Weed mapping using techniques of precision agriculture

The aim of this study was to identify and map the weed population in a no-tillage area. Geostatistical techniques were used in the mapping in order to assess this information as a tool for the localized application of herbicides. The area of study is 58.08 hectares wide and was sampled in a fixed square grid (which point spaced 50 m, 232 points) using a GPS receiver. In each point the weeds species and population were analyzed in a square with a 0.25 m2 fixed area. The species Ipomoea grandifolia, Gnaphalium spicatum, Richardia spp. and Emilia sonchifolia have presented no spatial dependence. However, the species Conyza spp., C. echinatus and E. indica have shown a spatial correlation. Among the models tested, the spherical model has shown had a better fit for Conyza spp. and Eleusine indica and the Gaussian model for Cenchrus echinatus. The three species have a clumped spatial distribution. The mapping of weeds can be a tool for localized control, making herbicide use more rational, effective and economical.

SobekCM’s Community Ecosystems & Socio-Technical Practices

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Context-specific independence in graphical models

The theme of this thesis is context-speci c independence in graphical models. Considering a system of stochastic variables it is often the case that the variables are dependent of each other. This can, for instance, be seen by measuring the covariance between a pair of variables. Using graphical models, it is possible to visualize the dependence structure found in a set of stochastic variables. Using ordinary graphical models, such as Markov networks, Bayesian networks, and Gaussian graphical models, the type of dependencies that can be modeled is limited to marginal and conditional (in)dependencies. The models introduced in this thesis enable the graphical representation of context-speci c independencies, i.e. conditional independencies that hold only in a subset of the outcome space of the conditioning variables. In the articles included in this thesis, we introduce several types of graphical models that can represent context-speci c independencies. Models for both discrete variables and continuous variables are considered. A wide range of properties are examined for the introduced models, including identi ability, robustness, scoring, and optimization. In one article, a predictive classi er which utilizes context-speci c independence models is introduced. This classi er clearly demonstrates the potential bene ts of the introduced models. The purpose of the material included in the thesis prior to the articles is to provide the basic theory needed to understand the articles.

Efficient Optimization Algorithms for Nonlinear Data Analysis

Identification of low-dimensional structures and main sources of variation from multivariate data are fundamental tasks in data analysis. Many methods aimed at these tasks involve solution of an optimization problem. Thus, the objective of this thesis is to develop computationally efficient and theoretically justified methods for solving such problems. Most of the thesis is based on a statistical model, where ridges of the density estimated from the data are considered as relevant features. Finding ridges, that are generalized maxima, necessitates development of advanced optimization methods. An efficient and convergent trust region Newton method for projecting a point onto a ridge of the underlying density is developed for this purpose. The method is utilized in a differential equation-based approach for tracing ridges and computing projection coordinates along them. The density estimation is done nonparametrically by using Gaussian kernels. This allows application of ridge-based methods with only mild assumptions on the underlying structure of the data. The statistical model and the ridge finding methods are adapted to two different applications. The first one is extraction of curvilinear structures from noisy data mixed with background clutter. The second one is a novel nonlinear generalization of principal component analysis (PCA) and its extension to time series data. The methods have a wide range of potential applications, where most of the earlier approaches are inadequate. Examples include identification of faults from seismic data and identification of filaments from cosmological data. Applicability of the nonlinear PCA to climate analysis and reconstruction of periodic patterns from noisy time series data are also demonstrated. Other contributions of the thesis include development of an efficient semidefinite optimization method for embedding graphs into the Euclidean space. The method produces structure-preserving embeddings that maximize interpoint distances. It is primarily developed for dimensionality reduction, but has also potential applications in graph theory and various areas of physics, chemistry and engineering. Asymptotic behaviour of ridges and maxima of Gaussian kernel densities is also investigated when the kernel bandwidth approaches infinity. The results are applied to the nonlinear PCA and to finding significant maxima of such densities, which is a typical problem in visual object tracking.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Statistical segmentation methods and color variance analysis of retinal images

In this research, the effectiveness of Naive Bayes and Gaussian Mixture Models classifiers on segmenting exudates in retinal images is studied and the results are evaluated with metrics commonly used in medical imaging. Also, a color variation analysis of retinal images is carried out to find how effectively can retinal images be segmented using only the color information of the pixels.

Ectopic development of skeletal muscle induced by subcutaneous transplant of rat satellite cells

The present study analyzes the ectopic development of the rat skeletal muscle originated from transplanted satellite cells. Satellite cells (10(6) cells) obtained from hindlimb muscles of newborn female 2BAW Wistar rats were injected subcutaneously into the dorsal area of adult male rats. After 3, 7, and 14 days, the transplanted tissues (N = 4-5) were processed for histochemical analysis of peripheral nerves, inactive X-chromosome and acetylcholinesterase. Nicotinic acetylcholine receptors (nAChRs) were also labeled with tetramethylrhodamine-labeled alpha-bungarotoxin. The development of ectopic muscles was successful in 86% of the implantation sites. By day 3, the transplanted cells were organized as multinucleated fibers containing multiple clusters of nAChRs (N = 2-4), resembling those from non-innervated cultured skeletal muscle fibers. After 7 days, the transplanted cells appeared as a highly vascularized tissue formed by bundles of fibers containing peripheral nuclei. The presence of X chromatin body indicated that subcutaneously developed fibers originated from female donor satellite cells. Differently from the extensor digitorum longus muscle of adult male rat (87.9 ± 1.0 µm; N = 213), the diameter of ectopic fibers (59.1 µm; N = 213) did not obey a Gaussian distribution and had a higher coefficient of variation. After 7 and 14 days, the organization of the nAChR clusters was similar to that of clusters from adult innervated extensor digitorum longus muscle. These findings indicate the histocompatibility of rats from 2BAW colony and that satellite cells transplanted into the subcutaneous space of adult animals are able to develop and fuse to form differentiated skeletal muscle fibers.

Contribution of matrix vesicles and alkaline phosphatase to ectopic bone formation

Endochondral calcification involves the participation of matrix vesicles (MVs), but it remains unclear whether calcification ectopically induced by implants of demineralized bone matrix also proceeds via MVs. Ectopic bone formation was induced by implanting rat demineralized diaphyseal bone matrix into the dorsal subcutaneous tissue of Wistar rats and was examined histologically and biochemically. Budding of MVs from chondrocytes was observed to serve as nucleation sites for mineralization during induced ectopic osteogenesis, presenting a diameter with Gaussian distribution with a median of 306 ± 103 nm. While the role of tissue-nonspecific alkaline phosphatase (TNAP) during mineralization involves hydrolysis of inorganic pyrophosphate (PPi), it is unclear how the microenvironment of MV may affect the ability of TNAP to hydrolyze the variety of substrates present at sites of mineralization. We show that the implants contain high levels of TNAP capable of hydrolyzing p-nitrophenylphosphate (pNPP), ATP and PPi. The catalytic properties of glycosyl phosphatidylinositol-anchored, polidocanol-solubilized and phosphatidylinositol-specific phospholipase C-released TNAP were compared using pNPP, ATP and PPi as substrates. While the enzymatic efficiency (k cat/Km) remained comparable between polidocanol-solubilized and membrane-bound TNAP for all three substrates, the k cat/Km for the phosphatidylinositol-specific phospholipase C-solubilized enzyme increased approximately 108-, 56-, and 556-fold for pNPP, ATP and PPi, respectively, compared to the membrane-bound enzyme. Our data are consistent with the involvement of MVs during ectopic calcification and also suggest that the location of TNAP on the membrane of MVs may play a role in determining substrate selectivity in this micro-compartment.

Induced maturation of hepatic progenitor cellsin vitro

Hepatic progenitor cells (HPCs) are a potential cell source for liver cell transplantation but do not function like mature liver cells. We sought an effective and reliable method to induce HPC maturation. An immortalized HP14.5 albumin promoter-driven Gaussian luciferase (ALB-GLuc) cell line was established from HPCs isolated from fetal mouse liver of post coitus day 14.5 mice to investigate the effect of induction factors on ALB promoter. HP14.5 parental cells were cultured in DMEM with different combinations of 2% horse serum (HS), 0.1 µM dexamethasone (DEX), 10 ng/mL hepatic growth factor (HGF), and/or 20 ng/mL fibroblast growth factor 4 (FGF4). Trypan blue and crystal violet staining were used to assess cell proliferation with different induction conditions. Expression of hepatic markers was measured by semi-quantitative RT-PCR, Western blot, and immunofluorescence. Glycogen storage and metabolism were detected by periodic acid-Schiff and indocyanine green (ICG) staining. GLuc activity indicated ALB expression. The combination of 2% HS+0.1 µM Dex+10 ng/mL HGF+20 ng/mL FGF4 induced the highest ALB-GLuc activity. Cell proliferation decreased in 2% HS but increased by adding FGF4. Upon induction, and consistent with hepatocyte development, DLK, AFP, and CK19 expression decreased, while ALB, CK18, and UGT1A expression increased. The maturity markers tyrosine aminotransferase and apolipoprotein B were detected at days 3 and 6 post-induction, respectively. ICG uptake and glycogen synthesis were detectable at day 6 and increased over time. Therefore, we demonstrated that HPCs were induced to differentiate into functional mature hepatocytes in vitro, suggesting that factor-treated HPCs may be further explored as a means of liver cell transplantation.

Generative Part-Based Gabor Object Detector

Object detection is a fundamental task of computer vision that is utilized as a core part in a number of industrial and scientific applications, for example, in robotics, where objects need to be correctly detected and localized prior to being grasped and manipulated. Existing object detectors vary in (i) the amount of supervision they need for training, (ii) the type of a learning method adopted (generative or discriminative) and (iii) the amount of spatial information used in the object model (model-free, using no spatial information in the object model, or model-based, with the explicit spatial model of an object). Although some existing methods report good performance in the detection of certain objects, the results tend to be application specific and no universal method has been found that clearly outperforms all others in all areas. This work proposes a novel generative part-based object detector. The generative learning procedure of the developed method allows learning from positive examples only. The detector is based on finding semantically meaningful parts of the object (i.e. a part detector) that can provide additional information to object location, for example, pose. The object class model, i.e. the appearance of the object parts and their spatial variance, constellation, is explicitly modelled in a fully probabilistic manner. The appearance is based on bio-inspired complex-valued Gabor features that are transformed to part probabilities by an unsupervised Gaussian Mixture Model (GMM). The proposed novel randomized GMM enables learning from only a few training examples. The probabilistic spatial model of the part configurations is constructed with a mixture of 2D Gaussians. The appearance of the parts of the object is learned in an object canonical space that removes geometric variations from the part appearance model. Robustness to pose variations is achieved by object pose quantization, which is more efficient than previously used scale and orientation shifts in the Gabor feature space. Performance of the resulting generative object detector is characterized by high recall with low precision, i.e. the generative detector produces large number of false positive detections. Thus a discriminative classifier is used to prune false positive candidate detections produced by the generative detector improving its precision while keeping high recall. Using only a small number of positive examples, the developed object detector performs comparably to state-of-the-art discriminative methods.

Spatial variability of soil fertility and its relationship with seed physiological potential in a soybean production area

The correlation of soil fertility x seed physiological potential is very important in the area of seed technology but results published with that theme are contradictory. For this reason, this study to evaluate the correlations between soil chemical properties and physiological potential of soybean seeds. On georeferenced points, both soil and seeds were sampled for analysis of soil fertility and seed physiological potential. Data were assessed by the following analyses: descriptive statistics; Pearson's linear correlation; and geostatistics. The adjusted parameters of the semivariograms were used to produce maps of spatial distribution for each variable. Organic matter content, Mn and Cu showed significant effects on seed germination. Most variables studied presented moderate to high spatial dependence. Germination and accelerated aging of seeds, and P, Ca, Mg, Mn, Cu and Zn showed a better fit to spherical semivariogram: organic matter, pH and K had a better fit to Gaussian model; and V% and Fe showed a better fit to the linear model. The values for range of spatial dependence varied from 89.9 m for P until 651.4 m for Fe. These values should be considered when new samples are collected for assessing soil fertility in this production area.

Suomalaisen PK-yrityksen etabloitumisen kompleksisuus Venäjälle

Tässä diplomityössä perehdytään suomalaisten pk - yritysten kansainvälistymisen kompleksisuuteen Venäjälle. Tutkimuksen pääkohderyhmänä ovat Etelä-Karjalaiset kone – ja metallialan pienet ja keskisuuret yritykset. Tutkimuksessa selvitettiin myös Pietarin alueen suurten metalliyritysten etabloitumishalukkuutta Suomeen. Työn tavoitteena on tuottaa informaatiota kansainvälisen liiketoiminnan päätöksenteon tueksi. Työn tarkoituksena on myös selvittää lukijalle kansainvälistymiseen liittyvän kompleksisuuden ja yrityksen resurssien välistä yhteyttä. Työn yhtenä tuotoksena luotiin yksinkertainen malli, joka omalta osaltaan selittää haastavalle liiketoiminta-alueelle etabloitumista tavoittelevan yrityksen kokemaa kompleksisuutta ja sen yhteyttä yrityksen resursseihin.

An exploration of the experience of implementing the second step violence prevention curriculum in one primary general learning disabilities class /

Violence has always been a part of the human experience, and therefore, a popular topic for research. It is a controversial issue, mostly because the possible sources of violent behaviour are so varied, encompassing both biological and environmental factors. However, very little disagreement is found regarding the severity of this societal problem. Most researchers agree that the number and intensity of aggressive acts among adults and children is growing. Not surprisingly, many educational policies, programs, and curricula have been developed to address this concern. The research favours programs which address the root causes of violence and seek to prevent rather than provide consequences for the undesirable behaviour. But what makes a violence prevention program effective? How should educators choose among the many curricula on the market? After reviewing the literature surrounding violence prevention programs and their effectiveness, The Second Step Violence Prevention Curriculum surfaced as unique in many ways. It was designed to address the root causes of violence in an active, student-centred way. Empathy training, anger management, interpersonal cognitive problem solving, and behavioural social skills form the basis of this program. Published in 1992, the program has been the topic of limited research, almost entirely carried out using quantitative methodologies.The purpose of this study was to understand what happens when the Second Step Violence Prevention Curriculum is implemented with a group of students and teachers. I was not seeking a statistical correlation between the frequency of violence and program delivery, as in most prior research. Rather, I wished to gain a deeper understanding of the impact ofthe program through the eyes of the participants. The Second Step Program was taught to a small, primary level, general learning disabilities class by a teacher and student teacher. Data were gathered using interviews with the teachers, personal observations, staff reports, and my own journal. Common themes across the four types of data collection emerged during the study, and these themes were isolated and explored for meaning. Findings indicate that the program does not offer a "quick fix" to this serious problem. However, several important discoveries were made. The teachers feU that the program was effective despite a lack of concrete evidence to support this claim. They used the Second Step strategies outside their actual instructional time and felt it made them better educators and disciplinarians. The students did not display a marked change in their behaviour during or after the program implementation, but they were better able to speak about their actions, the source of their aggression, and the alternatives which were available. Although they were not yet transferring their knowledge into positive action,a heightened awareness was evident. Finally, staff reports and my own journal led me to a deeper understanding ofhow perception frames reality. The perception that the program was working led everyone to feel more empowered when a violent incident occurred, and efforts were made to address the cause rather than merely to offer consequences. A general feeling that we were addressing the problem in a productive way was prevalent among the staff and students involved. The findings from this investigation have many implications for research and practice. Further study into the realm of violence prevention is greatly needed, using a balance of quantitative and qualitative methodologies. Such a serious problem can only be effectively addressed with a greater understanding of its complexities. This study also demonstrates the overall positive impact of the Second Step Violence Prevention Curriculum and, therefore, supports its continued use in our schools.

A qualitative case study of the processes of peer education in a young adult tobacco control initiative, Leave the Pack Behind

Peer education involves peers offering credible and reliable information about sensitive life issues through the means of an informal peer group setting (Topping & Ehly, 1998). The purpose of this instrumental case study was to examine the processes of peer education through the exploration of two teams within a young adult tobacco control initiative, Leave the Pack Behind (LTPB). This qualitative case study examined two peer education teams over an eight-month period. Interviews, focus groups and observations were conducted with 12 participants across two peer education teams. Findings show the complexities of the processes of peer education including a connection between the stages of change and the changing role of the peer educator across stages of the empowerment process. Peer education teams and factors in the macro environment were also found to impact the process of peer education. This study provides a new definition for the process of peer education: peer education is a fluid process of knowledge exchange in which peer educators adopt different styles of facilitation as people move through stages of empowerment and change. This study contributes to the academic hterature upon the processes of peer education by providing a definition, a model and an overall understanding through an ecological and empowerment framework. The findings from this study suggest peer educators can be further trained to: use specific peer educational approaches that fit with student smoker's stage of change; better understand their position as a peer educator on the LTPB team; understand the reciprocal relationship between the macro environment and the peer education teams having an effect on one another.

Fluorescence spectra and ab initio theoretical studies of HCOOH /

A fluorescence excitation spectrum of formic acid monomer (HCOOH) , has been recorded in the 278-246 nm region and has been attributed to an n >7r* electron promotion in the anti conformer. The S^< S^ electronic origins of the HCOOH/HCOOD/DCOOH/DCOOD isotopomers were assigned to weak bands observed at 37431.5/37461.5/37445.5/37479.3 cm'''. From a band contour analysis of the 0°^ band of HCOOH, the rotational constants for the excited state were estimated: A'=1.8619, B'=0.4073, and C'=0.3730 cm'\ Four vibrational modes, 1/3(0=0), j/^(0-C=0) , J/g(C-H^^^) and i/,(0-H^yJ were observed in the spectrum. The activity of the antisymmetric aldehyde wagging and hydroxyl torsional modes in forming progressions is central to the analysis, leading to the conclusion that the two hydrogens are distorted from the molecular plane, 0-C=0, in the upper S. state. Ab initio calculations were performed at the 6-3 IG* SCF level using the Gaussian 86 system of programs to aid in the vibrational assignments. The computations show that the potential surface which describes the low frequency OH torsion (twisting motion) and the CH wagging (molecular inversion) motions is complex in the S^ excited electronic state. The OH and CH bonds were calculated to be twisted with respect to the 0-C=0 molecular frame by 63.66 and 4 5.76 degrees, respectively. The calculations predicted the existence of the second (syn) rotamer which is 338 cm'^ above the equilibrium configuration with OH and CH angles displaced from the plane by 47.91 and 41.32 degrees.