Search results for: 3D plant data

Authors: Yaara Oppenheimer-Shaanan, Nimrod Nachmias, Marina Campos Rocha, Neta Schlezinger, Noam Dotan, Asaf Levy

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Fusarium oxysporum, a fungus that attacks a broad range of plants and can cause infections in humans, operates across different kingdoms. This pathogen encounters varied conditions, such as temperature, pH, and nutrient availability, in plant and human hosts. The Fusarium oxysporum species complex, pervasive in soils globally, can affect numerous plants, including key crops like tomatoes and bananas. Controlling Fusarium infections can involve biocontrol agents that hinder the growth of harmful strains. Our research developed a computational method to identify toxin domains within a vast number of microbial genomes, leading to the discovery of nine distinct toxins capable of killing bacteria and fungi, including Fusarium. These toxins appear to function as enzymes, causing significant damage to cellular structures, membranes and DNA. We explored biological control using bacteria that produce polymorphic toxins, finding that certain bacteria, non-pathogenic to plants, offer a safe biological alternative for Fusarium management, as they did not harm macrophage cells or C. elegans. Additionally, we elucidated the 3D structures of two toxins with their protective immunity proteins, revealing their function as unique DNases. These potent toxins are likely instrumental in microbial competition within plant ecosystems and could serve as biocontrol agents to mitigate Fusarium wilt and related diseases.

Keywords: microbial toxins, antifungal, Fusarium oxysporum, bacterial-fungal intreactions

Procedia PDF Downloads 33

Authors: Alouache Souhila, Messai Yamina, Torres Carmen, Bakour Rabah

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Objective: It has often been reported an association between antibiotic resistance and virulence. However, resistance to quinolones seems to be an exception, it tends instead to be associated with an attenuation of virulence, particularly in clinical strains. The purpose of this study was to evaluate the potential virulence of 28 quinolone-resistant E. coli strains recovered from water at the inflow (n=16) and outflow (n=12) of an urban wastewater treatment plant (WWTP). Methods: E. coli isolates were selected on Tergitol-7 agar supplemented with 2µg/ml of ciprofloxacin, they were screened by PCR for 11 virulence genes related to Extraintestinal pathogenic E. coli (ExPEC): papC, papG, afa/draBC, sfa/foc, kpsMTII, iutA, iroN, hlyF, ompT, iss and traT. The phylogenetic groups were determined by PCR and clonal relationship was evaluated by ERIC-PCR. Results: Genotyping by ERIC-PCR showed 7 and 12 DNA profiles among strains of wastewater (inflow) and treated water (outflow), respectively. Strains were assigned to the following phylogenetic groups: B2 (n = 1, 3.5%), D (n = 3, 10.7%), B1 (n = 10, 35.7%.) and A (n = 14, 50%). A total of 8 virulence-associated genes were detected, traT (n=19, 67.8%), iroN (n= 16, 57 .1%), hlyF (n=15, 53 .5%), ompT (n=15, 53 .5%), iss (n=14, 50%), iutA (n=9, 32.1%) , sfa/foc (n=7, 25%) and kpsMTII (n=2, 7.1%). Combination of virulence factors allowed to define 16 virulence profiles. The pathotype APEC was observed in 17.8% (D=1, B1=4) and human ExPEC in 7% (B2=1, D=1) of strains. Conclusion: The study showed that quinolone-resistant E. coli strains isolated from wastewater and treated water in WWTP harbored virulence genes with the presence of APEC and human ExPEC strains.

Keywords: E. coli, quinolone-resistance, virulence, WWTP

Procedia PDF Downloads 449

Authors: Sook Ching Yee, Angela Siew Hoong Lee

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Big data technologies have become a trend to exploit business opportunities and provide valuable business insights through the analysis of big data. However, there are still many organizations that have yet to adopt big data technologies especially small and medium organizations (SME). This study uses the technology acceptance model (TAM) to look into several constructs in the TAM and other additional constructs which are positive affect, negative affect, organizational factor and motivational factor. The conceptual model proposed in the study will be tested on the relationship and influence of positive affect, negative affect, organizational factor and motivational factor towards the intention to use big data technologies to produce an outcome. Empirical research is used in this study by conducting a survey to collect data.

Keywords: big data technologies, motivational factor, negative affect, organizational factor, positive affect, technology acceptance model (TAM)

Procedia PDF Downloads 342

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

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Rhipe that integrates R and Hadoop environment made it possible to process and analyze massive amounts of data using a distributed processing environment. In this paper, we implemented multiple regression analysis using Rhipe with various data sizes of actual data. Experimental results for comparing the performance of our Rhipe with stats and biglm packages available on bigmemory, showed that our Rhipe was more fast than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases. We also compared the computing speeds of pseudo-distributed and fully-distributed modes for configuring Hadoop cluster. The results showed that fully-distributed mode was faster than pseudo-distributed mode, and computing speeds of fully-distributed mode were faster as the number of data nodes increases.

Keywords: big data, Hadoop, Parallel regression analysis, R, Rhipe

Procedia PDF Downloads 487

Authors: A. Alsoqeer

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The establishment, dry matter production and feeding value of six perennial grasses were measured over two growing seasons in a field experiments. The experiments were conducted at the Agricultural and Veterinary Medicine Research Station, Faculty of Agriculture and Veterinary Medicine, Qassim University, Kingdom of Saudi Arabia in 2009 and 2010 seasons. The six perennial grasses were: creeping bluegrass (Bothriochloa insculpta cv. Bisset), digit grass (Digitaria smutsi), Jarra digit grass (Digitaria milanjiana), panic (Panicum coloratum cv. Bambatsii), Sabi grass (Urochloa mosambicensis) and setaria (Setaria sphacelata cv. Kazungula). The experimental design used was a completely randomized block design with four replications. The results revealed significant differences among plant species of all agronomic characters and quality traits in the first year, while in the second year, plant species differed significantly for quality traits only. D. smutsi had a superior performance for all agronomic characters, however, it had the lowest values in protein content in the two years comparing with other genotypes. D. milanjiana and U. mosambicensis showed high values in dry matter yield and protein content in the first year, but showed a very poor performance in the second year because most of plants were die due to the low temperatures in the winter. These two species appear to be suitable for annual cultivation. The other species tolerate the cold winter and were a highly productive in the second year.

Keywords: dry mater yield, grass species, cuts, quality traits, crude protein content

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Authors: Mona Almansoori, Ahmed Mustafa, Ahmad Elshamy

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Wireless sensor network was formed by a combination of nodes, systematically it transmitting the data to their base stations, this transmission data can be easily compromised if the limited processing power and the data consistency from these nodes are kept in mind; there is always a discussion to address the secure data transfer or transmission in actual time. This will present a mechanism to securely transmit the data over a chain of sensor nodes without compromising the throughput of the network by utilizing available battery resources available in the sensor node. Our methodology takes many different advantages of Z-MAC protocol for its efficiency, and it provides a unique key by sharing the mechanism using neighbor node MAC address. We present a light weighted data integrity layer which is embedded in the Z-MAC protocol to prove that our protocol performs well than Z-MAC when we introduce the different attack scenarios.

Keywords: hybrid MAC protocol, data integrity, lightweight encryption, neighbor based key sharing, sensor node dataprocessing, Z-MAC

Procedia PDF Downloads 131

Authors: Madaki Umar Yusuf, Mohd Rizam B. Abubakar

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The survival censored data with incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. With model when the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM by the method of weights. The survival outcome for the class of generalized linear model is applied and this method requires the estimation of the parameters of the distribution of the covariates. In this paper, we propose some clinical trials with ve covariates, four of which have some missing values which clearly show that they were fully censored data.

Keywords: EM algorithm, incomplete categorical covariates, ignorable missing data, missing at random (MAR), Weibull Distribution

Procedia PDF Downloads 387

Authors: Abdeljalil Beniiche

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The limitation with smart contracts is that they cannot access external data that might be required to control the execution of business logic. Oracles can be used to provide external data to smart contracts. An oracle is an interface that delivers data from external data outside the blockchain to a smart contract to consume. Oracle can deliver different types of data depending on the industry and requirements. In this paper, we study and describe the widely used blockchain oracles. Then, we elaborate on his potential role, technical architecture, and design patterns. Finally, we discuss the human oracle and its key role in solving the truth problem by reaching a consensus about a certain inquiry and tasks.

Keywords: blockchain, oracles, oracles design, human oracles

Procedia PDF Downloads 107

Authors: Yasemin Kavdir, Ugur Gozel

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This study was conducted to determine the nematicidal activity of green walnut husk (GWH) and olive pomace (OP) extracts against root-knot nematode (Meloidogyne incognita). Aqueous extracts of GWH and OP were mixed with sandy loam soil at the rates of 0, 6,12,18,24, 60 and 120 ml kg-1. All pots were arranged in a randomized complete block design and replicated four times under controlled atmosphere conditions. Tomato seedlings were grown in sterilized soil then they were transplanted to pots. Inoculation was done by pouring the 20 ml suspension including 1000 M. incognita juvenile pot-1 into 3 cm deep hole made around the base of the plant root. Tomato root and shoot growth and nematode populations have been determined. In general, both GWH and OP extracts resulted in better growth parameters compared to the control plants. However, GWH extract was the most effective in improving growth parameters. Applications of 24 ml kg-1 OP extract enhanced plant growth compared to other OP treatments while 60 ml kg-1 application rate had the lowest nematode number and root galling. In this study, applications of GWH and OP extracts reduced the number of Meloidogyne incognita and root galling compared to control soils. Additionally GWH and OP extracts can be used safely for tomato growth. It could be concluded that OP and GWH extracts used as organic amendments showed promising nematicidal activity in the control of M. incognita. This research was supported by TUBİTAK Grant Number 214O422.

Keywords: olive pomace, green walnut husk, Meloidogyne incognita, tomato, soil, extract

Procedia PDF Downloads 171

Authors: Hyun Kyu Lee, Won Zin Oh, June Hyun Kim, Jin Hee Kim, Sang June Choi, Hak Soo Kim

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The oxidative dissolution of chromium oxide by manganese oxides including permanganate have been widely studied not only for the chemical decontamination of nuclear power plant, but also for the environmental control of the toxic chromate caused by naturally occurring manganese dioxide. However, little attention has been made for the spontaneous reductive decomposition of permanganate in the water, which is a competing reaction with the oxidation of the chromium oxide by permanganate. The objective of this study is to investigate the spontaneous reductive decomposition behavior of permanganate in the water, depending on the variation of acidity, temperature and concentration. Results of the experiments showed that the permanganate reductive decomposition product is manganese dioxide, and this reaction accompanies with the same molar amount of hydrogen ion consumption. Therefore, at the neutral condition (ex. potassium permanganate solution without acidic chemicals), the permanganate do not reduce by itself at any condition of temperature, concentration within the experimental range. From the results, we confirmed that the oxidation reaction for the permanganate reduction is the water oxidation that is accompanying the oxygen evolution. The experimental results on the reductive decomposition behavior of permanganate in the water also showed that the degree and rate of permanganate reduction increases with the temperature, acidity and concentration. The spontaneous decomposition of the permanganates obtained in the studies would become a good reference to select the operational condition, such as temperature, acidity and concentration, for the chemical decontamination of nuclear power plants.

Keywords: permanganate reduction, spontaneous decomposition, water oxidation, acidity, temperature, permanganate concentration, chemical decontamination, nuclear power plant

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Authors: Alhadi Bustaman, Soeganda Formalidin, Titin Siswantining

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DNA microarray technology is used to analyze thousand gene expression data simultaneously and a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been used for analyzing gene expression data. However, when analyzing very large and heterogeneous collections of gene expression data, conventional clustering methods often cannot produce a satisfactory solution. Biclustering algorithm has been used as an alternative approach to identifying structures from gene expression data. In this paper, we introduce a transform technique based on singular value decomposition to identify normalized matrix of gene expression data followed by Mixed-Clustering algorithm and the Lift algorithm, inspired in the node-deletion and node-addition phases proposed by Cheng and Church based on Agglomerative Hierarchical Clustering (AHC). Experimental study on standard datasets demonstrated the effectiveness of the algorithm in gene expression data.

Keywords: agglomerative hierarchical clustering (AHC), biclustering, gene expression data, lymphoma, singular value decomposition (SVD)

Procedia PDF Downloads 263

Authors: Ramya P, Lino Abraham Varghese, S. Bose

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By cloud storage services, the data can be stored in the cloud, and can be shared across multiple users. Due to the unexpected hardware/software failures and human errors, which make the data stored in the cloud be lost or corrupted easily it affected the integrity of data in cloud. Some mechanisms have been designed to allow both data owners and public verifiers to efficiently audit cloud data integrity without retrieving the entire data from the cloud server. But public auditing on the integrity of shared data with the existing mechanisms will unavoidably reveal confidential information such as identity of the person, to public verifiers. Here a privacy-preserving mechanism is proposed to support public auditing on shared data stored in the cloud. It uses group signatures to compute verification metadata needed to audit the correctness of shared data. The identity of the signer on each block in shared data is kept confidential from public verifiers, who are easily verifying shared data integrity without retrieving the entire file. But on demand, the signer of the each block is reveal to the owner alone. Group private key is generated once by the owner in the static group, where as in the dynamic group, the group private key is change when the users revoke from the group. When the users leave from the group the already signed blocks are resigned by cloud service provider instead of owner is efficiently handled by efficient proxy re-signature scheme.

Keywords: data integrity, dynamic group, group signature, public auditing

Procedia PDF Downloads 375

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

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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 145

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 39

Authors: Habiba Ben Abdessalem

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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 266

Authors: Jayanwita Sarkar, Usha Chakraborty, Bishwanath Chakraborty

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Plants are constantly interacting with various abiotic and biotic stresses. In changing climate scenario plants are continuously modifying physiological processes to adapt to changing environmental conditions which profoundly affect plant-pathogen interactions. Spot blotch in wheat is a fast-rising disease in the warmer plains of South Asia where the rise in minimum average temperature over most of the year already affecting wheat production. Hence, the study was undertaken to explore the role of elevated temperature in spot blotch disease development and modulation of antioxidative responses by plant growth promoting rhizobacteria (PGPR) for biocontrol of spot blotch at high temperature. Elevated temperature significantly increases the susceptibility of wheat plants to spot blotch causing pathogen Bipolaris sorokiniana. Two PGPR Bacillus safensis (W10) and Ochrobactrum pseudogrignonense (IP8) isolated from wheat (Triticum aestivum L.) and blady grass (Imperata cylindrical L.) rhizophere respectively, showing in vitro antagonistic activity against Bipolaris sorokiniana were tested for growth promotion and induction of resistance against spot blotch in wheat. GC-MS analysis showed that Bacillus safensis (W10) and Ochrobactrum pseudogrignonense (IP8) produced antifungal and antimicrobial compounds in culture. Seed priming with these two bacteria significantly increase growth, modulate antioxidative signaling and induce resistance and eventually reduce disease incidence in wheat plants at optimum as well as elevated temperature which was further confirmed by indirect immunofluorescence assay using polyclonal antibody raised against Bipolaris sorokiniana. Application of the PGPR led to enhancement in activities of plant defense enzymes- phenylalanine ammonia lyase, peroxidase, chitinase and β-1,3 glucanase in infected leaves. Immunolocalization of chitinase and β-1,3 glucanase in PGPR primed and pathogen inoculated leaf tissue was further confirmed by transmission electron microscopy using PAb of chitinase, β-1,3 glucanase and gold labelled conjugates. Activity of ascorbate-glutathione redox cycle related enzymes such as ascorbate peroxidase, superoxide dismutase and glutathione reductase along with antioxidants such as carotenoids, glutathione and ascorbate and osmolytes like proline and glycine betain accumulation were also increased during disease development in PGPR primed plant in comparison to unprimed plants at high temperature. Real-time PCR analysis revealed enhanced expression of defense genes- chalcone synthase and phenyl alanineammonia lyase. Over expression of heat shock proteins like HSP 70, small HSP 26.3 and heat shock factor HsfA3 in PGPR primed plants effectively protect plants against spot blotch infection at elevated temperature as compared with control plants. Our results revealed dynamic biochemical cross talk between elevated temperature and spot blotch disease development and furthermore highlight PGPR mediated array of antioxidative and molecular alterations responsible for induction of resistance against spot blotch disease at elevated temperature which seems to be associated with up-regulation of defense genes, heat shock proteins and heat shock factors, less ROS production, membrane damage, increased expression of redox enzymes and accumulation of osmolytes and antioxidants.

Keywords: antioxidative enzymes, defense enzymes, elevated temperature, heat shock proteins, PGPR, Real-Time PCR, spot blotch, wheat

Procedia PDF Downloads 152

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

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Authors: Hailemeleak Regassa

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Traditional medicines use medicinal plants as a source of ingredients, and many modern medications are indirectly derived from plants. Consumers in affluent nations are growing disenchanted with contemporary healthcare and looking for alternatives. Oxidative stress is the primary cause of multiple diseases, and exogenous antioxidant supplementation or strengthening the body's endogenous antioxidant defenses are potential ways to counteract the negative effects of oxidative damage. Plants can biosynthesize non-enzymatic antioxidants that can reduce ROS-induced oxidative damage. Aging often aids the propagation and development of carcinogenesis, and older animals and older people exhibit increased vulnerability to tumor promoters. Cancer is a major public health issue, with several anti-cancer medications in clinical use. Potential drugs such as flavopiridol, roscovitine, combretastatin A-4, betulinic acid, and silvestrol are in the clinical or preclinical stages of research. Methodology: Microbial Growth media, Dimethyl sulfoxide (DMSO), methanol, ethyl acetate, and n-hexane were obtained from Himedia Labs, Mumbai, India. plant were collected from the Herbal Garden of Shoolini University campus, Solan, India (Latitude - 30.8644° N and longitude - 77.1184° E). The identity was confirmed by Dr. Y.S. Parmar University of Horticulture and Forestry, Nauni, Solan (H.P.), India, and documented in Voucher specimens - UHF- Herbarium no. 13784; vide book no. 3818 Receipt No. 086. The plant materials were washed with tap water, and 0.1% mercury chloride for 2 minutes, rinsed with distilled water, air dried, and kept in a hot air oven at 40ºc on blotting paper until all the water evaporated and became well dried for grinding. After drying, the plant materials were grounded using a mixer grinder into fine powder transferred into airtight containers with proper labeling, and stored at 4ºc for future use (Horablaga et al., 2023). The extraction process was done according to Altemimi et al., 2017. The 5g powder was mixed with 15 ml of the respective solvents (n-hexane, ethyl acetate, and methanol), and kept for 4-5 days on the platform shaker. The solvents used are based on their increasing polarity index. Then the extract was centrifuged at 10,000rpm for 5 minutes and filtered using No.1 Whatman filter paper.

Keywords: cancer, phytomedicine, medicinal plants, oncology

Procedia PDF Downloads 49

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

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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 297

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

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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

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Authors: Chika C. Ogueke, Ijeoma M. Agunwah

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The antimicrobial activities and preservative potentials of crude extracts of Alstonia boonei stem bark and Euphorbia hirta leaves were studied. Soxhlet extraction and cold ethanol extraction methods were used for the extraction of the dried and ground plant samples. Well in agar diffusion method was used for the antimicrobial screening at different concentrations of 25mg/ml, 50mg/ml, 100mg/ml and 200mg/ml on E.coli and B.subtilis. The preservative effects of the extracts at 0.1%, 0.2% and 0.3% singly and in combination were determined in minced meat using E. coli and B. subtilis as test isolates. Phytochemical analysis was also conducted on the extracts using standard analytical methods. E.hirta cold and A.boonei cold extracts gave the highest zone of growth inhibition on E. coli and B.substilis with 20mm zone diameter at 200mg/ml concentration. Phytochemical analysis revealed the presence of alkaloids, flavonoids, tannins, saponins and cardiac glycosides. A.boonei at 0.1, 0.2 and 0.3% produced a log cycle reduction on the growth of E.coli. Mixture of A. boonei and E. hirta extracts (1:1) at 0.1% and 0.2% also produced a log cycle reduction on the growth of E.coli and B. subtilis, however the A. boonei extracts had more significant effect on the isolates. The observed antimicrobial activities are attributed to the phytochemicals identified in the extracts. The results reveal the potentials of plant extracts as natural antimicrobial preservatives in minced meat. Thus the crude extracts can act as inhibitors of bacteria in a food system. Upon further purification better results may be obtained.

Keywords: antimicrobial preservative, crude extracts, minced meat, test isolates

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Authors: Bhuwan C. Joshi, Atish Prakash, Ajudhia N. Kalia

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Ethnopharmacological relevance: The plant Urtica dioica L. (Urticaceae) is used in various diseases including hepatic ailments. Traditionally, the leaves and roots of the plant are used in jaundice. Objective: The aim of the present work was to evaluate hepatoprotective potential of potent antioxidant from Urtica dioica L. against CCl4 induced hepatotoxicity in-vitro and in-vivo model. Materials and methods: Antioxidant activity of hydro alcoholic extract and its fractions petroleum ether fraction (PEF), ethyl acetate fraction (EAF), n-butanol fraction (NBF) and aqueous fraction (AF) were determined by DPPH radicals scavenging assay. Fractions were subjected to in-vitro cell line study. Further, the most potent fraction (EAF) was subjected to in-vivo study. The in-vivo hepatoprotective active fraction was chromatographed on silica column to isolate the bioactive constituent(s). Structure elucidation was done by using various spectrophotometric techniques like UV, IR, 1H NMR, 13C NMR and MS spectroscopy. Results and conclusion: The ethyl acetate fraction (EAF) of Urtica. dioica L. possessed the potent antioxidant activity viz. DPPH (IC50 78.99 ± 0.17 µg/ml). The in-vitro cell line study showed EAF prevented the cell damage. The EAF significantly attenuated the increased liver enzymes activities in serum and tissue. Column chromatography of most potent antioxidant fraction (EAF) leads to the isolation of 4-hydroxy-3-methoxy cinnamic acid which is responsible for its hepatoprotective potential. Hence, the present study suggests that EAF has significant antioxidant and hepatoprotective potential on CCl4 induced hepatotoxicity in-vitro and in-vivo.

Keywords: Urtica dioica L., antioxidant, HepG2 cell line, hepatoprotective

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Authors: Soomal Hamza, Uzma Imran

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Organochlorine pesticides bio-accumulate into the fat of fish, birds, and animals through which it enters the human food cycle. Due to their persistence and stability in the environment, many health impacts are associated with them, most of which are carcinogenic in nature. In this study, the level of organochlorine pesticides has been detected in Keenjhar Lake and remediated using Rhizoremediation technique. 14 OC pesticides namely, Aldrin, Deldrin, Heptachlor, Heptachlor epoxide, Endrin, Endosulfun I and II, DDT, DDE, DDD, Alpha, Beta, Gamma BHC and two plants namely, Water Hyacinth and Slvinia Molesta were used in the system using pot experiment which processed for 11 days. A consortium was inoculated in both plants to increase its efficiency. Water samples were processed using liquide-liquid extraction. Sediments and roots samples were processed using Soxhlet method followed by clean-up and Gas Chromatography. Delta-BHC was the predominantly found in all samples with mean concentration (ppb) and standard deviation of 0.02 ± 0.14, 0.52 ± 0.68, 0.61 ± 0.06, in Water, Sediments and Roots samples respectively. The highest levels were of Endosulfan II in the samples of water, sediments and roots. Water Hyacinth proved to be better bioaccumulaor as compared to Silvinia Molesta. The pattern of compounds reduction rate by the end of experiment was Delta-BHC>DDD > Alpha-BHC > DDT> Heptachlor> H.Epoxide> Deldrin> Aldrin> Endrin> DDE> Endosulfun I > Endosulfun II. Not much significant difference was observed between the pots with the consortium and pots without the consortium addition. Phytoremediation is a promising technique, but more studies are required to assess the bioremediation potential of different aquatic plants and plant-endophyte relationship.

Keywords: aquatic plant, bio remediation, gas chromatography, liquid liquid extraction

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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

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Authors: Ameur Abdelkader, Abed Bouarfa Hafida

Abstract:

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

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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 101