Papaya seedlings growth using a low-cost, automatic watering controller

The study assessed growth and physiological parameters of 'Sunrise Golden' and 'Tainung 01' papaya seedlings grown in 280mL plastic tubes and watered using a low-cost automatic irrigation system adjusted to operate at substrate water tension for starting irrigation (STI) of 3.0, 6.0 or 9.0 kPa. The water depths applied by the dripping system and drainage were monitored during germination and seedling growth. Germination, emergence velocity index (EVI), leaf area, plant height, shoot and root dry weight, stomatal conductance, relative water content (RWC) and relative chlorophyll content (RCC) were evaluated. Soil nutrient levels were determined by electrical conductivity (EC). Water use efficiency (WUE) corresponded to the ratio of plant dry mass to depth of water applied. STI settings did not affect papaya germination or EVI. System configuration to 3.0 and 6.0 kPa STI exhibited the highest drainage and lowest EC and RCC, indicating soil nutrient loss and plant nutrient deficiency. Drainage was greater in tubes planted with the 'Tainung 01' variety, which developed smaller root systems and lower stomatal conductance than 'Sunrise Golden' seedlings. The highest values for shoot dry weight and WEU were obtained at 6.0 kPa STI for 'Sunrise Golden' (0.62 g and 0.69 g L-1) and at 9.0 kPa in 'Tainung 01' (0.35 g and 0.82 g L-1). RWC at 9.0 kPa STI was lower than at 3.0 kPa in both varieties. The results indicate that the low-cost technology developed for irrigation automation is promising. Even so, new studies are needed to evaluate low-flow irrigation systems as well as the nutrient and water needs of different papaya varieties.

Alzheimer Disease Diagnosis based on Automatic Spontaneous Speech Analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to improvement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units.

PURPOSE: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. METHOD: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). RESULTS: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. CONCLUSION: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information.

Representations of work and worker in the three generations of knowledge management. Critical Discourse Analysis of selected texts.

The thesis studies the representations of different elements of contemporary work as present in Knowledge Management (KM). KM is approached as management discourse that is seen to affect and influence managerial practices in organizations. As representatives of KM discourse four journal articles are analyzed, using the methodology of Critical Discourse Analysis and the framework of Critical Management Studies, with a special emphasis on the question of structure and agency. The results of the analysis reveal that structural elements such as information technology and organizational structures are strongly present in the most influential KM representations, making their improvement also a desirable course of action for managers. In contrast agentic properties are not in a central role, they are subjugated to structural constraints of varying kind and degree. The thesis claims that one such constraint is KM discourse itself, influencing managerial and organizational choices and decision making. The thesis concludes that the way human beings are represented, studied and treated in management studies such as KM needs to be re-examined. Pro gradu-tutkielmassa analysoidaan työhön ja sen tekijään liittyviä representaatioita Tietojohtamisen kirjallisuudessa. Tietojohtamista tarkastellaan liikkeenjohdollisena diskurssina, jolla nähdään olevan vaikutus organisaatioiden päätöksentekoon ja toimintaan. Tutkielmassa analysoidaan neljä Tietojohtamisen tieteellistä artikkelia, käyttäen metodina kriittistä diskurssianalyysiä. Tutkielman viitekehyksenä on kriittinen liikkeenjohdon tutkimus. Lisäksi työssä pohditaan kysymystä rakenteen ja toimijan välisestä vuorovaikutuksesta. Tutkielman analyysi paljastaa, että tietojohtamisen vaikutusvaltaisimmat representaatiot painottavat rakenteellisia tekijöitä, kuten informaatioteknologiaa ja organisaatiorakenteita. Tämän seurauksena mm. panostukset em. tekijöihin nähdään organisaatioissa toivottavana toimintana. Vastaavasti representaatiot jotka painottavat yksilöitä ja toimintaa ovat em. tekijöille alisteisessa asemassa. Tapaa, jolla yksilöitä kuvataan ja käsitellään Tietojohtamisen diskurssissa, tulisikin laajentaa ja monipuolistaa.

Automatic Brain Extraction in Fetal MRI using Multi-Atlas-based Segmentation

In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favorably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality.

A conceptual model for grid learning services automatic composition

Peer-reviewed