Basil conservation affected by cropping season, harvest time and storage period

Fresh basil (Ocimum basilicum L.) is used in food, phytotherapic industry, and in traditional therapeutic, due to its essential oil content and composition. Nevertheless basil can not be kept for long periods after harvest and its quality can be reduced. This work aimed to assess the influence of the season and harvest time in the postharvest conservation of basil stored for different periods. Basil was harvested at 8 am and 4 pm both in August/1999 and January/2000. Cuttings were conditioned in PVC packages and stored for 3, 6, and 9 days. During storage, chlorophyll content, essential oil content and composition were determined as well as microbiological analyses were carried out. Harvest season and the days of storage influenced the final content of essential oil. There was a linear decrease in the content of essential oil, in the chlorophyll content and in the number of mold and yeast colonies during storage. There was no effect of cropping season or harvest hour on essential oil composition, but the eugenol and linalool content increased during storage. Coliforms were under 0.3 MPN g-1 and the number of Staphylococcus aureus was under 1.0x10² UFC g-1.

Phosphorus acquisition and internal utilization efficiency in tropical maize genotypes

The objective of this work was to determine the relative importance of phosphorus acquisition efficiency (PAE - plant P uptake per soil available P), and phosphorus internal utilization efficiency (PUTIL - grain yield per P uptake) in the P use efficiency (PUE - grain yield per soil available P), on 28 tropical maize genotypes evaluated at three low P and two high P environments. PAE was almost two times more important than PUTIL to explain the variability observed in PUE, at low P environments, and three times more important at high P environments. These results indicate that maize breeding programs, to increase PUE in these environments, should use selection index with higher weights for PAE than for PUTIL. The correlation between these two traits showed no significance at low or at high P environments, which indicates that selection in one of these traits would not affect the other. The main component of PUTIL was P quotient of utilization (grain yield per grain P) and not the P harvest index (grain P per P uptake). Selection to reduce grain P concentration should increase the quotient of utilization and consequently increase PUTIL.

Transcriptome analysis of intraspecific competition in Arabidopsis thaliana reveals organ-specific signatures related to nutrient acquisition and general stress response pathways.

ABSTRACT: BACKGROUND: Plants are sessile and therefore have to perceive and adjust to changes in their environment. The presence of neighbours leads to a competitive situation where resources and space will be limited. Complex adaptive responses to such situation are poorly understood at the molecular level. RESULTS: Using microarrays, we analysed whole-genome expression changes in Arabidopsis thaliana plants subjected to intraspecific competition. The leaf and root transcriptome was strongly altered by competition. Differentially expressed genes were enriched in genes involved in nutrient deficiency (mainly N, P, K), perception of light quality, and responses to abiotic and biotic stresses. Interestingly, performance of the generalist insect Spodoptera littoralis on densely grown plants was significantly reduced, suggesting that plants under competition display enhanced resistance to herbivory. CONCLUSIONS: This study provides a comprehensive list of genes whose expression is affected by intraspecific competition in Arabidopsis. The outcome is a unique response that involves genes related to light, nutrient deficiency, abiotic stress, and defence responses.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

High-performance payload data handling system for Gaia

Gaia is the most ambitious space astrometry mission currently envisaged and is a technological challenge in all its aspects. We describe a proposal for the payload data handling system of Gaia, as an example of a high-performance, real-time, concurrent, and pipelined data system. This proposal includes the front-end systems for the instrumentation, the data acquisition and management modules, the star data processing modules, and the payload data handling unit. We also review other payload and service module elements and we illustrate a data flux proposal.

Studies on the characterization of dental whitening process by using both hyperspectral imaging and colorimeter techniques

A beautiful smile is directly related with white teeth. Nowadays oral care has increased and developed processes for beautiful smiles. Dental bleaching is frequently used in odontology, not just for health care also for aesthetic treatment. With the possibility of teeth bleaching, now the importance is in, how white the tooth is? Because color is relate to an individual perception. In order to assets teeth correct color identification has been developed many color guides, models, spaces and analytical methods. Spite all of these useful tools the color interpretation depends on environmental factors, position of the sample in the data acquisition and most importantly the instrument sensitivity. The commons methods have proved to be useful. They are easy to handle, some are portable but they do not have a high sensitivity. The present work is based on the integration of a new analytical technique for color acquisition. High spectral Image (HSI) is able to performed image analysis with high quality and efficiency. HSI is used in many fields and we used it for color image analysis within the bleaching process. The main comparison was done with the HSI and the colorimeter through the processes of two different bleaching protocols. The results showed that HSI has higher sensitivity than the colorimeter. During the analysis the dental surface with the HSI we were able to notice surface changes. These changes were analyzed by roughness studies.

Educational data mining and learning analytics : Clasificación de las matriculaciones de A.D.E. en la UOC

Trabajo de investigación que realiza un estudio clasificatorio de las asignaturas matriculadas en la carrera de Administración y Dirección de Empresas de la UOC en relación a su resultado. Se proponen diferentes métodos y modelos de comprensión del entorno en el que se realiza el estudio.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Reuse of information for selection of methods and operating conditions for drying of pharmaceutical materials

Drying is a major step in the manufacturing process in pharmaceutical industries, and the selection of dryer and operating conditions are sometimes a bottleneck. In spite of difficulties, the bottlenecks are taken care of with utmost care due to good manufacturing practices (GMP) and industries' image in the global market. The purpose of this work is to research the use of existing knowledge for the selection of dryer and its operating conditions for drying of pharmaceutical materials with the help of methods like case-based reasoning and decision trees to reduce time and expenditure for research. The work consisted of two major parts as follows: Literature survey on the theories of spray dying, case-based reasoning and decision trees; working part includes data acquisition and testing of the models based on existing and upgraded data. Testing resulted in a combination of two models, case-based reasoning and decision trees, leading to more specific results when compared to conventional methods.

Mobile Subscription Acquisition and Retention Costs - An Actvity-Based Model

The objective of this master’s thesis was to develop a model for mobile subscription acquisition cost, SAC, and mobile subscription retention cost, SRC, by applying activity-based cost accounting principles. The thesis was conducted as a case study for a telecommunication company operating on the Finnish telecommunication market. In addition to activity-based cost accounting there were other theories studied and applied in order to establish a theory framework for this thesis. The concepts of acquisition and retention were explored in a broader context with the concepts of customer satisfaction, loyalty and profitability and eventually customer relationship management to understand the background and meaning of the theme of this thesis. The utilization of SAC and SRC information is discussed through the theories of decision making and activity-based management. Also, the present state and future needs of SAC and SRC information usage at the case company as well as the functions of the company were examined by interviewing some members of the company personnel. With the help of these theories and methods it was aimed at finding out both the theory-based and practical factors which affect the structure of the model. During the thesis study it was confirmed that the existing SAC and SRC model of the case company should be used as the basis in developing the activity-based model. As a result the indirect costs of the old model were transformed into activities and the direct costs were continued to be allocated directly to acquisition of new subscriptions and retention of old subscriptions. The refined model will enable managing the subscription acquisition, retention and the related costs better through the activity information. During the interviews it was found out that the SAC and SRC information is also used in performance measurement and operational and strategic planning. SAC and SRC are not fully absorbed costs and it was concluded that the model serves best as a source of indicative cost information. This thesis does not include calculating costs. Instead, the refined model together with both the theory-based and interview findings concerning the utilization of the information produced by the model will serve as a framework for the possible future development aiming at completing the model.

Data mining and mall users profile

Marketing scholars have suggested a need for more empirical research on consumer response to malls, in order to have a better understanding of the variables that explain the behavior of the consumers. The segmentation methodology CHAID (Chi-square automatic interaction detection) was used in order to identify the profiles of consumers with regard to their activities at malls, on the basis of socio-demographic variables and behavioral variables (how and with whom they go to the malls). A sample of 790 subjects answered an online questionnaire. The CHAID analysis of the results was used to identify the profiles of consumers with regard to their activities at malls. In the set of variables analyzed the transport used in order to go shopping and the frequency of visits to centers are the main predictors of behavior in malls. The results provide guidelines for the development of effective strategies to attract consumers to malls and retain them there.

Remote Condition Monitoring and Asset Management in Offshore Power Systems

Condition monitoring systems for physical assets are constantly becoming more and more common in the industrial sector. At the same time an increasing portion of asset monitoring systems are being remotely supported. As global competitors are actively developing solutions for condition monitoring and condition-based maintenance, which it enables, Wärtsilä too feels the pressure to provide customers with more sophisticated condition-based maintenance solutions. The main aim of this thesis study is to consider Wärtsilä remote condition monitoring solutions and how they relate to similar solutions from other suppliers and end customers’ needs, in the context of offshore assets. A theoretical study is also included in the thesis, where the concepts of condition monitoring, condition-based maintenance, maintenance management and physical asset management are introduced.