Design and testing of stand-specific bucking instructions for use on modern cut-to-length harvesters

This study addresses three important issues in tree bucking optimization in the context of cut-to-length harvesting. (1) Would the fit between the log demand and log output distributions be better if the price and/or demand matrices controlling the bucking decisions on modern cut-to-length harvesters were adjusted to the unique conditions of each individual stand? (2) In what ways can we generate stand and product specific price and demand matrices? (3) What alternatives do we have to measure the fit between the log demand and log output distributions, and what would be an ideal goodness-of-fit measure? Three iterative search systems were developed for seeking stand-specific price and demand matrix sets: (1) A fuzzy logic control system for calibrating the price matrix of one log product for one stand at a time (the stand-level one-product approach); (2) a genetic algorithm system for adjusting the price matrices of one log product in parallel for several stands (the forest-level one-product approach); and (3) a genetic algorithm system for dividing the overall demand matrix of each of the several log products into stand-specific sub-demands simultaneously for several stands and products (the forest-level multi-product approach). The stem material used for testing the performance of the stand-specific price and demand matrices against that of the reference matrices was comprised of 9 155 Norway spruce (Picea abies (L.) Karst.) sawlog stems gathered by harvesters from 15 mature spruce-dominated stands in southern Finland. The reference price and demand matrices were either direct copies or slightly modified versions of those used by two Finnish sawmilling companies. Two types of stand-specific bucking matrices were compiled for each log product. One was from the harvester-collected stem profiles and the other was from the pre-harvest inventory data. Four goodness-of-fit measures were analyzed for their appropriateness in determining the similarity between the log demand and log output distributions: (1) the apportionment degree (index), (2) the chi-square statistic, (3) Laspeyres quantity index, and (4) the price-weighted apportionment degree. The study confirmed that any improvement in the fit between the log demand and log output distributions can only be realized at the expense of log volumes produced. Stand-level pre-control of price matrices was found to be advantageous, provided the control is done with perfect stem data. Forest-level pre-control of price matrices resulted in no improvement in the cumulative apportionment degree. Cutting stands under the control of stand-specific demand matrices yielded a better total fit between the demand and output matrices at the forest level than was obtained by cutting each stand with non-stand-specific reference matrices. The theoretical and experimental analyses suggest that none of the three alternative goodness-of-fit measures clearly outperforms the traditional apportionment degree measure. Keywords: harvesting, tree bucking optimization, simulation, fuzzy control, genetic algorithms, goodness-of-fit

Developments and Microbiological applications in African foods: Emphasis on Nigerian Wara cheese

African indigenous foods have received limited research. Most of these indigenous foods are fermented and they form part of the rich nutritional culture of many groups in African countries. The industrialization and commercialisation of these indigenous African fermented foods should be preceded by a thorough scientific knowledge of their processing which can be vital in the elimination of hunger and poverty. This study highlighted emerging developments and the microbiology of cereal-based and cassava-based food products that constitute a major part of the human diet in most African countries. In addition, investigations were also carried out on the coagulant of the Calotropis procera plant used in traditional production of Nigerian Wara cheese and on the effects of adding a nisin producing Lactococcus lactis strain originating from human milk to Nigerian Wara cheese. Fermented cereal-based food such as ogi utilize popular African and readily available grains maize, millet or sorghum as substrates and is popular as a weaning diet in infants. In this study, the bulkiness caused by starch gelatinization was solved by amylase treatments in the investigation on cooked and fermented oat bran porridge. A similar treatment could reduce the viscosity of any cereal porridge. The properties of the Sodom apple leaves (Calotropis procera) extract in cheesemaking were studied. C. procera was affected by monovalent (K+ and Na+) and divalent (Mg2+ and Ca2+) cations during coagulation. The rennet strength of this coagulant was found to be 7 % compared to animal rennet at 35 °C. Increasing the incubation temperature to 70 °C increased the rennet strength 28-fold. The molecular weight of the partially purified protease was determined by SDS-PAGE and was confirmed by Zymography to be approximately 60 kilodaltons. The high proteolytic activity at 70 °C supported the suitability of the protease enzyme as a coagulant in future commercial production of Nigerian Wara cheese. It was also possible to extend the shelf life of Wara cheese by a nisin producing lactic acid bacteria Lactococcus lactis LAC309. The levels of nisin in both whey and curd fractions of Wara were investigated, results showed a 3 log reduction of toxicogenic Bacillus licheniformis spiked on Wara after 3 days. These studies are the first in Finland to promote the advancement of scientific knowledge in African foods. Recognizing these indigenous food products and an efficient transfer of technology from the developed countries to industrialize them are necessary towards a successful realization of the United Nations Millenium Development Program.

Data mining for telecommunications network log analysis

Telecommunications network management is based on huge amounts of data that are continuously collected from elements and devices from all around the network. The data is monitored and analysed to provide information for decision making in all operation functions. Knowledge discovery and data mining methods can support fast-pace decision making in network operations. In this thesis, I analyse decision making on different levels of network operations. I identify the requirements decision-making sets for knowledge discovery and data mining tools and methods, and I study resources that are available to them. I then propose two methods for augmenting and applying frequent sets to support everyday decision making. The proposed methods are Comprehensive Log Compression for log data summarisation and Queryable Log Compression for semantic compression of log data. Finally I suggest a model for a continuous knowledge discovery process and outline how it can be implemented and integrated to the existing network operations infrastructure.

Anti-TNF treatment in juvenile idiopathic arthritis and associated uveitis : Clinical perspectives on growth, uveitis, and drug survival

Juvenile idiopathic arthritis (JIA) is a heterogeneous group of childhood chronic arthritides, associated with chronic uveitis in 20% of cases. For JIA patients responding inadequately to conventional disease-modifying anti-rheumatic drugs (DMARDs), biologic therapies, anti-tumor necrosis factor (anti-TNF) agents are available. In this retrospective multicenter study, 258 JIA-patients refractory to DMARDs and receiving biologic agents during 1999-2007 were included. Prior to initiation of anti-TNFs, growth velocity of 71 patients was delayed in 75% and normal in 25%. Those with delayed growth demonstrated a significant increase in growth velocity after initiation of anti-TNFs. Increase in growth rate was unrelated to pubertal growth spurt. No change was observed in skeletal maturation before and after anti-TNFs. The strongest predictor of change in growth velocity was growth rate prior to anti-TNFs. Change in inflammatory activity remained a significant predictor even after decrease in glucocorticoids was taken into account. In JIA-associated uveitis, impact of two first-line biologic agents, etanercept and infliximab, and second-line or third-line anti-TNF agent, adalimumab, was evaluated. In 108 refractory JIA patients receiving etanercept or infliximab, uveitis occurred in 45 (42%). Uveitis improved in 14 (31%), no change was observed in 14 (31%), and in 17 (38%) uveitis worsened. Uveitis improved more frequently (p=0.047) and frequency of annual uveitis flares was lower (p=0.015) in those on infliximab than in those on etanercept. In 20 patients taking adalimumab, 19 (95%) had previously failed etanercept and/or infliximab. In 7 patients (35%) uveitis improved, in one (5%) worsened, and in 12 (60%) no change occurred. Those with improved uveitis were younger and had shorter disease duration. Serious adverse events (AEs) or side-effects were not observed. Adalimumab was effective also in arthritis. Long-term drug survival (i.e. continuation rate on drug) with etanercept (n=105) vs. infliximab (n=104) was at 24 months 68% vs. 68%, and at 48 months 61% vs. 48% (p=0.194 in log-rank analysis). First-line anti-TNF agent was discontinued either due to inefficacy (etanercept 28% vs. infliximab 20%, p=0.445), AEs (7% vs. 22%, p=0.002), or inactive disease (10% vs. 16%, p=0.068). Females, patients with systemic JIA (sJIA), and those taking infliximab as the first therapy were at higher risk for treatment discontinuation. One-third switched to the second anti-TNF agent, which was discontinued less often than the first. In conclusion, in refractory JIA anti-TNFs induced enhanced growth velocity. Four-year treatment survival was comparable between etanercept and infliximab, and switching from first-line to second-line agent a reasonable therapeutic option. During anti-TNF treatment, one-third with JIA-associated anterior uveitis improved.

Discriminative learning of Bayesian networks via factorized conditional log-likelihood

We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (ˆfCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient estimation of the optimal parameters, achieving the same time and space complexity as the traditional log-likelihood scoring criterion. The resulting criterion has an information-theoretic interpretation based on interaction information, which exhibits its discriminative nature. To evaluate the performance of the proposed criterion, we present an empirical comparison with state-of-the-art classifiers. Results on a large suite of benchmark data sets from the UCI repository show that ˆfCLL-trained classifiers achieve at least as good accuracy as the best compared classifiers, using significantly less computational resources.