element method, earthquake, rigid body spring model

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Authors: Cahya Wimar Wicaksono, Reynara Davin Chen, Alvian Kristianto Santoso

Abstract:

Grasberg Cu-Au ore deposit as one of the biggest porphyry deposits located in Papua Province, Indonesia produced by several intrusion that restricted by Heavy Sulphide Zone (HSZ) in peripheral. HSZ is the rock that becomes the contact between Grassberg Igneous Complex (GIC) with sedimentary and igneous rock outside, which is rich in sulphide minerals such as pyrite ± pyrrhotite. This research is to obtain the characteristic of HSZ based on geotechnical, geochemical and mineralogy aspect and those implication for daily mining operational activities. Method used in this research are geological and alteration mapping, core logging, FAA (Fire Assay Analysis), AAS (Atomic absorption spectroscopy), RQD (Rock Quality Designation) and rock water content. Data generated from methods among RQD data, mineral composition and grade, lithological and structural geology distribution in research area. The mapping data show that HSZ material characteristics divided into three type based on rocks association, there are near igneous rocks, sedimentary rocks and on HSZ area. And also divided based on its location, north and south part of research area. HSZ material characteristic consist of rock which rich of pyrite ± pyrrhotite, and RQD range valued about 25%-100%. Pyrite ± pyrrhotite which outcropped will react with H₂O and O₂ resulting acid that generates corrosive effect on steel wire and rockbolt. Whereas, pyrite precipitation proses in HSZ forming combustible H₂S gas which is harmful during blasting activities. Furthermore, the impact of H₂S gas in blasting activities is forming poison gas SO₂. Although HSZ high grade Cu-Au, however those high grade Cu-Au rich in sulphide components which is affected in flotation milling process. Pyrite ± pyrrhotite in HSZ will chemically react with Cu-Au that will settle in milling process instead of floating.

Keywords: combustible, corrosive, heavy sulphide zone, pyrite ± pyrrhotite

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Authors: Jamil Hijazi, Stirling Howieson

Abstract:

Reducing energy consumption and CO₂ emissions are probably the greatest challenge now facing mankind. From considerations surrounding global warming and CO₂ production, it has to be recognized that oil is a finite resource and the KSA like many other oil-rich countries will have to start to consider a horizon where hydro-carbons are not the dominant energy resource. The employment of hybrid ground-cooling pipes in combination with the black body solar collection and radiant night cooling systems may have the potential to displace a significant proportion of oil currently used to run conventional air conditioning plant. This paper presents an investigation into the viability of such hybrid systems with the specific aim of reducing cooling load and carbon emissions while providing all year-round thermal comfort in a typical Saudi Arabian urban housing block. Soil temperatures were measured in the city of Jeddah. A parametric study then was carried out by computational simulation software (DesignBuilder) that utilized the field measurements and predicted the cooling energy consumption of both a base case and an ideal scenario (typical block retro-fitted with insulation, solar shading, ground pipes integrated with hypocaust floor slabs/stack ventilation and radiant cooling pipes embed in floor). Initial simulation results suggest that careful ‘ecological design’ combined with hybrid radiant and ground pipe cooling techniques can displace air conditioning systems, producing significant cost and carbon savings (both capital and running) without appreciable deprivation of amenity.

Keywords: cooling load, energy efficiency, ground pipe cooling, hybrid cooling strategy, hydronic radiant systems, low carbon emission, passive designs, thermal comfort

Procedia PDF Downloads 204