Nitrogen replacement value of alfalfa to corn and wheat under irrigated Mediterranean conditions

In crop rotations that include alfalfa (Medicago sativa L.), agronomic and environmental concerns mean that it is important to determine the N fertilizer contribution of this legume for subsequent crops in order to help to increase the sustainability of cropping systems. To determine the N fertilizer replacement value (FRV) of a 2-yr alfalfa crop on subsequent crops of corn (Zea mays L.) followed by wheat (Triticum aestivum L.) under irrigated Mediterranean conditions, two 4-yr rotations (alfalfa-corn-wheat and corn-corn-corn-wheat) were conducted from 2001 to 2004 in a Typic Xerofluvent soil. Corn yields were compared after two years of alfalfa and a third year of corn under monoculture and wheat yields were also compared after both rotations. Corn production after alfalfa outyielded monoculture corn at all four rates of N fertilizer application analyzed (0, 100, 200 and 300 kg N/ha). The FRV of 2-yr alfalfa for corn was about 160 kg N/ha. Wheat grown after the alfalfa-corn rotation outyielded that grown after corn under monoculture at both the rates of N studied (0 and 100 kg N/ha). The FRV of alfalfa for wheat following alfalfa-corn was about 76 kg N/ha. Soil NO3 -N content after alfalfa was greater than with the corn monoculture at all rates of N fertilizer application and this higher value persisted during the second crop after alfalfa. This was probably one of the reasons for the better yields associated with the alfalfa rotation. These results make a valuable contribution to irrigated agriculture under mediterranean conditions, show reasons for interest in rotating alfalfa with corn, and explain how it is possible to make savings when applying N fertilizer.

Software Security Design and Testing

Elektroninen kaupankäynti ja pankkipalvelut ovat herättäneet toiminnan jatkuvuuden kannalta erittäin kriittisen kysymyksen siitä, kuinka näitä palveluja pystytään suojaamaan järjestäytynyttä rikollisuutta ja erilaisia hyväksikäyttöjä vastaan.

Description and implementation of cut calculation-method in a sawmill

Työn tavoitteena oli kuvata ja ottaa käyttöön sahauseräkohtaisen kannattavuuden laskentamenetelmä sahalle, sekä tehdä laskentamalli menetelmän tueksi. Sahauksen peruskäsitteiden jälkeen työssä on esitelty sahan tuotantoprosessi. Tuotantoprosessi on kuvattu kirjallisuuden ja asiantuntijoiden haastattelujen perusteella. Seuraavaksi kartoitettiin hyötyjä ja vaikutuksia, mitä laskentamenetelmältä odotetaan.. Kustannuslaskennan teoriaa selvitettiin kirjallisuuslähteitä käyttäen silmälläpitäen juuri tätä kehitettävää laskentamenetelmää. Lisäksi esiteltiin Uimaharjun sahalla käytettävät ja laskentaan liittyvät laskenta- ja tietojärjestelmät.Nykyisin sahalla ei ole minkäänlaista menetelmää sahauseräkohtaisen tuloksen laskemiseksi. Pienillä muutoksilla sahan tietojärjestelmään ja prosessikoneisiin voidaan sahauserä kuljettaa prosessin läpi niin, että jokaisessa prosessin vaiheessa sille saadaan kohdistettua tuotantotietoa. Eri vaiheista saatua tietoa käyttämällä saadaan tarkasti määritettyä tuotteet, joita sahauserä tuotti ja paljonko tuotantoresursseja tuottamiseen kului. Laskentamalliin syötetään tuotantotietoja ja kustannustietoa ja saadaan vastaukseksi sahauserän taloudellinen tulos.Toimenpide ehdotuksena esitetään lisätutkimusta tuotantotietojen automaattisesta keräämisestä manuaalisen työn ja virheiden poistamiseksi. Suhteellisen pienillä panoksilla voidaan jokaiselle sahauserälle kerätä tuotantotiedot täysin automaattisesti. Lisäksi kehittämäni laskentamallin tilalle tulisi hankkia sovellus, joka käyttäisi paremmin hyväksi nykyisiä tietojärjestelmiä ja poistaisi manuaalisen työvaiheen laskennassa.

Gaining momentum: New options and opportunities for the treatment of advanced melanoma.

Before 2011, patients with advanced or metastatic melanoma had a particularly poor long-term prognosis. Since traditional treatments failed to confer a survival benefit, patients were preferentially entered into clinical trials of investigational agents. A greater understanding of the epidemiology and biology of disease has underpinned the development of newer therapies, including six agents that have been approved in the EU, US and/or Japan: a cytotoxic T-lymphocyte antigen-4 inhibitor (ipilimumab), two programmed cell death-1 receptor inhibitors (nivolumab and pembrolizumab), two BRAF inhibitors (vemurafenib and dabrafenib) and a MEK inhibitor (trametinib). The availability of these treatments has greatly improved the outlook for patients with advanced melanoma; however, a major consideration for physicians is now to determine how best to integrate these agents into clinical practice. Therapeutic decisions are complicated by the need to consider patient and disease characteristics, and individual treatment goals, alongside the different efficacy and safety profiles of agents with varying mechanisms of action. Long-term survival, an outcome largely out of reach with traditional systemic therapies, is now a realistic goal, creating the additional need to re-establish how clinical benefit is evaluated. In this review we summarise the current treatment landscape in advanced melanoma and discuss the promise of agents still in development. We also speculate on the future of melanoma treatment and discuss how combination and sequencing approaches may be used to optimise patient care in the future.

Systems Medicine and Integrated Care to Combat Chronic Noncommunicable Diseases.

We propose an innovative, integrated, cost-effective health system to combat major non-communicable diseases (NCDs), including cardiovascular, chronic respiratory, metabolic, rheumatologic and neurologic disorders and cancers, which together are the predominant health problem of the 21st century. This proposed holistic strategy involves comprehensive patient-centered integrated care and multi-scale, multi-modal and multi-level systems approaches to tackle NCDs as a common group of diseases. Rather than studying each disease individually, it will take into account their intertwined gene-environment, socio-economic interactions and co-morbidities that lead to individual-specific complex phenotypes. It will implement a road map for predictive, preventive, personalized and participatory (P4) medicine based on a robust and extensive knowledge management infrastructure that contains individual patient information. It will be supported by strategic partnerships involving all stakeholders, including general practitioners associated with patient-centered care. This systems medicine strategy, which will take a holistic approach to disease, is designed to allow the results to be used globally, taking into account the needs and specificities of local economies and health systems.

Comparison of Inoculation with the InoqulA and WASP Automated Systems with Manual Inoculation.

The quality of sample inoculation is critical for achieving an optimal yield of discrete colonies in both monomicrobial and polymicrobial samples to perform identification and antibiotic susceptibility testing. Consequently, we compared the performance between the InoqulA (BD Kiestra), the WASP (Copan), and manual inoculation methods. Defined mono- and polymicrobial samples of 4 bacterial species and cloudy urine specimens were inoculated on chromogenic agar by the InoqulA, the WASP, and manual methods. Images taken with ImagA (BD Kiestra) were analyzed with the VisionLab version 3.43 image analysis software to assess the quality of growth and to prevent subjective interpretation of the data. A 3- to 10-fold higher yield of discrete colonies was observed following automated inoculation with both the InoqulA and WASP systems than that with manual inoculation. The difference in performance between automated and manual inoculation was mainly observed at concentrations of >10(6) bacteria/ml. Inoculation with the InoqulA system allowed us to obtain significantly more discrete colonies than the WASP system at concentrations of >10(7) bacteria/ml. However, the level of difference observed was bacterial species dependent. Discrete colonies of bacteria present in 100- to 1,000-fold lower concentrations than the most concentrated populations in defined polymicrobial samples were not reproducibly recovered, even with the automated systems. The analysis of cloudy urine specimens showed that InoqulA inoculation provided a statistically significantly higher number of discrete colonies than that with WASP and manual inoculation. Consequently, the automated InoqulA inoculation greatly decreased the requirement for bacterial subculture and thus resulted in a significant reduction in the time to results, laboratory workload, and laboratory costs.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Combining Generic Programming and Service-Oriented Architectures for the Effective and Timely Development of Complex e-Learning Systems

Peer-reviewed

Case-Crossover Analysis of Air Pollution Health Effects: A Systematic Review of Methodology and Application

Case-crossover is one of the most used designs for analyzing the health-related effects of air pollution. Nevertheless, no one has reviewed its application and methodology in this context. Objective: We conducted a systematic review of case-crossover (CCO) designs used to study the relationship between air pollution and morbidity and mortality, from the standpoint of methodology and application.Data sources and extraction: A search was made of the MEDLINE and EMBASE databases.Reports were classified as methodologic or applied. From the latter, the following information was extracted: author, study location, year, type of population (general or patients), dependent variable(s), independent variable(s), type of CCO design, and whether effect modification was analyzed for variables at the individual level. Data synthesis: The review covered 105 reports that fulfilled the inclusion criteria. Of these, 24 addressed methodological aspects, and the remainder involved the design’s application. In the methodological reports, the designs that yielded the best results in simulation were symmetric bidirectional CCO and time-stratified CCO. Furthermore, we observed an increase across time in the use of certain CCO designs, mainly symmetric bidirectional and time-stratified CCO. The dependent variables most frequently analyzed were those relating to hospital morbidity; the pollutants most often studied were those linked to particulate matter. Among the CCO-application reports, 13.6% studied effect modification for variables at the individual level.Conclusions: The use of CCO designs has undergone considerable growth; the most widely used designs were those that yielded better results in simulation studies: symmetric bidirectional and time-stratified CCO. However, the advantages of CCO as a method of analysis of variables at the individual level are put to little use

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.

APPLICATION OF SULFONIC ACID FUNCTIONALIZED NANOPOROUS SILICA (SBA-Pr-SO3H) FOR THE PREPARATION OF 4,6-DIARYLPYRIMIDIN-2(1H)-ONES

In this work, we report the Biginelli-type reaction between various aldehydes, acetophenones and urea systems in the presence of sulfonic acid functionalized silica (SBA-Pr-SO3H) under solvent-free conditions, which led to 4,6-diarylpyrimidin-2(1H)-ones derivatives. SBA-Pr-SO3H with a pore size of 6 nm was found to be an efficient heterogeneous solid acid catalyst for this reaction which led to high product yields, was environmentally benign with short reaction times and easy handling.

Tiedon hajautus johtamisjärjestelmässä ja DDS-teknologia

Verkostokeskeisessä sodankäynnissä tietojärjestelmien suurimpana haasteena on oikean tiedon hajauttaminen oikeaan paikkaan ja aikaan. Tietojärjestelmissä esitettävän ilmatilannekuvan tulee vastata reaalimaailman tilannetta parhaalla mahdollisella tavalla. Ilmatorjunnassa reaaliaikaisuus nousee erityisen suureen rooliin nopeasti liikkuvien kohteiden takia. Tämä diplomityö on tehty Insta DefSec Oy:ssä liittyen johtamisjärjestelmän uudistamishankkeeseen. Työn vaatimuksina olivat standardeihin perustuvat ratkaisut, joista keskeisimmäksi nousi Data Distribution Service -standardi (DDS) ja sen hyödyntäminen osana johtamisjärjestelmän tiedon hajautusta. Työssä esitellään johtamisjärjestelmien tiedon hajautukseen liittyviä haasteita sekä paikallisessa että maantieteellisesti hajautetussa toimintaympäristössä. Työssä toteutettiin liityntäohjelmisto nykyisen ja uuden johtamisjärjestelmän välille. Liityntäohjelmiston tehtävänä on tuottaa reaaliaikaista ilmatilannekuvaa nykyisestä johtamisjärjestelmästä uuteen johtamisjärjestelmään. DDS-standardin toteuttavana välikerrosarkkitehtuurina käytettiin OpenSplice DDS -tuotetta. Valittu teknologia tarjoaa edistykselliset julkaisija–tilaaja-mallin mukaiset menetelmät tiedon reaaliaikaiseen hajauttamiseen. DDS:n arkkitehtuuri ja palvelun laadun mekanismit mahdollistavat tiedon hajautuksen sodanajan johtamisjärjestelmille.

Progress of soybean charcoal rot under tillage and no-tillage systems in Brazil

The increase in incidence of charcoal rot caused by Macrophomina phaseolina on soybeans (Glycine max) was followed four seasons in conventional and no-till cropping systems. In the 1997/98 and 2000/01 seasons, total precipitation between sowing and harvest reached 876.3 and 846.9 mm, respectively. For these seasons, disease incidence did not differ significantly between the no-till and conventional systems. In 1998/99 and 1999/00 precipitation totaled 689.9 and 478.3 mm, respectively. In 1998/99, in the no-till system, the disease incidence was 43.7% and 53.1% in the conventional system. In 1999/00 the final incidence was 68.7% and 81.2% for the no-till and conventional systems, respectively. For these two seasons, precipitation was lower than that required for soybean crops (840 mm), and the averages of disease incidence were significantly higher in the conventional system. The concentration of microsclerotia in soil samples was higher in samples collected in conventional system at 0 - 10 cm depth. However, analysis of microsclerotia in roots showed that in years with adequate rain no difference was detected. In dry years, however, roots from plants developed under the conventional system had significantly more microsclerotia. Because of the wide host range of M. phaseolina and the long survival times of the microsclerotia, crop rotation would probably have little benefit in reducing charcoal rot. Under these study conditions it may be a better alternative to suppress charcoal rot by using the no-till cropping system to conserve soil moisture and reduce disease progress.

Use Of Systems Approach with Total Quality Management in Industrial Organizations

The objective of the work has been to study why systems thinking should be used in combination with TQM, what are the main benefits of the integration and how it could best be done. The work analyzes the development of systems thinking and TQM with time and the main differences between them. The work defines prerequisites for adopting a systems approach and the organizational factors which embody the development of an efficient learning organization. The work proposes a model based on combination of an interactive management model and redesign to be used for application of systems approach with TQM in practice. The results of the work indicate that there are clear differences between systems thinking and TQM which justify their combination. Systems approach provides an additional complementary perspective to quality management. TQM is focused on optimizing operations at the operational level while interactive management and redesign of organization are focused on optimization operations at the conceptual level providing a holistic system for value generation. The empirical study demonstrates the applicability of the proposed model in one case study company but its application is tenable and possible also beyond this particular company. System dynamic modeling and other systems based techniques like cognitive mapping are useful methods for increasing understanding and learning about the behavior of systems. The empirical study emphasizes the importance of using a proper early warning system.

EDTA- and DTPA-functionalized silica gel and chitosan adsorbents for the removal of heavy metals from aqueous solutions

Adsorbents functionalized with chelating agents are effective in removal of heavy metals from aqueous solutions. Important properties of such adsorbents are high binding affinity as well as regenerability. In this study, aminopolycarboxylic acid, EDTA and DTPA, were immobilized on the surface of silica gel, chitosan, and their hybrid materials to achieve chelating adsorbents for heavy metals such as Co(II), Ni(II), Cd(II), and Pb(II). New knowledge about the adsorption properties of EDTA- and DTPA-functionalizedadsorbents was obtained. Experimental work showed the effectiveness, regenerability, and stability of the studied adsorbents. Both advantages and disadvantages of the adsorbents were evaluated. For example, the EDTA-functionalized chitosan-silica hybrid materials combined the benefits of the silica gel and chitosan while at the same time diminishing their observed drawbacks. Modeling of adsorption kinetics and isotherms is an important step in design process. Therefore, several kinetic and isotherm models were introduced and applied in this work. Important aspects such as effect of error function, data range, initial guess values, and linearization were discussed and investigated. The selection of the most suitable model was conducted by comparing the experimental and simulated data as well as evaluating the correspondence between the theory behind the model and properties of the adsorbent. In addition, modeling of two-component data was conducted using various extended isotherms. Modeling results for both one- and twocomponent systems supported each other. Finally, application testing of EDTA- and DTPA-functionalized adsorbents was conducted. The most important result was the applicability of DTPA-functionalized silica gel and chitosan in the capturing of Co(II) from its aqueous EDTA-chelate. Moreover, these adsorbents were efficient in various solution matrices. In addition, separation of Ni(II) from Co(II) and Ni(II) and Pb(II) from Co(II) and Cd(II) was observed in two- and multimetal systems. Lastly, prior to their analysis, EDTA- and DTPA-functionalized silica gels were successfully used to preconcentrate metal ions from both pure and salty waters