Search results for: partially observed data

Authors: Kacper Tomasz Kröber-Mulawa

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This paper will address the topic of data ownership from an economic perspective, and examples of economic limitations of data property rights will be provided, which have been identified using methods and approaches of economic analysis of law. To properly build a background for the economic focus, in the beginning a short perspective of data and data ownership in the EU’s legal system will be provided. It will include a short introduction to its political and social importance and highlight relevant viewpoints. This will stress the importance of a Single Market for data but also far-reaching regulations of data governance and privacy (including the distinction of personal and non-personal data, data held by public bodies and private businesses). The main discussion of this paper will build upon the briefly referred to legal basis as well as methods and approaches of economic analysis of law.

Keywords: antitrust, data, data ownership, digital economy, property rights

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Authors: Syeda Fahria Hoque Mimmi, Aparna Islam

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Genetically engineered (GE) plants are the need of time for increased demand for food. A complete set of regulations need to be followed from the development of a GE plant to its release into the environment. The whole regulation system is categorized into separate stages for maintaining the proper biosafety. Environmental risk assessment (ERA) is one of such crucial stages in the whole process. ERA identifies potential risks and their impacts through science-based evaluation where it is done in a case-by-case study. All the countries which deal with GE plants follow specific guidelines to conduct a successful ERA. In this study, ERA guidelines of 4 developing and 4 developed countries, including Bangladesh, were compared. ERA guidelines of countries such as India, Canada, Australia, the European Union, Argentina, Brazil, and the US were considered as a model to conduct the comparison study with Bangladesh. Initially, ten parameters were detected to compare the required data and information among all the guidelines. Surprisingly, an adequate amount of data and information requirements (e.g., if the intended modification/new traits of interest has been achieved or not, the growth habit of GE plants, consequences of any potential gene flow upon the cultivation of GE plants to sexually compatible plant species, potential adverse effects on the human health, etc.) matched between all the countries. However, a few differences in data requirement (e.g., agronomic conventions of non-transformed plants, applicants should clearly describe experimental procedures followed, etc.) were also observed in the study. Moreover, it was found that only a few countries provide instructions on the quality of the data used for ERA. If these similarities are recognized in a more framed manner, then the approval pathway of GE plants can be shared.

Keywords: GE plants, ERA, harmonization, ERA guidelines, Information and data requirements

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Authors: Abdullah Alsaeed

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Virtualized computing and cloud computing infrastructures are no longer fuzz or marketing term. They are a core reality in today’s corporate Information Technology (IT) organizations. Hence, developing an effective and efficient methodologies for data backup and data recovery is required more than any time. The purpose of data backup and recovery techniques are to assist the organizations to strategize the business continuity and disaster recovery approaches. In order to accomplish this strategic objective, a variety of mechanism were proposed in the recent years. This research paper will explore and examine the latest techniques and solutions to provide data backup and restoration for the cloud computing platforms.

Keywords: data backup, data recovery, cloud computing, business continuity, disaster recovery, cost-effective, data encryption.

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Authors: Loai AbdAllah, Mahmoud Kaiyal

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Missing values in real-world datasets are a common problem. Many algorithms were developed to deal with this problem, most of them replace the missing values with a fixed value that was computed based on the observed values. In our work, we used a distance function based on Bhattacharyya distance to measure the distance between objects with missing values. Bhattacharyya distance, which measures the similarity of two probability distributions. The proposed distance distinguishes between known and unknown values. Where the distance between two known values is the Mahalanobis distance. When, on the other hand, one of them is missing the distance is computed based on the distribution of the known values, for the coordinate that contains the missing value. This method was integrated with Wikaya, a digital health company developing a platform that helps to improve prevention of chronic diseases such as diabetes and cancer. In order for Wikaya’s recommendation system to work distance between users need to be measured. Since there are missing values in the collected data, there is a need to develop a distance function distances between incomplete users profiles. To evaluate the accuracy of the proposed distance function in reflecting the actual similarity between different objects, when some of them contain missing values, we integrated it within the framework of k nearest neighbors (kNN) classifier, since its computation is based only on the similarity between objects. To validate this, we ran the algorithm over diabetes and breast cancer datasets, standard benchmark datasets from the UCI repository. Our experiments show that kNN classifier using our proposed distance function outperforms the kNN using other existing methods.

Keywords: missing values, incomplete data, distance, incomplete diabetes data

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Authors: Mohamed Mahdi Alshahni, Takashi Tamura, Koichi Makimura

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Objective: The objective of this study is to evaluate roles of ABC-type efflux transporters in the resistance of Candida auris against common antifungal agents. Material and Methods: A wild-type C. auris strain and its antifungal resistant derivative strain that is generated through induction by antifungal agents were used in this study. The strains were cultured onto media containing beauvericin alone or in combination with azole agents. Moreover, expression levels of four ABC-type transporter’s homologs in those strains were analyzed by real time PCR with or without antifungal stress by fluconazole or voriconazole. Results: Addition of beauvericin helped to partially restore the susceptibility of the resistant strain against fluconazole, suggesting participation of ABC-type transporters in the resistance mechanism. Real time PCR results showed that mRNA levels of three out of the four analyzed transporters in the resistant strain were more than 2-fold higher than their counterparts in the wild-type strain under negative control and antifungal agent-containing conditions. Conclusion: C. auris is an emerging multidrug-resistant pathogen causing human mortality worldwide. Providing effective treatment has been hampered by the resistance to antifungal drugs, demanding understanding the resistance mechanism in order to devise new therapeutic strategies. Our data suggest a partial contribution of ABC-type transporters to the resistance of this pathogen.

Keywords: resistance, C. auris, transporters, antifungi

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Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

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In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: data estimation, link data, machine learning, road network

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Authors: Monica Lia

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This article presents a customer data analysis model using business intelligence tools for data modelling, transforming, data visualization and dynamic reports building. Economic organizational customer’s analysis is made based on the information from the transactional systems of the organization. The paper presents how to develop the data model starting for the data that companies have inside their own operational systems. The owned data can be transformed into useful information about customers using business intelligence tool. For a mature market, knowing the information inside the data and making forecast for strategic decision become more important. Business Intelligence tools are used in business organization as support for decision-making.

Keywords: customer analysis, business intelligence, data warehouse, data mining, decisions, self-service reports, interactive visual analysis, and dynamic dashboards, use cases diagram, process modelling, logical data model, data mart, ETL, star schema, OLAP, data universes

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Authors: Jun Gil Ahn, Jong Kang Park, Jong Tae Kim

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In this paper, we analyze major components of activity recognition (AR) in wearable device with 9-axis sensors and sensor fusion filters. 9-axis sensors commonly include 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. We chose sensor fusion filters as Kalman filter and Direction Cosine Matrix (DCM) filter. We also construct sensor fusion data from each activity sensor data and perform classification by accuracy of AR using Naïve Bayes and SVM. According to the classification results, we observed that the DCM filter and the specific combination of the sensing axes are more effective for AR in wearable devices while classifying walking, running, ascending and descending.

Keywords: accelerometer, activity recognition, directiona cosine matrix filter, gyroscope, Kalman filter, magnetometer

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Authors: Mahsa Hadipour Jahromy, Sara Rezaii

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Parkinson’s disease (PD) results primarily from the death of dopaminergic neurons in the substantia nigra. Soon after the discovery of levodopa and its beneficial effects in chronic administration, debilitating involuntary movements observed, termed levodopa-induced dyskinesia (LID) with poorly understood pathogenesis. Polyphenol-rich compounds, like pomegranate, provided neuroprotection in several animal models of brain diseases. In the present work, we investigated whether pomegranate has preventive effects following 4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced dopaminergic degenerations and the potential to diminish LID in mice. Mice model of PD was induced by MPTP (30 mg/kg daily for five consecutive days). To induce a mice model of LID, valid PD mice were treated with levodopa (50 mg/kg, i.p) for 15 days. Then the effects of chronic co-administration of pomegranate juice (20 ml/kg) with levodopa and continuing for 10 days, evaluated. Behavioural tests were performed in all groups, every other day including: Abnormal involuntary movements (AIMS), forelimb adjusting steps, cylinder, and catatonia tests. Finally, brain tissue sections were prepared to study substantia nigra changes and dopamine neuron density after treatments. With this MPTP regimen, significant movement disorders revealed in AIMS tests and there was a reduction in dopamine striatal density. Levodopa attenuates their loss caused by MPTP, however, in chronic administration, dyskinesia observed in forelimb adjusting step and cylinder tests. Besides, catatonia observed in some cases. Chronic pomegranate co-administration significantly improved LID in both tests and reduced dopaminergic loss in substantia nigra. These data indicate that pomegranate might be a good adjunct for preserving dopaminergic neurons in the substantia nigra and reducing LID in mice.

Keywords: levodopa-induced dyskinesia, MPTP, Parkinson’s disease, pomegranate

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Authors: Syed Towseef Ahmad, Fatima Amin, Pritha Acharya, Anil K. Gupta, Pervez Ahmad

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Glaciers being the sentinels of climate change, are the most visible evidence of global warming. Given the unavailability of observed field-based data, this study has focussed on the use of geospatial techniques to obtain information about the glaciers of Pir-Panjal (PPJ) and the Great Himalayan Regions of Jhelum Basin (GHR). These glaciers need to be monitored in line with the variations in climatic conditions because they significantly contribute to various sectors in the region. The main aim of this study is to map the glaciers in the two adjacent regions (PPJ and GHR) in the north-western Himalayas with different topographies and compare the changes in various glacial attributes during two different time periods (1990-2020). During the last three decades, both PPJ as well as GHR regions have observed deglaciation of around 36 and 26 percent, respectively. The mean elevation of GHR glaciers has increased from 4312 to 4390 masl, while the same for PPJ glaciers has increased from 4085 to 4124 masl during the observation period. Using accumulation area ratio (AAR) method, mean mass balance of -34.52 and -37.6 cm.w.e was recorded for the glaciers of GHR and PPJ, respectively. The difference in areal and mass loss of glaciers in these regions may be due to (i) the smaller size of PPJ glaciers which are all smaller than 1 km² and are thus more responsive to climate change (ii) Higher mean elevation of GHR glaciers (iii) local variations in climatic variables in these glaciated regions. Time series analysis of climate variables indicates that both the mean maximum and minimum temperatures of Qazigund station (Tmax= 19.2, Tmin= 6.4) are comparatively higher than the Pahalgam station (Tmax= 18.8, Tmin= 3.2). Except for precipitation in Qazigund (Slope= - 0.3 mm a⁻¹), each climatic parameter has shown an increasing trend during these three decades, and with the slope of 0.04 and 0.03°c a⁻¹, the positive trend in Tmin (pahalgam) and Tmax (qazigund) are observed to be statistically significant (p≤0.05).

Keywords: glaciers, climate change, Pir-Panjal, greater Himalayas, mass balance

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Authors: Şevket Evci, Mehmet Akif Karsli

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The aim of this study is to determine nutrient contents, in situ ruminal degradation kinetics and protein fractions of screenings bean (B), chick pea (ChP), red lentil (RL) and green lentil (GL) that is used as residue in grain legume packing industry. For this purpose, four samples of each legumes species-a total of 16 samples, collected from different parts of our country were utilized. Feedstuffs used in the experiment were incubated for 0, 2 4, 8, 12, 24, and 48 hours in the rumen of 3 ruminally cannulated Akkaraman rams as duplicate. The nutrient contents, in situ ruminal dry matter (DM), organic matter (OM) and crude protein (CP) degradabilities and fractions, and escape protein contents were evaluated. The highest OM and CP contents were observed in RL (P<0.05). Chick pea had the highest ether extract (EE) content and EE values were 3.47, 6.72, 2.26, 8.66 % for RL, B, GL and ChP, respectively (P<0.05). Crude fiber (CF), ADF, and NDF contents were the highest in RL and the lowest in ChP. CF values were 24.03, 10.80, 4.09 and 3.57 % for RL, GL, B and ChP (P<0.05). Acid detergent insoluble nitrogen content of samples did not differ. Escape protein content was the highest in RL and the lowest in B (P<0.05). After 48 h incubation, the lowest OM and CP degradabilities were observed in RL. While the highest OM degradability was seen in ChP the highest CP degradability was observed in B (P<0.05). The lowest water soluble OM and CP contents were observed in RL whereas the highest potentially degradable OM and CP contents were seen in B and ChP (P<0.05). Both rate of OM and CP degradations (k-1) did not differ among samples (P>0.05). In conclusion, it was noted that feedstuffs (GL, ChP and B) used in the experiment except RL had a greater ruminal degradibilities of both OM and CP and moreover, had a higher escape protein contents, except B. It was thought that these feedstuffs can be substituted with some of common protein sources used in animal nutrition.

Keywords: in situ, nutrient contents, ruminant, subsieve

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Authors: Afshin J. Ghajar, Swanand M. Bhagwat

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The main purpose of this work is to experimentally investigate the effect of pipe orientation on two phase flow phenomenon. Flow pattern, void fraction and two phase pressure drop is measured in a polycarbonate pipe with an inside diameter of 12.7mm for inclination angles ranging from -20° to +20° using air-water fluid combination. The experimental data covers all flow patterns and the entire range of void fraction typically observed in two phase flow. The effect of pipe orientation on void fraction and two phase pressure drop is justified with reference to the change in flow structure and two phase flow behavior. In addition to this, the top performing void fraction and two phase pressure drop correlations available in the literature are presented and their performance is assessed against the experimental data in the present study and that available in the literature.

Keywords: flow patterns, inclined two phase flow, pressure drop, void fraction

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Authors: M. S. Nandana, K. Udaya Bhat, C. M. Manjunatha

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In this study aluminum alloy 7010 was subjected to three different ageing treatments i.e., peak ageing (T6), over-ageing (T7451) and retrogression and re ageing (RRA) to study the influence of precipitate microstructure on the fatigue crack growth rate behavior. The microstructural modification was studied by using transmission electron microscope (TEM) to examine the change in the size and morphology of precipitates in the matrix and on the grain boundaries. The standard compact tension (CT) specimens were fabricated and tested under constant amplitude fatigue crack growth tests to evaluate the influence of heat treatment on the fatigue crack growth rate properties. The tests were performed in a computer-controlled servo-hydraulic test machine applying a load ratio, R = 0.1 at a loading frequency of 10 Hz as per ASTM E647. The fatigue crack growth was measured by adopting compliance technique using a CMOD gauge attached to the CT specimen. The average size of the matrix precipitates were found to be of 16-20 nm in T7451, 5-6 nm in RRA and 2-3 nm in T6 conditions respectively. The grain boundary precipitate which was continuous in T6, was disintegrated in RRA and T7451 condition. The PFZ width was lower in RRA compared to T7451 condition. The crack growth rate was higher in T7451 and lowest in RRA treated alloy. The RRA treated alloy also exhibits an increase in threshold stress intensity factor range (∆Kₜₕ). The ∆Kₜₕ measured was 11.1, 10.3 and 5.7 MPam¹/² in RRA, T6 and T7451 alloys respectively. The fatigue crack growth rate in RRA treated alloy was nearly 2-3 times lower than that in T6 and was one order lower than that observed in T7451 condition. The surface roughness of RRA treated alloy was more pronounced when compared to the other conditions. The reduction in fatigue crack growth rate in RRA alloy was majorly due to the increase in roughness and partially due to increase in spacing between the matrix precipitates. The reduction in crack growth rate and increase in threshold stress intensity range is expected to benefit the damage tolerant capability of aircraft structural components under service loads.

Keywords: damage tolerance, fatigue, heat treatment, PFZ, RRA

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Authors: K. Hardy, A. Maurushat

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Policy makers are increasingly looking to make evidence-based decisions. Evidence-based decisions have historically used rigorous methodologies of empirical studies by research institutes, as well as less reliable immediate survey/polls often with limited sample sizes. As we move into the era of Big Data analytics, policy makers are looking to different methodologies to deliver reliable empirics in real-time. The question is not why did these people do this for the last 10 years, but why are these people doing this now, and if the this is undesirable, and how can we have an impact to promote change immediately. Big data analytics rely heavily on government data that has been released in to the public domain. The open data movement promises greater productivity and more efficient delivery of services; however, Australian government agencies remain reluctant to release their data to the general public. This paper considers the barriers to releasing government data as open data, and how these barriers might be overcome.

Keywords: big data, open data, productivity, data governance

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Authors: Haragobinda Srichandan, Ashish Pathak, Dong Jin Kim, Seoung-Won Lee

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The present investigation deals with bioleaching of spent refinery catalyst (as received) using At. thiooxidans. The effect of substrate concentration and pulp density was studied. XPS analysis concluded that the metals in spent catalyst were present as both sulfide and oxides. The dissolution behavior of metals during bioleaching was different. During bioleaching, higher dissolution of Ni and lower dissolution of Mo, V and Al was observed. An increase in pulp density from 1% to 10% led to a decrease in leaching yields of all the metals. This was due to the substantial increase in medium pH at higher pulp densities. The maximum negative impact of pulp density was observed on the leaching yield of V. An increase in sulfur concentration from 0.5% to 2.5% didn’t bring positive impact on metal leaching yield. 0.5% sulfur was found to be the optimum above which no significant increase in leaching yields of metals was observed.

Keywords: At. thiooxidans, pulp density, spent catalyst, bioleaching

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Authors: Monica Dantas

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At what socio-economic costs can the food industry offer products at low prices? This research seeks to understand and to explore how we attribute competence and meaning, what enables the outcomes of agriculture and what institutions provides validation regarding food production. This study objective is to explain and interpret conditions of the present state of agriculture in Brazil centring on two distinct segments, agribusiness and family farming, as the Brazilian, rapidly changing political environment unfolds. The approach is grounded in multidisciplinary literature drawing from the politics of development, the sociology of food, the sustainability framework and the conceptual differences between agribusiness and family farming regarding the innate purpose of the two segments. In addition, a quantitative portion of the research includes secondary data analysis from statistical measurements, economic indicators, federal budget information, and census data to compare the two segments, conveying a general snapshot of the conditions investigated. The results raised questions about the perceived image of the success of agribusiness, against some contradicting economic checks and balances. Analyzing how public funds are invested in agriculture shed light on what can enable or undermine the development of food systems in Brazil. It also revealed how politics, ideology, and corporations might influence the Brazilian Federal. In the 2000-2018 observed timeline of annual federal spending on agriculture in Brazil, there is variation in the amount invested in family farming that seems to 'coincide' with the ideological direction of the federal government in power. It was also observed that significant changes in the institutional framework and financial support either promoted or purposely undermined family farming importance using public institutions to validate support for agribusiness.

Keywords: food politics, sustainability, family farming, food system, public budget

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Authors: Hema Bhardwaj, D. Srinivasa Rao

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Technology for analysing, processing, and extracting meaningful data from enormous and complicated datasets can be termed as "big data." The technique of big data mining and big data analysis is extremely helpful for business movements such as making decisions, building organisational plans, researching the market efficiently, improving sales, etc., because typical management tools cannot handle such complicated datasets. Special computational and statistical issues, such as measurement errors, noise accumulation, spurious correlation, and storage and scalability limitations, are brought on by big data. These unique problems call for new computational and statistical paradigms. This research paper offers an overview of the literature on big data mining, its process, along with problems and difficulties, with a focus on the unique characteristics of big data. Organizations have several difficulties when undertaking data mining, which has an impact on their decision-making. Every day, terabytes of data are produced, yet only around 1% of that data is really analyzed. The idea of the mining and analysis of data and knowledge discovery techniques that have recently been created with practical application systems is presented in this study. This article's conclusion also includes a list of issues and difficulties for further research in the area. The report discusses the management's main big data and data mining challenges.

Keywords: big data, data mining, data analysis, knowledge discovery techniques, data mining challenges

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Authors: Golnush Masghati-Amoli, Paul Chin

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Over the past recent years, with the rapid increases in data availability and computing power, Machine Learning (ML) techniques have been called on in a range of different industries for their strong predictive capability. However, the use of Machine Learning in commercial banking has been limited due to a special challenge imposed by numerous regulations that require lenders to be able to explain their analytic models, not only to regulators but often to consumers. In other words, although Machine Leaning techniques enable better prediction with a higher level of accuracy, in comparison with other industries, they are adopted less frequently in commercial banking especially for scoring purposes. This is due to the fact that Machine Learning techniques are often considered as a black box and fail to provide information on why a certain risk score is given to a customer. In order to bridge this gap between the explain-ability and performance of Machine Learning techniques, a Hybrid Model is developed at Dun and Bradstreet that is focused on blending Machine Learning algorithms with traditional approaches such as scorecards. The Hybrid Model maximizes efficiency of traditional scorecards by merging its practical benefits, such as explain-ability and the ability to input domain knowledge, with the deep insights of Machine Learning techniques which can uncover patterns scorecard approaches cannot. First, through development of Machine Learning models, engineered features and latent variables and feature interactions that demonstrate high information value in the prediction of customer risk are identified. Then, these features are employed to introduce observed non-linear relationships between the explanatory and dependent variables into traditional scorecards. Moreover, instead of directly computing the Weight of Evidence (WoE) from good and bad data points, the Hybrid Model tries to match the score distribution generated by a Machine Learning algorithm, which ends up providing an estimate of the WoE for each bin. This capability helps to build powerful scorecards with sparse cases that cannot be achieved with traditional approaches. The proposed Hybrid Model is tested on different portfolios where a significant gap is observed between the performance of traditional scorecards and Machine Learning models. The result of analysis shows that Hybrid Model can improve the performance of traditional scorecards by introducing non-linear relationships between explanatory and target variables from Machine Learning models into traditional scorecards. Also, it is observed that in some scenarios the Hybrid Model can be almost as predictive as the Machine Learning techniques while being as transparent as traditional scorecards. Therefore, it is concluded that, with the use of Hybrid Model, Machine Learning algorithms can be used in the commercial banking industry without being concerned with difficulties in explaining the models for regulatory purposes.

Keywords: machine learning algorithms, scorecard, commercial banking, consumer risk, feature engineering

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Authors: Hadeer E. Eliwa, Naglaa S. Bazan, Ebtissam A. Darweesh, Nagwa A. Sabri

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Background: Generic substitution of brand ivabradine prescriptions can reduce drug expenditures and improve adherence. However, the distrust of generic medicines by practitioners and patients due to doubts regarding their quality and fear of counterfeiting compromise the acceptance of this practice. Aim: The goal of this study is to compare the therapeutic equivalence of brand product versus the generic product of ivabradine in adult patients with chronic heart failure with reduced ejection fraction (≤ 40%) (HFrEF). Methodology: Thirty-two Egyptian patients with chronic heart failure with reduced ejection fraction (HFrEF) were treated with branded ivabradine (Procrolan ©) and generic (Bradipect ©) during 24 (2x12) weeks. Primary outcomes were resting heart rate (HR), NYHA FC, Quality of life (QoL) using Minnesota Living with Heart Failure (MLWHF) and EF. Secondary outcomes were the number of hospitalizations for worsening HFrEF and adverse effects. The washout period was not allowed. Findings: At the 12th week, the reduction in HR was comparable in the two groups (90.13±7.11 to 69±11.41 vs 96.13±17.58 to 67.31±8.68 bpm in brand and generic groups, respectively). Also, the increase in EF was comparable in the two groups (27.44 ±4.59 to 33.38±5.62 vs 32±5.96 to 39.31±8.95 in brand and generic groups, respectively). The improvement in NYHA FC was comparable in both groups (87.5% in brand group vs 93.8% in the generic group). The mean value of the QOL improved from 31.63±15.8 to 19.6±14.7 vs 35.68±17.63 to 22.9±15.1 for the brand and generic groups, respectively. Similarly, at end of 24 weeks, no significant changes were observed from data observed at 12th week regarding HR, EF, QoL and NYHA FC. Only minor side effects, mainly phosphenes, and a comparable number of hospitalizations were observed in both groups. Conclusion: The study revealed no statistically significant differences in the therapeutic effect and safety between generic and branded ivabradine. We assume that practitioners can safely interchange between them for economic reasons.

Keywords: bradipect©, heart failure, ivabradine, Procrolan ©, therapeutic equivalence

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Authors: Rimmy Yadav, Anmol Preet Kaur

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Big Data has demonstrated the vast potential in streamlining, deciding, spotting business drifts in different fields, for example, producing, fund, Information Technology. This paper gives a multi-disciplinary diagram of the research issues in enormous information and its procedures, instruments, and system identified with the privacy, data storage management, network and energy utilization, adaptation to non-critical failure and information representations. Other than this, result difficulties and openings accessible in this Big Data platform have made.

Keywords: big data, privacy, data management, network and energy consumption

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Authors: Ana Paula Lisboa Sohn, Filipa Dionísio Vieria, Nelson Casarotto, Idaulo José Cunha

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This paper aims at identifying and analyzing the knowledge transmission channels in textile and clothing clusters located in Brazil and in Europe. Primary data was obtained through interviews with key individuals. The collection of primary data was carried out based on a questionnaire with ten categories of indicators of knowledge transmission. Secondary data was also collected through a literature review and through international organizations sites. Similarities related to the use of the main transmission channels of knowledge are observed in all cases. The main similarities are: influence of suppliers of machinery, equipment and raw materials; imitation of products and best practices; training promoted by technical institutions and businesses; and cluster companies being open to acquire new knowledge. The main differences lie in the relationship between companies, where in Europe the intensity of this relationship is bigger when compared to Brazil. The differences also occur in importance and frequency of the relationship with the government, with the cultural environment, and with the activities of research and development. It is also found factors that reduce the importance of geographical proximity in transmission of knowledge, and in generating trust and the establishment of collaborative behavior.

Keywords: industrial clusters, interorganizational learning, knowledge transmission channels, textile and clothing industry

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Authors: Christian N. Chibo

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Imo River Basin is located in South Eastern Nigeria comprising of 11 states of Imo, Abia, Anambra, Ebonyi, Enugu, Edo, Rivers, Cross river, AkwaIbom, Bayelsa, Delta, and Bayelsa states. The basin has a fluvial erosional system dominated by powerful rivers coming down from steep slopes in the area. This research investigated various hazards associated with flood, the vulnerable areas, elements at risk of flood and various adaptation strategies adopted by local inhabitants to cope with the hazards. The research aim is to identify, examine and assess flood hazards, vulnerability and adaptations in the Upper Imo River Basin. The study identified the role of elevation in cause of flood, elements at risk of flood as well as examine the effectiveness or otherwise of the adaptation strategies for coping with the hazards. Data for this research is grouped as primary and secondary. Their various methods of generation are field measurement, questionnaire, library websites etc. Other types of data were generated from topographical, geological, and Digital Elevation model (DEM) maps, while the hydro meteorological data was sourced from Nigeria Meteorological Agency (NIMET), Meteorological stations of Geography and Environmental Management Departments of Imo State University and Alvan Ikoku Federal College of Education. 800 copies of questionnaire were distributed using systematic sampling to 8 locations used for the pilot survey. About 96% of the questionnaire were retrieved and used for the study. 13 flood events were identified in the study area. Their causes, years and dates of events were documented in the text, and the damages they caused were evaluated. The study established that for each flood event, there is over 200mm of rain observed on the day of the flood and the day before the flood. The study also observed that the areas that situate at higher elevation (See DEM) are less prone to flood hazards while areas at low elevations are more prone to flood hazards. Elements identified to be at risk of flood are agricultural land, residential dwellings, retail trading and related services, public buildings and community services. The study thereby recommends non settlement at flood plains and flood prone areas and rearrangement of land use activities in the upper Imo River Basin among others

Keywords: flood hazard, flood plain, geomorphology, Imo River Basin

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Authors: Mansoureh Ghiasabadi Farahani, Samira Kalantary, Sara Taghi-Pour, Mahboubeh Shamsi

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Nowadays, the development of computers technology and its recent applications provide access to new types of data, which have not been considered by the traditional data analysts. Two particularly interesting characteristics of such data sets include their huge size and streaming nature .Incremental learning techniques have been used extensively to address the data stream classification problem. This paper presents a concise survey on the obstacles and the requirements issues classifying data streams with using decision tree. The most important issue is to maintain a balance between accuracy and efficiency, the algorithm should provide good classification performance with a reasonable time response.

Keywords: big data, data streams, classification, decision tree

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Authors: Aishwarya Shekhar, Himanshu Sharma

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Data deduplication is one of important data compression techniques for eliminating duplicate copies of repeating data, and has been widely used in cloud storage to reduce the amount of storage space and save bandwidth. In this process, duplicate data is expunged, leaving only one copy means single instance of the data to be accumulated. Though, indexing of each and every data is still maintained. Data deduplication is an approach for minimizing the part of storage space an organization required to retain its data. In most of the company, the storage systems carry identical copies of numerous pieces of data. Deduplication terminates these additional copies by saving just one copy of the data and exchanging the other copies with pointers that assist back to the primary copy. To ignore this duplication of the data and to preserve the confidentiality in the cloud here we are applying the concept of hybrid nature of cloud. A hybrid cloud is a fusion of minimally one public and private cloud. As a proof of concept, we implement a java code which provides security as well as removes all types of duplicated data from the cloud.

Keywords: confidentiality, deduplication, data compression, hybridity of cloud

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Authors: M. I. Oshimagye, V. O. Ayuba, P. A. Annune

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The effect of Sublethal Concentrations of Parkia biglobosa pod extract on the growth and survival of Clarias gariepinus juveniles (mean weight 32.73g ± 0.0) were investigated under laboratory conditions for 8 weeks using the static renewal and continuous aeration system. Statistical analysis showed that fish exposed to various concentrations had significantly lower (P<0.05) growth rate than the control groups. The reduction in growth was observed to be directly proportional to increase in concentration. However, at 50 mg/L no significant depression in weight was observed.

Keywords: Clarias gariepinus, Parkia biglobosa, pod, weight

Procedia PDF Downloads 475

Authors: Devatha Kalyan Kumar, Aravindraj D., Sadathulla A.

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Big data are now rapidly expanding in all engineering and science and many other domains. The potential of large or massive data is undoubtedly significant, make sense to require new ways of thinking and learning techniques to address the various big data challenges. Machine learning is continuously unleashing its power in a wide range of applications. In this paper, the latest advances and advancements in the researches on machine learning for big data processing. First, the machine learning techniques methods in recent studies, such as deep learning, representation learning, transfer learning, active learning and distributed and parallel learning. Then focus on the challenges and possible solutions of machine learning for big data.

Keywords: active learning, big data, deep learning, machine learning

Procedia PDF Downloads 411

Authors: Tomy Prihananto, Damar Apri Sudarmadi

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Indonesia recognizes the right to privacy as a human right. Indonesia provides legal protection against data management activities because the protection of personal data is a part of human rights. This paper aims to describe the arrangement of data management and data management in Indonesia. This paper is a descriptive research with qualitative approach and collecting data from literature study. Results of this paper are comprehensive arrangement of data that have been set up as a technical requirement of data protection by encryption methods. Arrangements on encryption and protection of personal data are mutually reinforcing arrangements in the protection of personal data. Indonesia has two important and immediately enacted laws that provide protection for the privacy of information that is part of human rights.

Keywords: Indonesia, protection, personal data, privacy, human rights, encryption

Procedia PDF Downloads 159

Authors: E. Marušić-Paloka, I. Pažanin, M. Prša

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Subject of this study is the stationary heat conduction problem through a pipe filled with incompressible viscous fluid. In previous work, we observed the existence and uniqueness theorems for the corresponding boundary-value problem and within we have taken into account the effects of the pipe's dilatation due to the temperature of the fluid inside of the pipe. The main difficulty comes from the fact that flow domain changes depending on the solution of the observed heat equation leading to a non-standard coupled governing problem. The goal of this work is to find solution estimate since the exact solution of the studied problem is not possible to determine. We use an asymptotic expansion in order of a small parameter which is presented as a heat expansion coefficient of the pipe's material. Furthermore, an error estimate is provided for the mentioned asymptotic approximation of the solution for inner area of the pipe. Close to the boundary, problem becomes more complex so different approaches are observed, mainly Theory of Perturbations and Separations of Variables. In view of that, error estimate for the whole approximation will be provided with additional software simulations of gotten situation.

Keywords: asymptotic analysis, dilated pipe, error estimate, heat conduction

Procedia PDF Downloads 215

Authors: Fatma Tezel Sahin, Zeynep Nur Aydin Kilic, Aysegul Akinci Cosgun

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Family involvement activities are a significant place in increasing the success in preschool education and maintaining the education. It is necessary that both of the parents be in the family involvement activities. However, while mother involvement is obtained in the family involvement activities, father involvement is neglected. For that reason, the current study aims at determining the views of teachers with regard to father involvement in the preschool education programs. The working group of the study consisted of 23 preschool teachers. The study is a descriptive survey. The data were obtained through individual interviews. As a data collection instrument, “Teacher Interview Form” was used. The data were analysed through content analysis method. The data regarding the views of the teachers were given as frequency and percentage values. At the end of the research, a great majority of the teachers stated that they were proficient in applying family involvement studies. They also pointed out that they held more family meetings in order to obtain family involvement and then they implemented involvement activities both in the class and out of the class for parents. They expressed that they observed more mother involvement in these activities that fathers. Parents expressed that the reasons why fathers involved in these activities less compared to mothers were the working conditions of fathers and that it was regarded as a task of mothers. Depending on the results of the research, it is likely to recommend that fathers should be informed about the involvement in family activities and that some applications and opportunities should be supplied for the fathers in preschool education institutions in order to encourage them.

Keywords: preschool education, parent involvement, father involvement, teacher views

Procedia PDF Downloads 299

Authors: Dagim Dessalegn Haile

Abstract:

This study investigated the interaction between the drivers' socio-demographic background information (age, gender, and driving experience) and the risky events score in the i-DREAMS platform. Further, the relationship between the participants' background driving behavior and the i-DREAMS platform behavioral output scores of risky events was also investigated. The i-DREAMS acronym stands for Smart Driver and Road Environment Assessment and Monitoring System. It is a European Union Horizon 2020 funded project consisting of 13 partners, researchers, and industry partners from 8 countries. A total of 25 Belgian car drivers (16 male and nine female) were considered for analysis. Drivers' ages were categorized into ages 18-25, 26-45, 46-65, and 65 and older. Drivers' driving experience was also categorized into four groups: 1-15, 16-30, 31-45, and 46-60 years. Drivers are classified into two clusters based on the recorded score for risky events during phase 1 (baseline) using risky events; acceleration, deceleration, speeding, tailgating, overtaking, and lane discipline. Agglomerative hierarchical clustering using SPSS shows Cluster 1 drivers are safer drivers, and Cluster 2 drivers are identified as risky drivers. The analysis result indicated no significant relationship between age groups, gender, and experience groups except for risky events like acceleration, tailgating, and overtaking in a few phases. This is mainly because the fewer participants create less variability of socio-demographic background groups. Repeated measure ANOVA shows that cluster 2 drivers improved more than cluster 1 drivers for tailgating, lane discipline, and speeding events. A positive relationship between background drivers' behavior and i-DREAMS platform behavioral output scores is observed. It implies that car drivers who in the questionnaire data indicate committing more risky driving behavior demonstrate more risky driver behavior in the i-DREAMS observed driving data.

Keywords: i-dreams, car drivers, socio-demographic background, risky events

Procedia PDF Downloads 50