Search results for: micro data

Authors: Sibusiso Trevor Tshabalala, Samuel Lubbe, Vince Vuledzani Ndou

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Dependency on chemicals has had many disadvantages in pest management control strategies. Such genetic rodenticide resistance and secondary exposure risk are what is currently being experienced. Emphasis on integrated pest management suggests that to control future pests, early intervention and economic threshold development are key starting points in crop production. The significance of this research project is to help establish a relationship between Giant African Land Snail (Achatina Fulica) solution extract, its shell chemical properties, and farmer’s perceptions of biological control in eThekwini Municipality Agri-hubs. A mixed design approach to collecting data will be explored using a trial layout in the field and through interviews. The experimental area will be explored using a split-plot design that will be replicated and arranged in a randomised complete block design. The split-plot will have 0, 10, 20 and 30 liters of water to one liter of snail solution extract. Plots were 50 m² each with a spacing of 12 m between each plot and a plant spacing of 0.5 m (inter-row) ‘and 0.5 m (intra-row). Trials will be irrigated using sprinkler irrigation, with objective two being added to the mix every 4-5 days. The expected outcome will be improved soil fertility and micro-organisms population proliferation.

Keywords: giant african land snail, integrated pest management, photosynthesis, genetic rodenticide resistance, control future pests, shell chemical properties

Procedia PDF Downloads 104

Authors: Choupani Andisheh, Temucin Elif Sevval, Bediz Bekir

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Due to their various advantages compared to many other drug delivery systems such as hypodermic injections and oral medications, microneedle arrays (MNAs) are a promising drug delivery system. To achieve enhanced performance of the MN, it is crucial to develop numerical models, optimization methods, and simulations. Accordingly, in this work, the optimized design of dissolvable MNAs, as well as their manufacturing, is investigated. For this purpose, a mechanical model of a single MN, having the geometry of an obelisk, is developed using commercial finite element software. The model considers the condition in which the MN is under pressure at the tip caused by the reaction force when penetrating the skin. Then, a multi-objective optimization based on non-dominated sorting genetic algorithm II (NSGA-II) is performed to obtain geometrical properties such as needle width, tip (apex) angle, and base fillet radius. The objective of the optimization study is to reach a painless and effortless penetration into the skin along with minimizing its mechanical failures caused by the maximum stress occurring throughout the structure. Based on the obtained optimal design parameters, master (male) molds are then fabricated from PMMA using a mechanical micromachining process. This fabrication method is selected mainly due to the geometry capability, production speed, production cost, and the variety of materials that can be used. Then to remove any chip residues, the master molds are cleaned using ultrasonic cleaning. These fabricated master molds can then be used repeatedly to fabricate Polydimethylsiloxane (PDMS) production (female) molds through a micro-molding approach. Finally, Polyvinylpyrrolidone (PVP) as a dissolvable polymer is cast into the production molds under vacuum to produce the dissolvable MNAs. This fabrication methodology can also be used to fabricate MNAs that include bioactive cargo. To characterize and demonstrate the performance of the fabricated needles, (i) scanning electron microscope images are taken to show the accuracy of the fabricated geometries, and (ii) in-vitro piercing tests are performed on artificial skin. It is shown that optimized MN geometries can be precisely fabricated using the presented fabrication methodology and the fabricated MNAs effectively pierce the skin without failure.

Keywords: microneedle, microneedle array fabrication, micro-manufacturing structural optimization, finite element analysis

Procedia PDF Downloads 113

Authors: Jerlin George, R. Chitra

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The devices with sensors that can monitor your temperature, heart rate, and other vital signs and link to the internet, known as the Internet of Things (IoT), have completely transformed the way we control health. Providing real-time health data, these sensors improve diagnostics and treatment outcomes. Security and privacy matters when IoT comes into play in healthcare. Cyberattacks on centralized database systems are also a problem. To solve these challenges, the study uses blockchain technology coupled with proxy re-encryption to secure health data. ThingSpeak IoT cloud analyzes the collected data and turns them into blockchain transactions which are safely kept on the DriveHQ cloud. Transparency and data integrity are ensured by blockchain, and secure data sharing among authorized users is made possible by proxy re-encryption. This results in a health monitoring system that preserves the accuracy and confidentiality of data while reducing the safety risks of IoT-driven healthcare applications.

Keywords: internet of things, healthcare, sensors, electronic health records, blockchain, proxy re-encryption, data privacy, data security

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Authors: Nathainail Bashir, Neil Anderson

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The objective of this study site was to investigate the current state of the practice with regards to karst detection methods and recommend the best method and pattern of arrays to acquire the desire results. Proper site investigation in karst prone regions is extremely valuable in determining the location of possible voids. Two geophysical techniques were employed: multichannel analysis of surface waves (MASW) and electric resistivity tomography (ERT).The MASW data was acquired at each test location using different array lengths and different array orientations (to increase the probability of getting interpretable data in karst terrain). The ERT data were acquired using a dipole-dipole array consisting of 168 electrodes. The MASW data was interpreted (re: estimated depth to physical top of rock) and used to constrain and verify the interpretation of the ERT data. The ERT data indicates poorer quality MASW data were acquired in areas where there was significant local variation in the depth to top of rock.

Keywords: dipole-dipole, ERT, Karst terrains, MASW

Procedia PDF Downloads 315

Authors: H. W. Meerveld, R. H. A. Lindelauf

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In contemporary warfare, data science is crucial for the military in achieving information superiority. Yet, to the authors’ knowledge, no extensive literature survey on data science in military decision-making has been conducted so far. In this study, 156 peer-reviewed articles were analysed through an integrative, semi-systematic literature review to gain an overview of the topic. The study examined to what extent literature is focussed on the opportunities or risks of data science in military decision-making, differentiated per level of war (i.e. strategic, operational, and tactical level). A relatively large focus on the risks of data science was observed in social science literature, implying that political and military policymakers are disproportionally influenced by a pessimistic view on the application of data science in the military domain. The perceived risks of data science are, however, hardly addressed in formal science literature. This means that the concerns on the military application of data science are not addressed to the audience that can actually develop and enhance data science models and algorithms. Cross-disciplinary research on both the opportunities and risks of military data science can address the observed research gaps. Considering the levels of war, relatively low attention for the operational level compared to the other two levels was observed, suggesting a research gap with reference to military operational data science. Opportunities for military data science mostly arise at the tactical level. On the contrary, studies examining strategic issues mostly emphasise the risks of military data science. Consequently, domain-specific requirements for military strategic data science applications are hardly expressed. Lacking such applications may ultimately lead to a suboptimal strategic decision in today’s warfare.

Keywords: data science, decision-making, information superiority, literature review, military

Procedia PDF Downloads 167

Authors: Cai Qianyi

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In the midst of global digital revolution, the flow of data poses security threats that call China's existing legislative framework for protecting personal information into question. As a preliminary procedure for risk analysis and prevention, the risk assessment of personal data transmission lacks detailed guidelines for support. Existing provisions reveal unclear responsibilities for network operators and weakened rights for data subjects. Furthermore, the regulatory system's weak operability and a lack of industry self-regulation heighten data transmission hazards. This paper aims to compare the regulatory pathways for data information transmission risks between China and Europe from a legal framework and content perspective. It draws on the “Data Protection Impact Assessment Guidelines” to empower multiple stakeholders, including data processors, controllers, and subjects, while also defining obligations. In conclusion, this paper intends to solve China's digital security shortcomings by developing a more mature regulatory framework and industry self-regulation mechanisms, resulting in a win-win situation for personal data protection and the development of the digital economy.

Keywords: personal information data transmission, risk assessment, DPIA, internet service provider, personal information data transimission, risk assessment

Procedia PDF Downloads 60

Authors: Habiba Ben Abdessalem

Abstract:

The emergence of giant set of textual data was the push that has encouraged researchers to invest in this field. The purpose of textual data analysis methods is to facilitate access to such type of data by providing various graphic visualizations. Applying these methods requires a corpus pretreatment step, whose standards are set according to the objective of the problem studied. This step determines the forms list contained in contingency table by keeping only those information carriers. This step may, however, lead to noisy contingency tables, so the use of wavelet denoising function. The validity of the proposed approach is tested on a text database that offers economic and political events in Tunisia for a well definite period.

Keywords: textual data, wavelet, denoising, contingency table

Procedia PDF Downloads 277

Authors: H. S. Niroshani, W. M. Ediri Arachchi, R. Tudugala, U. J. M. A. L. Jayasinghe, U. M. U. J. Jayasekara, P. B. Hewavithana

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Mammography is used as a screening tool for early diagnosis of breast cancer. It is also useful in refining the diagnosis of breast cancer either by assessment or work up after a suspicious area in the breast has been detected. In order to detect breast cancer accurately and at the earliest possible stage, the image must have an optimum contrast to reveal mass densities and spiculated fibrous structures radiating from them. In addition, the spatial resolution must be adequate to reveal the suffusion of micro calcifications and their shape. The above factors can be optimized by implementing an effective QA programme to enhance the accurate diagnosis of mammographic imaging. Therefore, the radiographer’s knowledge on QA is greatly instrumental in routine mammographic practice. The aim of this study was to assess the radiographer’s knowledge on Quality Assurance and Quality Control programmes in relation to mammographic procedures. A cross-sectional study was carried out among all radiographers working in each mammography setting in Sri Lanka. Pre-tested, anonymous self-administered questionnaires were circulated among the study population and duly filled questionnaires returned within a period of three months were taken into the account. The data on demographical information, knowledge on QA programme and associated QC tests, overall knowledge on QA and QC programmes were obtained. Data analysis was performed using IBM SPSS statistical software (version 20.0). The total response rate was 59.6% and the average knowledge score was 54.15±11.29 SD out of 100. Knowledge was compared on the basis of education level, special training of mammography, and the years of working experience in a mammographic setting of the individuals. Out of 31 subjects, 64.5% (n=20) were graduate radiographers and 35.5% (n=11) were diploma holders while 83.9% (n=26) of radiographers have been specially trained for mammography and 16.1% (n=5) have not been attended for any special training for mammography. It is also noted that 58.1% (n=18) of individuals possessed their experience of less than one year and rest 41.9% (n=13) of them were greater than that. Further, the results found that there is a significant difference (P < 0.05) in the knowledge of QA and overall knowledge on QA and QC programme in the categories of education level and working experience. Also, results imply that there was a significant difference (P < 0.05) in the knowledge of QC test among the groups of trained and non-trained radiographers. This study reveals that education level, working experience and the training obtained particularly in the field of mammography have a significant impact on their knowledge on QA and QC in mammography.

Keywords: knowledge, mammography, quality assurance, quality control

Procedia PDF Downloads 330

Authors: Burcu Oralhan, Zeki Oralhan, Nilsun Sariyer, Kumru Uyar

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Data mining has been becoming more and more important and a wide range of applications in recent years. Data mining is the process of find hidden and unknown patterns in big data. One of the applied fields of data mining is Customer Relationship Management. Understanding the relationships between products and customers is crucial for every business. Customer Relationship Management is an approach to focus on customer relationship development, retention and increase on customer satisfaction. In this study, we made an application of a data mining methods in telecommunication customer relationship management side. This study aims to determine the customers profile who likely to leave the system, develop marketing strategies, and customized campaigns for customers. Data are clustered by applying classification techniques for used to determine the churners. As a result of this study, we will obtain knowledge from international telecommunication industry. We will contribute to the understanding and development of this subject in Customer Relationship Management.

Keywords: customer churn analysis, customer relationship management, data mining, telecommunication industry

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Authors: N. R. N. Idris, S. Baharom

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A meta-analysis may be performed using aggregate data (AD) or an individual patient data (IPD). In practice, studies may be available at both IPD and AD level. In this situation, both the IPD and AD should be utilised in order to maximize the available information. Statistical advantages of combining the studies from different level have not been fully explored. This study aims to quantify the statistical benefits of including available IPD when conducting a conventional summary-level meta-analysis. Simulated meta-analysis were used to assess the influence of the levels of data on overall meta-analysis estimates based on IPD-only, AD-only and the combination of IPD and AD (mixed data, MD), under different study scenario. The percentage relative bias (PRB), root mean-square-error (RMSE) and coverage probability were used to assess the efficiency of the overall estimates. The results demonstrate that available IPD should always be included in a conventional meta-analysis using summary level data as they would significantly increased the accuracy of the estimates. On the other hand, if more than 80% of the available data are at IPD level, including the AD does not provide significant differences in terms of accuracy of the estimates. Additionally, combining the IPD and AD has moderating effects on the biasness of the estimates of the treatment effects as the IPD tends to overestimate the treatment effects, while the AD has the tendency to produce underestimated effect estimates. These results may provide some guide in deciding if significant benefit is gained by pooling the two levels of data when conducting meta-analysis.

Keywords: aggregate data, combined-level data, individual patient data, meta-analysis

Procedia PDF Downloads 375

Authors: Adil Mustafa, Ahmet Erten, Alper Kiraz, Melikhan Tanyeri

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Work presented here is based on a novel experimental technique used to hydrodynamically trap oil microdroplets inside a microfluidic chip at the junction of microchannels known as stagnation point. Hydrodynamic trapping has been recently used to trap and manipulate a number of particles starting from microbeads to DNA and single cells. Benzyl Benzoate (BB) is used as droplet material. The microdroplets are trapped individually at stagnation point and their dissolution was observed. Experiments are performed for two concentrations (10mM or 10µM) of AOT surfactant (Docusate Sodium Salt) and two flow rates for each case. Moreover, experimental data is compared with Zhang-Yang-Mao (ZYM) model which studies dissolution of liquid microdroplets in the presence of a host fluid experiencing extensional creeping flow. Industrial processes like polymer blending systems in which heat or mass transport occurs experience extensional flow and an insight into these phenomena is of significant importance to many industrial processes. The experimental technique exploited here gives an insight into the dissolution of liquid microdroplets under extensional flow regime. The comparison of our experimental results with ZYM model reveals that dissolution of microdroplets at lower surfactant concentration (10µM) fits the ZYM model at saturation concentration (Cs) value reported in literature (Cs = 15×10⁻³Kg\m³) while for higher surfactant concentration (10mM) which is also above the critical micelle concentration (CMC) of surfactant (5mM) the data fits ZYM model at (Cs = 45×10⁻³Kg\m³) which is 3X times the value reported in literature. The difference in Cs value from the literature shows enhancement in dissolution rate of sparingly soluble BB microdroplets at surfactant concentrations higher than CMC. Enhancement in the dissolution of sparingly soluble materials is of great importance in pharmaceutical industry. Enhancement in the dissolution of sparingly soluble drugs is a key research area for drug design industry. The experimental method is also advantageous because it is robust and has no mechanical contact with droplets under study are freely suspended in the fluid as compared existing methods used for testing dissolution of drugs. The experiments also give an insight into CMC measurement for surfactants.

Keywords: extensional flow, hydrodynamic trapping, Zhang-Yang-Mao, CMC

Procedia PDF Downloads 345

Authors: Hyun-Woo Cho

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The monitoring of industrial production quality has to be implemented to alarm early warning for unusual operating conditions. Furthermore, identification of their assignable causes is necessary for a quality control purpose. For such tasks many multivariate statistical techniques have been applied and shown to be quite effective tools. This work presents a process data-based monitoring scheme for production processes. For more reliable results some additional steps of noise filtering and preprocessing are considered. It may lead to enhanced performance by eliminating unwanted variation of the data. The performance evaluation is executed using data sets from test processes. The proposed method is shown to provide reliable quality control results, and thus is more effective in quality monitoring in the example. For practical implementation of the method, an on-line data system must be available to gather historical and on-line data. Recently large amounts of data are collected on-line in most processes and implementation of the current scheme is feasible and does not give additional burdens to users.

Keywords: detection, filtering, monitoring, process data

Procedia PDF Downloads 559

Authors: Ali Momeni

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Visual media has had a significant impact on the mental structure and behaviors of humanity. One interactive platform that has played a major role in this is Instagram. In Islamic countries, particularly Iran, many Muslims have embraced this interactive media platform for various reasons. Instagram has also provided an opportunity for individuals to become famous and gain micro-celebrity status through its semi-algorithmic features. A notable group of Iranian women who have gained fame through Instagram are religious Muslim women who have transitioned into bloggers. These Iranian religious women bloggers (IRWB) have garnered a large following by showcasing different models of hijab and their private lives. This research aims to qualitatively study the representation of femininity and religiosity of these women. The main question addressed in this study is the acceptance of Instagram activity by IRWB among religious women. Drawing on concepts such as 'The Society of the Spectacle' and 'Celebrity Online', this study utilized the netnography method to analyze 14 pages of IRWB. Data was collected in two phases, with the first phase involving the analysis of religious women's comments on posts related to these themes. The second phase included interviews with religious women students who view or follow these pages. A total of 120 comments and 14 interviews were thematically analyzed. The results revealed that the reception of these pages by religious women fell into four main themes: the spectacle of femininity, the commercialization of religiosity, the distortion of Islam, and the construction of religiosity and femininity. Ultimately, religious women did not find these pages to be reflective of their own experiences of female and religious life.

Keywords: women, bloggers, instagram, IRWB, reception.

Procedia PDF Downloads 75

Authors: Muhammad Awais Shafique, Eiji Hato

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Household trip data is of crucial importance for managing present transportation infrastructure as well as to plan and design future facilities. It also provides basis for new policies implemented under Transportation Demand Management. The methods used for household trip data collection have changed with passage of time, starting with the conventional face-to-face interviews or paper-and-pencil interviews and reaching to the recent approach of employing smartphones. This study summarizes the step-wise evolution in the travel data collection methods. It provides a comprehensive review of the topic, for readers interested to know the changing trends in the data collection field.

Keywords: computer, smartphone, telephone, travel survey

Procedia PDF Downloads 313

Authors: Zeev Wiesman, Paula Berman, Nitzan Meiri, Charles Linder

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Supramolecular chemistry refers to the domain of chemistry beyond that of molecules and focuses on the chemical systems made up of a discrete number of assembled molecular sub units or components. Biodiesel components self arrangements is closely related/affect their physical properties in combustion systems and emission. Due to technological difficulties, knowledge regarding the molecular packing of FAMEs (biodiesel) in the liquid phase is limited. Spectral tools such as X-ray and NMR are known to provide evidences related to molecular structure organization. Recently, it was reported by our research group that using 1H Time Domain NMR methodology based on relaxation time and self diffusion coefficients, FAMEs clusters with different motilities can be accurately studied in the liquid phase. Head to head dimarization with quasi-smectic clusters organization, based on molecular motion analysis, was clearly demonstrated. These findings about the assembly/packing of the FAME components are directly associated with fluidity/viscosity of the biodiesel. Furthermore, these findings may provide information of micro/nano-particles that are formed in the delivery and injection system of various combustion systems (affected by thermodynamic conditions). Various relevant parameters to combustion such as: distillation/Liquid Gas phase transition, cetane number/ignition delay, shoot, oxidation/NOX emission maybe predicted. These data may open the window for further optimization of FAME/diesel mixture in terms of combustion and emission.

Keywords: supermolecular chemistry, FAMEs, liquid phase, fluidity, LF-NMR

Procedia PDF Downloads 341

Authors: Hiroaki Nasu, Ryota Miyamoto, Yuta Kodera, Yasuyuki Nogami

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To promote Industry 4.0 and Society 5.0 and so on, it is important to connect and share data so that every member can trust it. Blockchain (BC) technology is currently attracting attention as the most advanced tool and has been used in the financial field and so on. However, the data collaboration using BC has not progressed sufficiently among companies on the supply chain of manufacturing industry that handle sensitive data such as product quality, manufacturing conditions, etc. There are two main reasons why data utilization is not sufficiently advanced in the industrial supply chain. The first reason is that manufacturing information is top secret and a source for companies to generate profits. It is difficult to disclose data even between companies with transactions in the supply chain. In the blockchain mechanism such as Bitcoin using PKI (Public Key Infrastructure), in order to confirm the identity of the company that has sent the data, the plaintext must be shared between the companies. Another reason is that the merits (scenarios) of collaboration data between companies are not specifically specified in the industrial supply chain. For these problems this paper proposes a Business to Business (B2B) collaboration system using homomorphic encryption and BC technique. Using the proposed system, each company on the supply chain can exchange confidential information on encrypted data and utilize the data for their own business. In addition, this paper considers a scenario focusing on quality data, which was difficult to collaborate because it is a top secret. In this scenario, we show a implementation scheme and a benefit of concrete data collaboration by proposing a comparison protocol that can grasp the change in quality while hiding the numerical value of quality data.

Keywords: business to business data collaboration, industrial supply chain, blockchain, homomorphic encryption

Procedia PDF Downloads 136

Authors: Ali Rashash Alzahrani, Elizabeth Stojanovski

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Data on various aspects of education are collected at the institutional and government level regularly. In Australia, for example, students at various levels of schooling undertake examinations in numeracy and literacy as part of NAPLAN testing, enabling longitudinal assessment of such data as well as comparisons between schools and states within Australia. Another source of educational data collected internationally is via the PISA study which collects data from several countries when students are approximately 15 years of age and enables comparisons in the performance of science, mathematics and English between countries as well as ranking of countries based on performance in these standardised tests. As well as student and school outcomes based on the tests taken as part of the PISA study, there is a wealth of other data collected in the study including parental demographics data and data related to teaching strategies used by educators. Overall, an abundance of educational data is available which has the potential to be used to help improve educational attainment and teaching of content in order to improve learning outcomes. A multivariate assessment of such data enables multiple variables to be considered simultaneously and will be used in the present study to help develop profiles of students based on performance in mathematics using data obtained from the PISA study.

Keywords: cluster analysis, education, mathematics, profiles

Procedia PDF Downloads 126

Authors: Sakda Loetpiparwanich, Preecha Vichitthamaros

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Nowadays, poor data quality is considered one of the majority costs for a data project. The data project with data quality awareness almost as much time to data quality processes while data project without data quality awareness negatively impacts financial resources, efficiency, productivity, and credibility. One of the processes that take a long time is defining the expectations and measurements of data quality because the expectation is different up to the purpose of each data project. Especially, big data project that maybe involves with many datasets and stakeholders, that take a long time to discuss and define quality expectations and measurements. Therefore, this study aimed at developing meaningful indicators to describe overall data quality for each dataset to quick comparison and priority. The objectives of this study were to: (1) Develop a practical data quality indicators and measurements, (2) Develop data quality dimensions based on statistical characteristics and (3) Develop Composite Indicator that can describe overall data quality for each dataset. The sample consisted of more than 500 datasets from public sources obtained by random sampling. After datasets were collected, there are five steps to develop the Dataset Quality Index (SDQI). First, we define standard data quality expectations. Second, we find any indicators that can measure directly to data within datasets. Thirdly, each indicator aggregates to dimension using factor analysis. Next, the indicators and dimensions were weighted by an effort for data preparing process and usability. Finally, the dimensions aggregate to Composite Indicator. The results of these analyses showed that: (1) The developed useful indicators and measurements contained ten indicators. (2) the developed data quality dimension based on statistical characteristics, we found that ten indicators can be reduced to 4 dimensions. (3) The developed Composite Indicator, we found that the SDQI can describe overall datasets quality of each dataset and can separate into 3 Level as Good Quality, Acceptable Quality, and Poor Quality. The conclusion, the SDQI provide an overall description of data quality within datasets and meaningful composition. We can use SQDI to assess for all data in the data project, effort estimation, and priority. The SDQI also work well with Agile Method by using SDQI to assessment in the first sprint. After passing the initial evaluation, we can add more specific data quality indicators into the next sprint.

Keywords: data quality, dataset quality, data quality management, composite indicator, factor analysis, principal component analysis

Procedia PDF Downloads 139

Authors: Ameur Abdelkader, Abed Bouarfa Hafida

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Since its inception, predictive analysis has revolutionized the IT industry through its robustness and decision-making facilities. It involves the application of a set of data processing techniques and algorithms in order to create predictive models. Its principle is based on finding relationships between explanatory variables and the predicted variables. Past occurrences are exploited to predict and to derive the unknown outcome. With the advent of big data, many studies have suggested the use of predictive analytics in order to process and analyze big data. Nevertheless, they have been curbed by the limits of classical methods of predictive analysis in case of a large amount of data. In fact, because of their volumes, their nature (semi or unstructured) and their variety, it is impossible to analyze efficiently big data via classical methods of predictive analysis. The authors attribute this weakness to the fact that predictive analysis algorithms do not allow the parallelization and distribution of calculation. In this paper, we propose to extend the predictive analysis algorithm, Classification And Regression Trees (CART), in order to adapt it for big data analysis. The major changes of this algorithm are presented and then a version of the extended algorithm is defined in order to make it applicable for a huge quantity of data.

Keywords: predictive analysis, big data, predictive analysis algorithms, CART algorithm

Procedia PDF Downloads 142

Authors: Agata Zakrzewska, Dominik Kopeć, Adrian Ochtyra

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The growing number of new cameras, sensors, and research methods allow for a broader application of thermal data in remote sensing vegetation studies. The aim of this research was to check whether it is possible to use thermal infrared data with a spectral range (3.6-4.9 μm) obtained during the day and the night to assess the health condition of selected species of deciduous trees in an urban environment. For this purpose, research was carried out in the city center of Warsaw (Poland) in 2020. During the airborne data acquisition, thermal data, laser scanning, and orthophoto map images were collected. Synchronously with airborne data, ground reference data were obtained for 617 studied species (Acer platanoides, Acer pseudoplatanus, Aesculus hippocastanum, Tilia cordata, and Tilia × euchlora) in different health condition states. The results were as follows: (i) healthy trees are cooler than trees in poor condition and dying both in the daytime and nighttime data; (ii) the difference in the canopy temperatures between healthy and dying trees was 1.06oC of mean value on the nighttime data and 3.28oC of mean value on the daytime data; (iii) condition classes significantly differentiate on both daytime and nighttime thermal data, but only on daytime data all condition classes differed statistically significantly from each other. In conclusion, the aerial thermal data can be considered as an alternative to hyperspectral data, a method of assessing the health condition of trees in an urban environment. Especially data obtained during the day, which can differentiate condition classes better than data obtained at night. The method based on thermal infrared and laser scanning data fusion could be a quick and efficient solution for identifying trees in poor health that should be visually checked in the field.

Keywords: middle wave infrared, thermal imagery, tree discoloration, urban trees

Procedia PDF Downloads 115

Authors: Noriyuki Suyama

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This paper looked into consumer behavioral changes by analyzing the data collected by ASMARKS Co., one of a research companies in Japan. The purpose of the paper is to understand the two differences of before vs. after COVID-19 pandemic and Japanese living in Japan. Subsequently, examining the analysis results helped obtain useful insights into new business models for business parties in Japan as a microlevel perspective. The paper also tried to explore future conditions of globalization by taking into consideration nation’s political and economic changes as a macro-level perspective. The COVID-19 has been continuing its spread across the world with more than 60 million confirmed cases in 190 countries. This pandemic with restricted scopes of behavior mandates have disrupted the consumer habits of their lifestyles. Consumers have tendency to learn new ways when they have trouble in taking routine action. For example, the government forces people to refrain from going out, they try to telecommute at home. If the situation come back to normal, people still change their lifestyles to fit in the best. Some of data show typical effects of COVID-19; forceful exposure to digitalized work-life styles; more flexible time at home; importance of trustful and useful information gathering between what's good and bad;etc. in comparison with before vs. after COVID-19 pandemic. In addition, Japanese have less changed their lifestyles than Expatriates living in Japan. For example, while 94% of the expatriates have decreased their outgo because of self-quarantine, only 55% of the Japanese have done. There are more differences in both comparisons in the analysis results. The economic downtrend resulting from COVID-19 is supposed to be at least as devastating if not more so than that of the financial crisis. With unemployment levels in the US taking two weeks to reach what took 6 months in the 2008 crisis, there is no doubt of a global recession some predict could reach 10% or above of GDP. As a result, globalization in the global supply chain of goods and services will end up with negative impact. A lot of governmental financial and economic policies are supposed to focus on their own profits and interests, exclusing other countries interests as is the case with the Recovery Act just after the global financial crisis from 2007 to 2008. Both micro- and macro-levels analysis successfully reveal important connotations and managerial implications of business in Japan for Japanese consumers as well as after COVID-19 global business.

Keywords: COVID-19, lifestyle in Japan, expatriates, consumer behavior

Procedia PDF Downloads 140

Authors: Milton Ayoki, Edward Bbaale

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This paper analyses the relationship between innovation and employment at firm level with the objective of understanding the contribution of the different innovation strategies in fostering employment growth in Uganda. We use National Innovation Survey (micro-data of 705 Ugandan firms) for the period 2011-2014 and follow closely Harrison et al. (2014) structured approach, and relate employment growth to process innovations and to the growth of sales separately due to innovative and unchanged products. We find positive effects of product innovation on employment at firm level, while process innovation has no discernable impact on employment. Although there is evidence to suggest displacement of labour in some cases where firms only introduce new process, this effect is compensated by growth in employment from new products, which for most firms are introduced simultaneously with new process. Results suggest that source of innovation as well as size of innovating firms or end users of innovation matter for job growth. Innovation that develops from within the firm itself (user) and involving larger firms has greater impact on employment than that developed from outside or coming from within smaller firms. In addition, innovative firms are one and half times more likely to survive in the innovation driven economy environment than those that do not innovate. These results have important implications for policymakers and stakeholders in innovation ecosystem. Supporting policies need to be correctly tailored since the impacts depend on the innovation strategy (type) and characteristics and sector of the innovative firms (small, large, industry, etc.). Policies to spur investment, particularly in innovative sectors and firms with high growth potential would have long lasting effects on job creation. JEL Classification: D24, J0, J20, L20, O30.

Keywords: employment, process innovation, product innovation, Sub-Saharan Africa

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Authors: Z. Abdullah, A. R. Hamdan

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Clustering is a process of grouping objects and data into groups of clusters to ensure that data objects from the same cluster are identical to each other. Clustering algorithms in one of the areas in data mining and it can be classified into partition, hierarchical, density based, and grid-based. Therefore, in this paper, we do a survey and review for four major hierarchical clustering algorithms called CURE, ROCK, CHAMELEON, and BIRCH. The obtained state of the art of these algorithms will help in eliminating the current problems, as well as deriving more robust and scalable algorithms for clustering.

Keywords: clustering, unsupervised learning, algorithms, hierarchical

Procedia PDF Downloads 885

Authors: Syed Kashif Ali, Usman Javaid, Abdullah Chohan

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In large enterprises multiple departments use different sort of information systems and databases according to their needs. These systems are independent and heterogeneous in nature and sharing information/data between these systems is not an easy task. The usage of middleware technologies have made data sharing between systems very easy. However, monitoring the exchange of data/information for verification purposes between target and source systems is often complex or impossible for maintenance department due to security/access privileges on target and source systems. In this paper, we are intended to present our experience of an end to end data monitoring approach at middle ware level implemented in Oracle BPEL for data verification without any help of monitoring tool.

Keywords: service level agreement, SOA, BPEL, oracle fusion middleware, web service monitoring

Procedia PDF Downloads 480

Authors: K. Umbleja, M. Ichino

Abstract:

Symbolic data mining has been developed to analyze data in very large datasets. It is also useful in cases when entry specific details should remain hidden. Symbolic data mining is quickly gaining popularity as datasets in need of analyzing are becoming ever larger. One type of such symbolic data is a histogram, which enables to save huge amounts of information into a single variable with high-level of granularity. Other types of symbolic data can also be described in histograms, therefore making histogram a very important and general symbolic data type - a method developed for histograms - can also be applied to other types of symbolic data. Due to its complex structure, analyzing histograms is complicated. This paper proposes a method, which allows to compare two histogram-valued variables and therefore find a dissimilarity between two histograms. Proposed method uses the Ichino-Yaguchi dissimilarity measure for mixed feature-type data analysis as a base and develops a dissimilarity measure specifically for histogram data, which allows to compare histograms with different number of bins and bin widths (so called general histogram). Proposed dissimilarity measure is then used as a measure for clustering. Furthermore, linkage method based on weighted averages is proposed with the concept of cluster compactness to measure the quality of clustering. The method is then validated with application on real datasets. As a result, the proposed dissimilarity measure is found producing adequate and comparable results with general histograms without the loss of detail or need to transform the data.

Keywords: dissimilarity measure, hierarchical clustering, histograms, symbolic data analysis

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Authors: Majid Mokhtarnia, Alireza Amini

Abstract:

Mobile operators deal with increasing in the data traffic as a critical issue. As a result, a vital responsibility of the operators is to deal with such a trend in order to create added values. This paper addresses a bundling method in a Canvas business model in a WiFi Data Offloading (WDO) strategy by which some elements of the model may be affected. In the proposed method, it is supposed to sell a number of data packages for subscribers in which there are some packages with a free given volume of data-offloaded WiFi complimentary. The paper on hands analyses this method in the views of attractiveness and profitability. The results demonstrate that the quality of implementation of the WDO strongly affects the final result and helps the decision maker to make the best one.

Keywords: bundling, canvas business model, telecommunication, WiFi data offloading

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Authors: M. Derradji, W. B. Liu

Abstract:

The current work discusses the effects of adding various types of ceramic fillers on the curing behavior, thermal, mechanical, anticorrosion, and UV shielding properties of the bisphenol-A based phthalonitrile resins. The effects of different ceramic filler contents and sizes as well as their surface treatments are also discussed in terms of their impact on the morphology and mechanisms of enhancement. The synergistic effect obtained by these combinations extends the use of the phthalonitrile resins to more exigent applications such as aerospace and military. The presented results reveal the significant advantages that can be obtained from the preparation of hybrid materials based on phthalonitrile resins and open the way for further research in the field.

Keywords: mechanical properties, particle reinforced composites, polymer matrix composites (PMCs), thermal properties

Procedia PDF Downloads 155

Authors: Nathalie Piquemal, Faiçal Zellama, Bathélemy Bolivar, Leyla Sall

Abstract:

Canada defines itself as a country of immigration and a multicultural nation, ideologically, politically and programmatically (in terms of its integration practices). As such, principles of hospitality may seem, at first glance, incontestable, given the convergence of the views of the majority of Canadian politicians on the need to welcome, each year, a significant number of immigrants and to offer them the hospitality that facilitates their transition to Canadian citizenship. However, immigrants are welcomed in a Canadian societal context in which power and resources are unevenly distributed, resulting in complex social relationships between hosts and newcomers. Qualitative data obtained from newcomers in Winnipeg, Manitoba, Canada, focuses on experiences of hospitality, with special attention to host-guest social and power dynamics, contested policies on foreign credential and micro spaces of belongingin a multicultural context. The act of welcoming a newcomer is inherently shaped by both macropolitical structures and everyday relational practices that can lead to experiences of belonging, marginalisation, empowerment and/or disempowerment depending on economic agenda, humanitarian and humanistic orientations. We first explore the extent to which immigrants experience hospitality in relation to unequal distribution of power and resources as well as cultural discontinuities. We then examine ways in which immigrants have been able to find sanctuaries of hospitality within their own ethnocultural communities. Finally, we discuss the complexity of hospitality in a multicultural context and offer critical insights on host factors that may produce, develop and nurture hospitable environments.

Keywords: migration, hospitality, diversity, culture, race

Procedia PDF Downloads 123

Authors: Tak-Chung Fu, Ying-Kit Hung, Fu-Lai Chung

Abstract:

In the field of time series data mining, the concept of the Perceptually Important Point (PIP) identification process is first introduced in 2001. This process originally works for financial time series pattern matching and it is then found suitable for time series dimensionality reduction and representation. Its strength is on preserving the overall shape of the time series by identifying the salient points in it. With the rise of Big Data, time series data contributes a major proportion, especially on the data which generates by sensors in the Internet of Things (IoT) environment. According to the nature of PIP identification and the successful cases, it is worth to further explore the opportunity to apply PIP in time series ‘Big Data’. However, the performance of PIP identification is always considered as the limitation when dealing with ‘Big’ time series data. In this paper, two distributed versions of PIP identification based on the Specialized Binary (SB) Tree are proposed. The proposed approaches solve the bottleneck when running the PIP identification process in a standalone computer. Improvement in term of speed is obtained by the distributed versions.

Keywords: distributed computing, performance analysis, Perceptually Important Point identification, time series data mining

Procedia PDF Downloads 433

Authors: Riddhiman Sherlekar, Umang Paladia, Rachit Desai, Yash Patel

Abstract:

A number of surfactants which exhibit ultra-low interfacial tension and an excellent microemulsion phase behavior with crude oils of low to medium gravity are not sufficiently soluble at optimum salinity to produce stable aqueous solutions. Such solutions often show phase separation after a few days at reservoir temperature, which does not suffice the purpose and the time is short when compared to the residence time in a reservoir for a surfactant flood. The addition of polymer often exacerbates the problem although the poor stability of the surfactant at high salinity remains a pivotal issue. Surfactants such as SDS, Ctab with large hydrophobes produce lowest IFT, but are often not sufficiently water soluble at desired salinity. Hydrophilic co-solvents and/or co-surfactants are needed to make the surfactant-polymer solution stable at the desired salinity. This study focuses on contrasting the effect of addition of a co-solvent in stability of a surfactant –oil emulsion. The idea is to use a co-surfactant to increase stability of an emulsion. Stability of the emulsion is enhanced because of creation of micro-emulsion which is verified both visually and with the help of particle size analyzer at varying concentration of salinity, surfactant and co-surfactant. A lab-experimental method description is provided and the method is described in detail to permit readers to emulate all results. The stability of the oil-water emulsion is visualized with respect to time, temperature, salinity of the brine and concentration of the surfactant. Nonionic surfactant TX-100 when used as a co-surfactant increases the stability of the oil-water emulsion. The stability of the prepared emulsion is checked by observing the particle size distribution. For stable emulsion in volume% vs particle size curve, the peak should be obtained for particle size of 5-50 nm while for the unstable emulsion a bigger sized particles are observed. The UV-Visible spectroscopy is also used to visualize the fraction of oil that plays important role in the formation of micelles in stable emulsion. This is important as the study will help us to decide applicability of the surfactant based EOR method for a reservoir that contains a specific type of crude. The use of nonionic surfactant as a co-surfactant would also increase the efficiency of surfactant EOR. With the decline in oil discoveries during the last decades it is believed that EOR technologies will play a key role to meet the energy demand in years to come. Taking this into consideration, the work focuses on the optimization of the secondary recovery(Water flooding) with the help of surfactant and/or co-surfactants by creating desired conditions in the reservoir.

Keywords: co-surfactant, enhanced oil recovery, micro-emulsion, surfactant flooding

Procedia PDF Downloads 251