Search results for: plant data

Authors: Jung-Soo Lee, Ujjal Kumar Nath, IllSup Nou, Dulal Chandra

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Leaf lettuce is one of the most important leafy vegetables that is used as raw for salad and part of everyday dishes in many parts of the world including Asian countries. Since it is used as fresh, its quality maintenance is crucial which depends on several pre- and postharvest factors. In order to investigate the effects of pre-fix factors on the postharvest quality, the interaction of pre-fix factors such as growing conditions and fixed factor like cultivars were evaluated. Four Korean leaf lettuce cultivars ‘Cheongchima’, ‘Cheongchuckmyeon’, ‘Geockchima’ and ‘Geockchuckmyeon’ were grown under natural condition (as control) and altered growing condition (green house) with excess soil water and 50% shading to monitor their postharvest qualities. Several growth parameters like plant height, number of leaves, leaf thickness, fresh biomass yield as well as postharvest qualities like fresh weight loss, respiration rate, changes in color and shelf-life were measured in lettuce during storage up to 36 days at 5°C. Plant height and the number of leaves were affected by both pre-fix growing conditions as well as the cultivars. However, fresh biomass yield was affected by only growing condition, whereas leaf thickness was affected by cultivars. Additionally, the degrees of fresh weight loss and respiration rate of leaf lettuce at postharvest stages were influenced by pre-fix growing conditions and cultivars. However, changes in color of leaves during storage were less remarkable in samples harvested from of ‘Cheongchima’ and ‘Cheongchuckmyeon’ cultivars grown in excess watering with 50% shade than that grown in control condition. Consequently, these two cultivars also showed longer shelf-life when they were grown in excess watering with 50% shade than other cultivars or samples were grown in control condition. Based on the measured parameters, it can be concluded that postharvest quality of leaf lettuce might be accelerated by growing lettuce under excess soil water with 50% shading.

Keywords: cultivar, growing condition, leaf lettuce, postharvest quality, shelf-life

Procedia PDF Downloads 258

Authors: S. Lahijanian, M. Mobli, B. Baninasab, N. Etemadi

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Low temperature extremes are amongst the major stresses that adversely affect the plant growth and productivity. Cold stress causes oxidative stress, physiological, morphological and biochemical changes in plant cells. Generally, low temperatures similar to salinity and drought exert their negative effects mainly by disrupting the ionic and osmotic equilibrium of the plant cells. Changes in climatic condition leading to more frequent extreme conditions will require adapted crop species on a larger scale in order to sustain agricultural production. Eucalyptus is a diverse genus of flowering trees (and a few shrubs) in the myrtle family, Myrtaceae. Members of this genus dominate the tree flora of Australia. The eucalyptus genus contains more than 580 species and large number of cultivars, which are native to Australia. Large distribution and diversity of compatible eucalyptus cultivars reflect the fact of ecological flexibility of eucalyptus. Some eucalyptus cultivars can sustain hard environmental conditions like high and low temperature, salinity, high level of PH, drought, chilling and freezing which are intensively effective on crops with tropical and subtropical origin. In this study, we tried to evaluate freezing tolerance of 12 eucalyptus genotypes by means of four different morphological and physiological methods: Chlorophyll fluorescence, electrolyte leakage, proline and stomatal density. The studied cultivars include Eucalyptus camaldulensis, E. coccifera, E. darlympleana, E. erythrocorys, E. glaucescens, E. globulus, E. gunnii, E. macrocorpa, E. microtheca, E. rubida, E. tereticornis, and E. urnigera. Except for stomatal density recording, in other methods, plants were exposed to five gradual temperature drops: zero, -5, -10, -15 and -20 degree of centigrade and they remained in these temperatures for at least one hour. Experiment for measuring chlorophyll fluorescence showed that genotypes E. erythrocorys and E. camaldulensis were the most resistant genotypes and E. gunnii and E.coccifera were more sensitive than other genotypes to freezing stress effects. In electrolyte leakage experiment with regard to significant interaction between cultivar and temperature, genotypes E. erythrocorys and E.macrocorpa were shown to be the most tolerant genotypes and E. gunnii, E. urnigera, E. microtheca and E. tereticornis with the more ionic leakage percentage showed to be more sensitive to low temperatures. Results of Proline experiment approved that the most resistant genotype to freezing stress is E. erythrocorys. In the stomatal density experiment, the numbers of stomata under microscopic field were totally counted and the results showed that the E. erythrocorys and E. macrocorpa genotypes had the maximum and E. coccifera and E. darlympleana genotypes had minimum number of stomata under microscopic field (0.0605 mm2). In conclusion, E. erythrocorys identified as the most tolerant genotype; meanwhile E. gunnii classified as the most freezing susceptible genotype in this investigation. Further, remarkable correlation was not obtained between the stomatal density and other cold stress measures.

Keywords: chlorophyll fluorescence, cold stress, ionic leakage, proline, stomatal density

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Authors: Nurul Aliaa Idris, David A. Collings

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Phi thickenings are specialized secondary cell wall thickenings that are found in the cortex of the roots in a wide range of plant species, including orchids. The role of phi thickenings in the root is still under debate through research have linked environmental conditions, particularly abiotic stresses such as water stress, heavy metal stress and salinity to their induction in the roots. It has also been suggested that phi thickenings may act as a barrier to regulate solute uptake, act as a physical barrier against fungal hyphal penetration due to its resemblance to the Casparian strip and play a mechanical role to support cortical cells. We have investigated phi thickening function in epiphytic orchids of the genus Miltoniopsis through induction experiment against factors such as soil compaction and water stress. The permeability of the phi thickenings in Miltoniopsis was tested through uptake experiments using the fluorescent tracer dyes Calcofluor white, Lucifer yellow and Propidium iodide then viewed with wide-field or confocal microscopy. To test whether phi thickening may prevent fungal colonization in the root cell, fungal re-infection experiment was conducted by inoculating isolated symbiotic fungus to sterile in vitro Miltoniopsis explants. As the movement of fluorescent tracers through the apoplast was not blocked by phi thickenings, and as phi thickenings developed in the roots of sterile cultures in the absence of fungus and did not prevent fungal colonization of cortical cells, the phi thickenings in Miltoniopsis do not function as a barrier. Phi thickenings were found to be absent in roots grown on agar and remained absent when plants were transplanted to moist soil. However, phi thickenings were induced when plants were transplanted to well-drained media, and by the application of water stress in all soils tested. It is likely that phi thickenings stabilize the root cortex during dehydration. Nevertheless, the varied induction responses present in different plant species suggest that the phi thickenings may play several adaptive roles, instead of just one, depending on species.

Keywords: abiotic stress, Miltoniopsis, orchid, phi thickening

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Authors: Amadou Wurry Jallow, Adama N. S. Bah, Karamo Bah, Shih-Ye Wang, Kuo-Chung Chu, Chien-Yeh Hsu

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Chronic kidney disease (CKD) is a major public health challenge with high prevalence, rising incidence, and serious adverse consequences. Developing effective risk prediction models is a cost-effective approach to predicting and preventing complications of chronic kidney disease (CKD). This study aimed to develop an accurate machine learning model that can dynamically identify individuals at risk of CKD using various kinds of diagnostic data, with or without laboratory data, at different follow-up points. Creatinine is a key component used to predict CKD. These models will enable affordable and effective screening for CKD even with incomplete patient data, such as the absence of creatinine testing. This retrospective cohort study included data on 19,429 adults provided by a private research institute and screening laboratory in Taiwan, gathered between 2001 and 2015. Univariate Cox proportional hazard regression analyses were performed to determine the variables with high prognostic values for predicting CKD. We then identified interacting variables and grouped them according to diagnostic data categories. Our models used three types of data gathered at three points in time: non-laboratory, laboratory, and metabolic indices data. Next, we used subgroups of variables within each category to train two machine learning models (Random Forest and XGBoost). Our machine learning models can dynamically discriminate individuals at risk for developing CKD. All the models performed well using all three kinds of data, with or without laboratory data. Using only non-laboratory-based data (such as age, sex, body mass index (BMI), and waist circumference), both models predict chronic kidney disease as accurately as models using laboratory and metabolic indices data. Our machine learning models have demonstrated the use of different categories of diagnostic data for CKD prediction, with or without laboratory data. The machine learning models are simple to use and flexible because they work even with incomplete data and can be applied in any clinical setting, including settings where laboratory data is difficult to obtain.

Keywords: chronic kidney disease, glomerular filtration rate, creatinine, novel metabolic indices, machine learning, risk prediction

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Authors: Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

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Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work.

Keywords: support vector mechanism (SVM), machine learning (ML), support vector machines (SVM), department of transportation (DFT)

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Authors: Raffaele Danilo Esposito, Domingo Planes Meseguer, Maria Del Pilar Dorado Rodriguez

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As far as we know, it is still unavailable a commercial solution which would allow to manage, openly and configurable up to user needs, the huge amount of data generated in a modern Radiation Oncology Department. Currently, available information management systems are mainly focused on Record & Verify and clinical data, and only to a small extent on physical data. Thus, results in a partial and limited use of the actually available information. In the present work we describe the implementation at our department of a centralized information management system based on a web server. Our system manages both information generated during patient planning and treatment, and information of general interest for the whole department (i.e. treatment protocols, quality assurance protocols etc.). Our objective it to be able to analyze in a simple and efficient way all the available data and thus to obtain quantitative evaluations of our treatments. This would allow us to improve our work flow and protocols. To this end we have implemented a relational data base which would allow us to use in a practical and efficient way all the available information. As always we only use license free software.

Keywords: information management system, radiation oncology, medical physics, free software

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Authors: Komol Phaisarn, Natcha Wattanaprapa

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This research is a survey research with an objective to study the safety of data storage devices of graduate students of academic year 2013, Suan Sunandha Rajabhat University. Data were collected by questionnaire on the safety of data storage devices according to CIA principle. A sample size of 81 was drawn from population by purposive sampling method. The results show that most of the graduate students of academic year 2013 at Suan Sunandha Rajabhat University use handy drive to store their data and the safety level of the devices is at good level.

Keywords: security, safety, storage devices, graduate students

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Authors: Aravind Kumar

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The high terrains of Garhwal (Uttarakhand) Himalaya are the niches of a number of rare and endemic plant species of great therapeutic importance. However, the wild flora of the area is still under a constant threat due to rapid upsurge in human interferences, especially through multifarious tourism growth and peri-urban encroachments. After getting the status of a ‘Special State’ of the country since its inception in the year 2000, this newly borne State led to very rapid infrastructural growth and development. Consequently, its townships started expanding in an unmanaged way grabbing nearby agricultural lands and forest areas into peri-urban landscapes. Simultaneously, a boom in tourism and pilgrimage in the state and the infrastructural facilities raised by the government for tourists/pilgrims are destroying its biodiversity. Field survey revealed 242 plant species of therapeutic significance naturally growing in the area and being utilized by local inhabitants as traditional medicines. On conservation scale, 6 species (2.2%) were identified as critically endangered, 19 species (7.1%) as the endangered ones, 8 species (3.0%) under rare category, 17 species (6.4%) as threatened and 14 species (5.2%) as vulnerable. The Government of India has brought mega-biodiversity hot spots of the state under Biosphere Reserve, National Parks, etc. restricting all kinds of human interferences; however, the two most sacred shrines of Hindus and Sikhs viz. Shri Badrinath and Shri Hemkunt Sahib, and two great touristic attractions viz. Valley of Flowers and Auli-Joshimath Skiing Track oblige the government to maintain equilibrium between entries of visitors vis-à-vis biodiversity conservation in high terrains of Uttarakhand Himalaya.

Keywords: biodiversity conservation, ethno-botany, Garhwal (Uttarakhand) Himalaya, peri-urban encroachment, pilgrimage and tourism

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Authors: Bhupendra Kumar Poudel, Sadhana Amatya, Tirtha Maiya Shrestha, Bharatmani Pokhrel, Mohan Prasad Amatya

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Background: H. salicifolia is a dense, branched, multipurpose, deciduous, nitrogen fixing, thorny willow-like small to moderate tree, restricted to the Himalaya. Among the two species of Nepal (Hippophae salicifolia and H. tibetana), it has been traditionally used as food additive, anticancer (bark), and treating toothache, tooth inflammation (anti-inflammatory) and radiation injury; while people of Western Nepal have largely undermined its veiled treasure by using it for fuel, wood and soil stabilization only. Therefore, the main objective of this study was to explore biological properties (analgesic, antidiabetic, cytotoxic and anti-inflammatory properties of this plant. Methodology: The transverse section of leaves and stems were viewed under microscope. Extracts obtained from soxhlation subjected to tests for phytochemical and biological studies. Rats (used to study antidiabetic and anti-inflammatory properties) and mice (used to study analgesic, CNS depressant, muscle relaxant and locomotor properties) were assumed to be normally distributed; then ANOVA and post hoc tukey test was used to find significance. The data obtained were analyzed by SPSS 17 and Excel 2007. Results and Conclusion: Pharmacognostical analysis revealed the presence of long stellate trichomes, double layered vascular bundle 5-6 in number and double layered compact sclerenchyma. The preliminary phytochemical screening of the extracts was found to exhibit the positive reaction tests for glycoside, steroid, tannin, flavonoid, saponin, coumarin and reducing sugar. The brine shrimp lethality bioassay tested in 1000, 100 and 10 ppm revealed cytotoxic activity inherent in methanol, water, chloroform and ethyl acetate extracts with LC50 (μg/ml) values of 61.42, 99.77, 292.72 and 277.84 respectively. The cytotoxic activity may be due to presence of tannins in the constituents. Antimicrobial screening of the extracts by cup diffusion method using Staphylococcus aereus, Escherichia coli and Pseudomonas aeruginosa against standard antibiotics (oxacillin, gentamycin and amikacin respectively) portrayed no activity against the microorganisms tested. The methanol extract of the stems and leaves showed various pharmacological properties: and antidiabetic, anti-inflammatory, analgesic [chemical writhing method], CNS depressant, muscle relaxant and locomotor activities in a dose-dependent fashion, indicating the possibility of the presence of different constituents in the stems and leaves responsible for these biological activities. All the effects when analyzed by post hoc tukey test were found to be significant at 95% confidence level. The antidiabetic activity was presumed to be due to flavonoids present in extract. Therefore, it can be concluded that this plant’s secondary metabolites possessed strong antidiabetic, anti-inflammatory and cytotoxic activity which could be isolated for further investigation.

Keywords: Hippophae salicifolia, constituents, antidiabetic, inflammatory, brine shrimp

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Authors: Kaddi Mohammed, A. Benatiallah, D. Benatiallah

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Data grid is a technology that has full emergence of new challenges, such as the heterogeneity and availability of various resources and geographically distributed, fast data access, minimizing latency and fault tolerance. Researchers interested in this technology address the problems of the various systems related to the industry such as task scheduling, load balancing and replication. The latter is an effective solution to achieve good performance in terms of data access and grid resources and better availability of data cost. In a system with duplication, a coherence protocol is used to impose some degree of synchronization between the various copies and impose some order on updates. In this project, we present an approach for placing replicas to minimize the cost of response of requests to read or write, and we implement our model in a simulation environment. The placement techniques are based on a cost model which depends on several factors, such as bandwidth, data size and storage nodes.

Keywords: response time, query, consistency, bandwidth, storage capacity, CERN

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Authors: Lu Song Ma Li Zhi

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With the rapid advancement of artificial intelligence technology, large language models (LLMs) have become a milestone in the field of natural language processing, demonstrating remarkable capabilities in semantic understanding, intelligent question answering, and text generation. These models are gradually penetrating various industries, particularly showcasing significant application potential in the data analysis domain. However, retraining or fine-tuning these models requires substantial computational resources and ample downstream task datasets, which poses a significant challenge for many enterprises and research institutions. Without modifying the internal parameters of the large models, prompt engineering techniques can rapidly adapt these models to new domains. This paper proposes a prompt design strategy aimed at leveraging the capabilities of large language models to automate the generation of data analysis code. By carefully designing prompts, data analysis requirements can be described in natural language, which the large language model can then understand and convert into executable data analysis code, thereby greatly enhancing the efficiency and convenience of data analysis. This strategy not only lowers the threshold for using large models but also significantly improves the accuracy and efficiency of data analysis. Our approach includes requirements for the precision of natural language descriptions, coverage of diverse data analysis needs, and mechanisms for immediate feedback and adjustment. Experimental results show that with this prompt design strategy, large language models perform exceptionally well in multiple data analysis tasks, generating high-quality code and significantly shortening the data analysis cycle. This method provides an efficient and convenient tool for the data analysis field and demonstrates the enormous potential of large language models in practical applications.

Keywords: large language models, prompt design, data analysis, code generation

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Authors: N. Samarinas, C. Evangelides, C. Vrekos

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The aim of this paper is the comparison of three different methods, in order to produce fuzzy tolerance relations for rainfall data classification. More specifically, the three methods are correlation coefficient, cosine amplitude and max-min method. The data were obtained from seven rainfall stations in the region of central Greece and refers to 20-year time series of monthly rainfall height average. Three methods were used to express these data as a fuzzy relation. This specific fuzzy tolerance relation is reformed into an equivalence relation with max-min composition for all three methods. From the equivalence relation, the rainfall stations were categorized and classified according to the degree of confidence. The classification shows the similarities among the rainfall stations. Stations with high similarity can be utilized in water resource management scenarios interchangeably or to augment data from one to another. Due to the complexity of calculations, it is important to find out which of the methods is computationally simpler and needs fewer compositions in order to give reliable results.

Keywords: classification, fuzzy logic, tolerance relations, rainfall data

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Authors: Hallal Nouria, Kharoubi Omar

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Wormwood (Artemisia absinthium L.) is a medicinal and aromatic bitter herb, which has been used as a medicine from ancient times. It has traditionally been used as anthelmintic, choleretic, antiseptic, balsamic, depurative, digestive, diuretic, emmenagogue and in treating leukemia and sclerosis. The species was cited to be used externally as cataplasm of crushed leaves for snake and scorpion bites or decoction for wounds and sores applied locally as antiseptic and antifungal. Wormwood extract have high contents of total phenolic compounds and total flavonoids indicating that these compounds contribute to antiradical and antioxidative activity. Most of the degenerative diseases are caused by free radicals. Antioxidants are the agents responsible for scavenging free radicals. The aim of present study was to evaluate the phytochemical and in vitro antioxidant properties of Wormwood extract. DPPH assay and reducing power assay were the method adopted to study antioxidant potentials of extracts. Standard methods were used to screen preliminary phytochemistry and quantitative analysis of tannin, phenolics and flavanoids. Aqueous and alcoholic extracts were showed good antioxidant effect with IC50 ranges from 62 μg/ml for aqueous and 116μg/ml for alcoholic extracts. Phenolic compounds, tannins and flavonoids were the major phytochemicals present in both the extracts. Percentage of inhibition increased with the increased concentration of extracts. The aqueous and alcoholic extract yielded 20, 15& 3, 59 mg/g gallic acid equivalent phenolic content 2, 78 & 1,83 mg/g quercetin equivalent flavonoid and 2, 34 & 6, 40 g tannic acid equivalent tannins respectively. The aqueous and methanol extracts of the aerial parts showed a positive correlation between the total phenolic content and the antioxidant activity measured in the plant samples. The present study provides evidence that both extracts of Artemisia absinthium is a potential source of natural antioxidant.

Keywords: pharmaceutical industries, medicinal and aromatic plant, antioxidants, phenolic compounds, Artemisia absinthium

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Authors: S. Anastasiou, C. Nathanailides

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The purpose of this study is to examine the possible link between employee and customer satisfaction. The service provided by employees, help to build a good relationship with customers and can help at increasing their loyalty. Published data for job satisfaction and indicators of customer services were gathered from relevant published works which included data from five different countries. The reviewed data indicate a significant correlation between indicators of customer and employee satisfaction in the Banking sector. There was a significant correlation between the two parameters (Pearson correlation R2=0.52 P<0.05) The reviewed data provide evidence that there is some practical evidence which links these two parameters.

Keywords: job satisfaction, job performance, customer’ service, banks, human resources management

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Authors: Suman Podder

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As Australian ‘Open Banking’ allows customers to share their financial data with accredited Third-Party Providers (‘TPPs’), it is necessary to evaluate whether the regulators have achieved the balance between protecting customer data privacy and promoting data-related innovation. Recognising the need to increase customers’ influence on their own data, and the benefits of data-related innovation, the Australian Government introduced ‘Consumer Data Right’ (‘CDR’) to the banking sector through Open Banking regulation. Under Open Banking, TPPs can access customers’ banking data that allows the TPPs to tailor their products and services to meet customer needs at a more competitive price. This facilitated access and use of customer data will promote innovation by providing opportunities for new products and business models to emerge and grow. However, the success of Open Banking depends on the willingness of the customers to share their data, so the regulators have augmented the protection of data by introducing new privacy safeguards to instill confidence and trust in the system. The dilemma in policymaking is that, on the one hand, lenient data privacy laws will help the flow of information, but at the risk of individuals’ loss of privacy, on the other hand, stringent laws that adequately protect privacy may dissuade innovation. Using theoretical and doctrinal methods, this paper examines whether the privacy safeguards under Open Banking will add to the compliance burden of the participating financial institutions, resulting in the undesirable effect of stifling other policy objectives such as innovation. The contribution of this research is three-fold. In the emerging field of customer data sharing, this research is one of the few academic studies on the objectives and impact of Open Banking in the Australian context. Additionally, Open Banking is still in the early stages of implementation, so this research traces the evolution of Open Banking through policy debates regarding the desirability of customer data-sharing. Finally, the research focuses not only on the customers’ data privacy and juxtaposes it with another important objective of promoting innovation, but it also highlights the critical issues facing the data-sharing regime. This paper argues that while it is challenging to develop a regulatory framework for protecting data privacy without impeding innovation and jeopardising yet unknown opportunities, data privacy and innovation promote different aspects of customer welfare. This paper concludes that if a regulation is appropriately designed and implemented, the benefits of data-sharing will outweigh the cost of compliance with the CDR.

Keywords: consumer data right, innovation, open banking, privacy safeguards

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Authors: Saeed Qaisrani

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The study was conducted to observe carbon and nutrient loss by burning of rice residues on rice-wheat cropping system The rice crop was harvested to conduct the experiment in a randomized complete block design (RCBD) with factors and 4 replications with a net plot size of 10 m x 20 m. Rice stubbles were managed by two methods i.e. Incorporation & burning of rice residues. Soil samples were taken to a depth of 30 cm before sowing & after harvesting of wheat. Wheat was sown after harvesting of rice by three practices i.e. Conventional tillage, Minimum tillage and Zero tillage to observe best tillage practices. Laboratory and field experiments were conducted on wheat to assess best tillage practice and residues management method with estimation of carbon losses. Data on the following parameters; establishment count, plant height, spike length, number of grains per spike, biological yield, fat content, carbohydrate content, protein content, and harvest index were recorded to check wheat quality & ensuring food security in the region. Soil physico-chemical analysis i.e. pH, electrical conductivity, organic matter, nitrogen, phosphorus, potassium, and carbon were done in soil fertility laboratory. Substantial results were found on growth, yield and related parameters of wheat crop. The collected data were examined statistically with economic analysis to estimate the cost-benefit ratio of using different tillage techniques and residue management practices. Obtained results depicted that Zero tillage method have positive impacts on growth, yield and quality of wheat, Moreover, it is cost effective methodology. Similarly, Incorporation is suitable and beneficial method for soil due to more nutrients provision and reduce the need of fertilizers. Burning of rice stubbles has negative impact including air pollution, nutrient loss, microbes died and carbon loss. Recommended the zero tillage technology to reduce carbon losses along with food security in Pakistan.

Keywords: agricultural agronomy, food security, carbon sequestration, rice-wheat cropping system

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Authors: Imme Petersen, Wiebke Sick, Regine Kollek

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In systems medicine, high-throughput technologies produce large amounts of data on different biological and pathological processes, including (disturbed) gene expressions, metabolic pathways and signaling. The large volume of data of different types, stored in separate databases and often located at different geographical sites have posed new challenges regarding data handling and processing. Tools based on bioinformatics have been developed to resolve the upcoming problems of systematizing, standardizing and integrating the various data. However, the heterogeneity of data gathered at different levels of biological complexity is still a major challenge in data analysis. To build multilayer disease modules, large and heterogeneous data of disease-related information (e.g., genotype, phenotype, environmental factors) are correlated. Therefore, a great deal of attention in systems medicine has been put on data standardization, primarily to retrieve and combine large, heterogeneous datasets into standardized and incorporated forms and structures. However, this data-centred concept of standardization in systems medicine is contrary to the debate in science and technology studies (STS) on standardization that rather emphasizes the dynamics, contexts and negotiations of standard operating procedures. Based on empirical work on research consortia that explore the molecular profile of diseases to establish systems medical approaches in the clinic in Germany, we trace how standardized data are processed and shaped by bioinformatics tools, how scientists using such data in research perceive such standard operating procedures and which consequences for knowledge production (e.g. modeling) arise from it. Hence, different concepts and meanings of standardization are explored to get a deeper insight into standard operating procedures not only in systems medicine, but also beyond.

Keywords: data, science and technology studies (STS), standardization, systems medicine

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Authors: Talita Kato, Moises Grespan, Elza I. Ida, Massami Shimokomaki, Adriana L. Soares

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White Striping (WS) is becoming a concern for the poultry industry, as it affects the look of breast broiler chicken meat leading it to rejection by the consumers. It is characterized by the appearance of varying degrees of white striations on the Pectoralis major muscle surface following the direction of the muscle fiber. The etiology of this myopathy is still unknown, however it is suggested to be associated with increased weight gain rate and age of the bird, attributing the phenomenon to the genetically bird’s selection for efficiently higher meat production. The aim of this study was to evaluate the occurrence of Pectoralis major WS in a commercial plant in southern Brazil and its chemical characterization. The breast meat samples (n=660) from birds of 47 days of age, were classified as: Normal NG (no apparent white striations), Moderate MG (when the fillets present thin lines <1 mm) and Severe SG (white striations present ˃1 mm thick covering a large part of the fillet surface). Thirty samples (n = 10 for each level of severity) were analyzed for pH, color (L*, a*, b*), proximate chemical composition (moisture, protein, ash and lipids contents) and hydroxyproline in order to determine the collagen content. The results revealed the occurrence for NG group was 16.97%, 51.67% for MG group and 31.36% for SG group. Although the total protein content did not differ significantly, the collagen index was 42% higher in favor to SG in relation to NG. Also the lipid fraction was 27% higher for SG group. The NG presented the lowest values of the parameters L* and a* (P ≤ 0.05), as there was no white striations on its surface and highest b* value in SG, because of the maximum lipid contents. These results indicate there was a contribution of the SG muscle cells to oversynthesize connective tissue components on the muscle fascia. In conclusion, this study revealed a high incidence of White Striping on broiler commercial line in Brazil thus, there is a need to identify the causes of this abnormality in order to diminish or to eliminate it.

Keywords: collagen content, commercial line, pectoralis major muscle, proximate composition

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Authors: Masoumeh Ghorbania, Falk Liebnerb, Hendrikus W.G. van Herwijnenc, Johannes Konnertha

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Phenol formaldehyde (PF) resins are widely used as wood adhesives for variety of industrial products such as plywood, laminated veneer lumber and others. Lignin as a main constituent of wood has become well-known as a potential substitute for phenol in PF adhesives because of their structural similarity. During the last decades numerous research approaches have been carried out to substitute phenol with pulping-derived lignin, whereby the lower reactivity of resins synthesized with shares of lignin seem to be one of the major challenges. This work reports about a systematic screening of different types of lignin (plant origin and pulping process) for their suitability to replace phenol in phenolic resins. Lignin from different plant sources (softwood, hardwood and grass) were used, as these should differ significantly in their reactivity towards formaldehyde of their reactive phenolic core units. Additionally a possible influence of the pulping process was addressed by using the different types of lignin from soda, kraft, and organosolv process and various lignosulfonates (sodium, ammonium, calcium, magnesium). To determine the influence of lignin on the adhesive performance beside others the rate of viscosity development, bond strength development of varying hot pressing time and other thermal properties were investigated. To evaluate the performance of the cured end product, a few selected properties were studied at the example of solid wood-adhesive bond joints, compact panels and plywood. As main results it was found that lignin significantly accelerates the viscosity development in adhesive synthesis. Bonding strength development during curing of adhesives decelerated for all lignin types, while this trend was least for pine kraft lignin and spruce sodium lignosulfonate. However, the overall performance of the products prepared with the latter adhesives was able to fulfill main standard requirements, even after exposing the products to harsh environmental conditions. Thus, a potential application can be considered for processes where reactivity is less critical but adhesive cost and product performance is essential.

Keywords: phenol formaldehyde resin, lignin phenol formaldehyde resin, ABES, DSC

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Authors: Kavian Khosravinia, Mohd Khair Hassan, Ribhan Zafira Abdul Rahman, Syed Abdul Rahman Al-Haddad

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The CAN (controller area network) bus is introduced as a multi-master, message broadcast system. The messages sent on the CAN are used to communicate state information, referred as a signal between different ECUs, which provides data consistency in every node of the system. OBD-II Dongles that are based on request and response method is the wide-spread solution for extracting sensor data from cars among researchers. Unfortunately, most of the past researches do not consider resolution and quantity of their input data extracted through OBD-II technology. The maximum feasible scan rate is only 9 queries per second which provide 8 data points per second with using ELM327 as well-known OBD-II dongle. This study aims to develop and design a programmable, and latency-sensitive vehicle data acquisition system that improves the modularity and flexibility to extract exact, trustworthy, and fresh car sensor data with higher frequency rates. Furthermore, the researcher must break apart, thoroughly inspect, and observe the internal network of the vehicle, which may cause severe damages to the expensive ECUs of the vehicle due to intrinsic vulnerabilities of the CAN bus during initial research. Desired sensors data were collected from various vehicles utilizing Raspberry Pi3 as computing and processing unit with using OBD (request-response) and direct CAN method at the same time. Two types of data were collected for this study. The first, CAN bus frame data that illustrates data collected for each line of hex data sent from an ECU and the second type is the OBD data that represents some limited data that is requested from ECU under standard condition. The proposed system is reconfigurable, human-readable and multi-task telematics device that can be fitted into any vehicle with minimum effort and minimum time lag in the data extraction process. The standard operational procedure experimental vehicle network test bench is developed and can be used for future vehicle network testing experiment.

Keywords: CAN bus, OBD-II, vehicle data acquisition, connected cars, telemetry, Raspberry Pi3

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Authors: Sergio Zabala-Vargas, Miguel Jiménez-Barrera, Luz VArgas-Sánchez

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In recent years, information related to project management in organizations has been increasing exponentially. Performance data, management statistics, indicator results have forced the collection, analysis, traceability, and dissemination of project managers to be essential. In this sense, there are current trends to facilitate efficient decision-making in emerging technology projects, such as: Machine Learning, Data Analytics, Data Mining, and Big Data. The latter is the most interesting in this project. This research is part of the thematic line Construction methods and project management. Many authors present the relevance that the use of emerging technologies, such as Big Data, has taken in recent years in project management in the construction sector. The main focus is the optimization of time, scope, budget, and in general mitigating risks. This research was developed in the northeastern region of Colombia-South America. The first phase was aimed at diagnosing the use of emerging technologies (Big-Data) in the construction sector. In Colombia, the construction sector represents more than 50% of the productive system, and more than 2 million people participate in this economic segment. The quantitative approach was used. A survey was applied to a sample of 91 companies in the construction sector. Preliminary results indicate that the use of Big Data and other emerging technologies is very low and also that there is interest in modernizing project management. There is evidence of a correlation between the interest in using new data management technologies and the incorporation of Building Information Modeling BIM. The next phase of the research will allow the generation of guidelines and strategies for the incorporation of technological tools in the construction sector in Colombia.

Keywords: big data, building information modeling, tecnology, project manamegent

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Authors: Ludmila Polechonska, Agnieszka Klink

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The biogeochemical studies of macrophytes shed light on elements bioavailability, transfer through the food webs and their possible effects on the biota, and provide a basis for their practical application in aquatic monitoring and remediation. Measuring the accumulation of elements in plants can provide time-integrated information about the presence of chemicals in aquatic ecosystems. The aim of the study was to determine and compare the contents of micro- and macroelements in two cosmopolitan macrophytes, submerged Ceratophyllum demersum (hornworth) and free-floating Hydrocharis morsus-ranae (European frog-bit), in order to assess their bioaccumulation potential, elements stock accumulated in each plant and their role in nutrients cycling in small water reservoirs. Sampling sites were designated in 25 oxbow lakes in urban areas in Lower Silesia (SW Poland). In each sampling site, fresh whole plants of C. demersum and H. morsus-ranae were collected from squares of 1x1 meters each where the species coexisted. European frog-bit was separated into leaves, stems and roots. For biomass measurement all plants growing on 1 square meter were collected, dried and weighed. At the same time, water samples were collected from each reservoir and their pH and EC were determined. Water samples were filtered and acidified and plant samples were digested in concentrated nitric acid. Next, the content of Ca, Cu, Fe, K, Mg, Mn, Ni and Zn was determined using atomic absorption method (AAS). Statistical analysis showed that C. demersum and organs of H. morsus-ranae differed significantly in respect of metals content (Kruskal-Wallis Anova, p<0.05). Contents of Cu, Mn, Ni and Zn were higher in hornwort, while European frog-bit contained more Ca, Fe, K, Mg. Bioaccumulation Factors (BCF=content in plant/concentration in water) showed similar pattern of metal bioaccumulation – microelements were more intensively accumulated by hornwort and macroelements by frog-bit. Based on BCF values both species may be positively evaluated as good accumulators of Cu, Fe, Mn, Ni and Zn. However, the distribution of metals in H. morsus-ranae was uneven – the majority of studied elements were retained in roots, which may indicate to existence of physiological barriers developed for dealing with toxicity. Some percent of Ca and K was actively transported to stems, but to leaves Mg only. Although the biomass of C. demersum was two times greater than biomass of H. morsus-ranae, the element off-take was greater only for Cu, Mn, Ni and Zn. Nevertheless, it can be stated that despite a relatively small biomass, compared to other macrophytes, both species may have an influence on the removal of trace elements from aquatic ecosystems and, as they serve as food for some animals, also on the incorporation of toxic elements into food chains. There was a significant positive correlation between content of Mn and Fe in water and roots of H. morus-ranae (R=0.51 and R=0.60, respectively) as well as between Cu concentration in water and in C. demersum (R=0.41) (Spearman rank correlation, p<0.05). High bioaccumulation rates and correlation between plants and water elements concentrations point to their possible use as passive biomonitors of aquatic pollution.

Keywords: aquatic plants, bioaccumulation, biomonitoring, macroelements, phytoremediation, trace metals

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Authors: Elkington Sibusiso Mnguni

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In South Africa, mining has been a key economic sector since the discovery of gold in 1886 in the Witwatersrand region, where the city of Johannesburg is located. However, some mines have since been decommissioned, and the continuous pumping of acid mine drainage (AMD) also stopped causing the AMD to rise towards the ground surface. This posed a serious environmental risk to the groundwater resources and river systems in the region. This paper documents the development and extent of the environmental damage as well as the measures implemented by the government to alleviate such damage. The study will add to the body of knowledge on the subject of AMD treatment to prevent environmental degradation. The method used to gather and collate relevant data and information was the desktop study. The key findings include the social and environmental impact of the AMD, which include the pollution of water sources for domestic use leading to skin and other health problems and the loss of biodiversity in some areas. It was also found that the technical intervention of constructing a plant to pump and treat the AMD using the high-density sludge technology was the most effective short-term solution available while a long-term solution was being explored. Some successes and challenges experienced during the implementation of the project are also highlighted. The study will be a useful record of the current status of the AMD treatment interventions in the region.

Keywords: acid mine drainage, groundwater resources, pollution, river systems, technical intervention, high density sludge

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Authors: Nazaket Gazieva

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Voice biometric data associated with physiological, psychological and other factors are widely used in forensic phonoscopy. There are various methods for identifying and verifying a person by voice. This article explores the minimum speech signal data as individual parameters of a speech signal. Monozygotic twins are believed to be genetically identical. Using the minimum data of the speech signal, we came to the conclusion that the voice imprint of monozygotic twins is individual. According to the conclusion of the experiment, we can conclude that the minimum indicators of the speech signal are more stable and reliable for phonoscopic examinations.

Keywords: phonogram, speech signal, temporal characteristics, fundamental frequency, biometric fingerprints

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Authors: Ujala, Diksha Sharma, Mahinder Partap, Ashish R. Warghat, Bhavya Bhargava

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In soil-less agriculture, hydroponic is considered a potential farming system for the production of uniform quality plant material in significantly less time. Therefore, for the first time, the current investigation corroborates the effect of different cultivation conditions (open-field, poly-house, and hydroponic) on morpho-physiological traits, phenolic content, and essential oil components analysis in three flower color variants (yellow, scarlet red, and orange) of Tagetes patula. The results revealed that the maximum plant height, number of secondary branches, number of flowers, photosynthesis, stomatal conductance, and transpiration rate were observed under the hydroponic system as compared to other conditions. However, the maximum content of gallic acid (0.82 mg/g DW), syringic acid (3.98 mg/g DW), epicatechin (0.48 mg/g DW), p-coumaric acid (7.28 mg/g DW), protocatechuic acid (0.59 mg/g DW), ferulic acid (2.58 mg/g DW), and luteolin (8.24 mg/g DW) were quantified maximally under open-field conditions. However, under hydroponic conditions, the higher content of vanillic acid (0.43 mg/g DW), caffeic acid (0.49 mg/g DW), and quercetin (0.92 mg/g DW) were quantified. Moreover, a total of nineteen volatile components were identified in the essential oil of different flower color variants of T. patula cultivated under different conditions. The major reported volatile components in essential oil were (-)-caryophyllene oxide, trans-β-caryophyllene, trans-geraniol, 3 methyl-benzyl alcohol, and 2,2’:5’,2”-terthiophene. It has also been observed that the volatile component percentage range in all variants was observed in open-field (70.85 % to 90.54 %), poly-house (59.03 % to 77.93 %), and hydroponic (68.78 % to 89.41 %). In conclusion, the research highlighted that morpho-physiological performance with flower production was enhanced in the hydroponic system. However, phenolic content and volatile components were maximally observed in open-field conditions. However, significant results have been reported under hydroponic conditions in all studied parameters, so it could be a potential strategy for quality biomass production in T. patula.

Keywords: Tagetes patula, cultivation conditions, hydroponic, morpho-physiology

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Authors: Francky Fouedjio

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Multivariate geostatistical data have become omnipresent in the geosciences and pose substantial analysis challenges. One of them is the grouping of data locations into spatially contiguous clusters so that data locations within the same cluster are more similar while clusters are different from each other, in some sense. Spatially contiguous clusters can significantly improve the interpretation that turns the resulting clusters into meaningful geographical subregions. In this paper, we develop an agglomerative hierarchical clustering approach that takes into account the spatial dependency between observations. It relies on a dissimilarity matrix built from a non-parametric kernel estimator of the spatial dependence structure of data. It integrates existing methods to find the optimal cluster number and to evaluate the contribution of variables to the clustering. The capability of the proposed approach to provide spatially compact, connected and meaningful clusters is assessed using bivariate synthetic dataset and multivariate geochemical dataset. The proposed clustering method gives satisfactory results compared to other similar geostatistical clustering methods.

Keywords: clustering, geostatistics, multivariate data, non-parametric

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Authors: Sara ElElimy, Samir Moustafa

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Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

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Authors: Nidhin Dani Abraham, T. K. Sri Shilpa

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Asset liability management is an important aspect in banking business. Moreover, the today’s banking is based on BASEL III which strictly regulates on the counterparty default. This paper focuses on prediction and analysis of counter party default risk, which is a type of risk occurs when the customers fail to repay the amount back to the lender (bank or any financial institutions). This paper proposes an approach to reduce the counterparty risk occurring in the financial institutions using an appropriate data mining technique and thus predicts the occurrence of NPA. It also helps in asset building and restructuring quality. Liability management is very important to carry out banking business. To know and analyze the depth of liability of bank, a suitable technique is required. For that a data mining technique is being used to predict the dormant behaviour of various deposit bank customers. Various models are implemented and the results are analyzed of saving bank deposit customers. All these data are cleaned using data cleansing approach from the bank data warehouse.

Keywords: data mining, asset liability management, BASEL III, banking

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Authors: Wanyi Zhu, Xia Ming, Huafeng Wang, Junda Chen, Lu Liu, Jiangwei Jiang, Guohua Liu

Abstract:

Anomaly detection is a first and imperative step needed to respond to unexpected problems and to assure high performance and security in large data center management. This paper presents an online anomaly detection system through an innovative approach of ensemble machine learning and adaptive differentiation algorithms, and applies them to performance data collected from a continuous monitoring system for multi-tier web applications running in Alibaba data centers. We evaluate the effectiveness and efficiency of this algorithm with production traffic data and compare with the traditional anomaly detection approaches such as a static threshold and other deviation-based detection techniques. The experiment results show that our algorithm correctly identifies the unexpected performance variances of any running application, with an acceptable false positive rate. This proposed approach has already been deployed in real-time production environments to enhance the efficiency and stability in daily data center operations.

Keywords: Alibaba data centers, anomaly detection, big data computation, dynamic ensemble learning

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Authors: A. Sivakumar, S. S. Darun Prakash, P. Navaneethakrishnan

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In and around Erode District, it is estimated that more than 1250 chemical and allied textile processing fabric industries are affected, partially closed and shut off for various reasons such as poor management, poor supplier performance, lack of planning for productivity, fluctuation of output, poor investment, waste analysis, labor problems, capital/labor ratio, accumulation of stocks, poor maintenance of resources, deficiencies in the quality of fabric, low capacity utilization, age of plant and equipment, high investment and input but low throughput, poor research and development, lack of energy, workers’ fear of loss of jobs, work force mix and work ethic. The main objective of this work is to analyze the existing conditions in textile fabric sector, validate the break even of Total Productivity (TP), analyze, design and implement fuzzy sets and mathematical programming for improvement of productivity and quality dimensions in the fabric processing industry. It needs to be compatible with the reality of textile and fabric processing industries. The highly risk events from productivity and quality dimension were found by fuzzy systems and results are wrapped up among the textile fabric processing industry.

Keywords: break even point, fuzzy crisp data, fuzzy sets, productivity, productivity cycle, total productive maintenance

Procedia PDF Downloads 332