Democratic dawn? : Civil society and elections in Myanmar 2010 – 2012

While the general elections in Myanmar in November 2010 were widely condemned, both national and international actors approached the by-elections of April 2012 as a political rite-de-passage to improve relations between the government and the opposition inside, and between the former pariah state and the international community outside the country. An undercurrent to the government-led transition process from an authoritarian to a formally more democratic regime was the development of a politically oriented civil society that found ways to engage in the electoral process. This article describes the emerging spaces of election-related civil society activism in the forms of civic and voter education, national election observation, and election-related agency in the media. Noting that, in particular, election observation offers connections for civil society to regional and international debates, the paper draws preliminary conclusions about further developments ahead of the general elections in Myanmar expected for 2015

MiX99-ohjelmistosta jalostettiin eläinarvostelujen vientituote

MTT:ssa kehitetty MiX99-tietokoneohjelmisto on herättänyt laajaa kansainvälistä kiinnostusta. Sen avulla voidaan arvioida aikaisempaa luotettavammin jalostustyössä tarvittavia eläinten jalostusarvoja.vo

Body and milk traits as indicators of dairy cow energy status in early lactation

The inclusion of feed intake and efficiency traits in dairy cow breeding goals can lead to increased risk of metabolic stress. An easy and inexpensive way to monitor postpartum energy status (ES) of cows is therefore needed. Cows' ES can be estimated by calculating the energy balance from energy intake and output and predicted by indicator traits such as change in body weight (Delta BW), change in body condition score (Delta BCS), milk fat:protein ratio (FPR), or milk fatty acid (FA) composition. In this study, we used blood plasma nonesterified fatty acids (NEFA) concentration as a biomarker for ES. We determined associations between NEFA concentration and ES indicators and evaluated the usefulness of body and milk traits alone, or together, in predicting ES of the cow. Data were collected from 2 research herds during 2013 to 2016 and included 137 Nordic Red dairy cows, all of which had a first lactation and 59 of which also had a second lactation. The data included daily body weight, milk yield, and feed intake and monthly BCS. Plasma samples for NEFA were collected twice in lactation wk 2 and 3 and once in wk 20. Milk samples for analysis of fat, protein, lactose, and FA concentrations were taken on the blood sampling days. Plasma NEFA concentration was higher in lactation wk 2 and 3 than in wk 20 (0.56 +/- 0.30, 0.43 +/- 0.22, and 0.13 +/- 0.06 mmol/L, respectively; all means +/- standard deviation). Among individual indicators, C18:1 cis-9 and the sum of C18:1 in milk had the highest correlations (r = 0.73) with NEFA. Seven multiple linear regression models for NEFA prediction were developed using stepwise selection. Of the models that included milk traits (other than milk FA) as well as body traits, the best fit was achieved by a model with milk yield, FPR, Delta BW, Delta BCS, FPR x Delta BW, and days in milk. The model resulted in a cross-validation coefficient of determination (R(2)cv) of 0.51 and a root mean squared error (RMSE) of 0.196 mmol/L. When only milk FA concentrations were considered in the model, NEFA prediction was more accurate using measurements from evening milk than from morning milk (R(2)cv = 0.61 vs. 0.53). The best model with milk traits contained FPR, C10:0, C14:0, C18:1 cis-9, C18:1 cis-9 x C14:0, and days in milk (R(2)cv = 0.62; RMSE = 0.177 mmol/L). The most advanced model using both milk and body traits gave a slightly better fit than the model with only milk traits (R(2)cv = 0.63; RMSE = 0.176 mmol/L). Our findings indicate that ES of cows in early lactation can be monitored with moderately high accuracy by routine milk measurements.Peer reviewe

Elopaino, kuntoluokka ja maidon pitoisuudet lehmän energiataseen kuvaajana

201

Lidar-pohjaisen yhtäaikaisen paikannuksen ja kartoituksen tarkkuus rakentamattomissa ympäristöissä

Simultaneous localization and mapping (SLAM) algorithms have received much attention due to their capability of constructing globally consistent maps without external measurements. SLAM implementations are commonly evaluated in urban environments, which is why they can be expected to work accurately in urban environments. However, SLAM would have meaningful applications also in rural environments. Therefore, the aim of this thesis is to evaluate the accuracy of a SLAM algorithm (LIO-SAM), in rural environments, as well as evaluate the effect of different algorithmic configurations on the accuracy. The accuracy is evaluated by comparing the trajectory created by the SLAM algorithm to a reference trajectory measured using a commonly employed lidar mapping method that is based on the global navigation satellite system (GNSS) and inertial measurements (IMU). By using the reference trajectory, the relative and absolute accuracy is evaluated for each SLAM configuration and dataset. Additionally, rigid alignment errors to world coordinates and local consistency errors are evaluated. The datasets of this thesis are captured using a purpose-built capture device, consisting of a Velodyne VLP-32C lidar, a Pixhawk 4 autopilot, and a u-blox ZED-F9P GNSS receiver. The datasets are captured in rural and urban environments using an unmanned aerial vehicle and a car, as well as by foot. The results show that the obtained accuracy of SLAM depends on the SLAM configuration and on the environment. Contrary to the initial expectation, rural environments did not always have worse accuracies than urban environments. However, based on the results, the accuracy of LIO-SAM deteriorates in environments in which only few features are visible in the direction of travel (such as open fields and highways). For datasets captured in rural environments, the smallest obtained absolute errors (i.e., best accuracies) were: 0.14m RMSE and 0.23deg RMSE, and the smallest relative errors were: 0.04m RMSE and 0.07deg RMSE.Yhtäaikainen paikannus ja kartoitus (SLAM) on ollut aktiivisen tutkimuksen kohteena johtuen sen kyvystä luoda globaalisti yhtenäisiä karttoja ilman ulkoisia mittauksia. Usein SLAM-toteutuksien tarkkuuksia testataan rakennetuissa ympäristöissä, joissa SLAM-algoritmien voidaan siten olettaa toimivan tarkasti. SLAM-algoritmeille löytyisi kuitenkin myös merkityksellisiä käyttökohteita rakentamattomista ympäristöistä. Tämän vuoksi tämän työn tavoitteena on määrittää LIO-SAM nimisen SLAM-algoritmin tarkkuus rakentamattomissa ympäristöissä käyttäen useampaa konfiguraatioita. Tarkkuus määritetään vertaamalla SLAM-algoritmin laskemaa trajektoria referenssitrajektoriin, joka on mitattu käyttäen lidar-kartoitukseen yleisesti käytettyä satelliittipaikannukseen (GNSS) ja inertiaalimittaukseen (IMU) pohjautuvaa menetelmää. Referenssitrajektoria käyttäen määritetään SLAM algoritmin suhteellinen ja absoluuttinen tarkkuus. Lisäksi määritetään virhe globaaleihin koordinaatteihin nähden ja paikallinen yhdenmukaisuusvirhe. Työssä arvioitavat aineistot on mitattu käyttäen työtä varten kehitettyä mittalaitetta, joka muodostuu Velodyne VLP-32C lidarista, Pixhawk 4 autopilotista ja u-blox ZED-F9P GNSS vastaanottimesta. Aineistot kerättiin rakennetuissa ja rakentamattomissa ympäristöissä käyttäen miehittämätöntä ilma-alusta ja autoa, sekä jalan kulkien. Tulokset osoittavat, että saavutettu tarkkuus riippuu konfiguraatiosta sekä ympäristöstä. Alkuperäisen oletuksen vastaisesti tarkkuus ei ollut aina rakentamattomissa ympäristöissä huonompi kuin rakennetuissa ympäristöissä. Tuloksista voidaan kuitenkin huomata, että LIO-SAM algoritmin tarkkuus heikkenee sellaisissa ympäristöissä, joissa on vain vähän vaihtelevuutta kulkusuuntaan nähden (esimerkiksi peltoaukeat ja moottoritiet). Rakentamattomissa ympäristöissä mitattujen aineistojen pienimmät absoluuttiset virheet (eli parhaat tarkkuudet) olivat: 0.14m RMSE ja 0.23deg RMSE, sekä pienimmät suhteelliset virheet olivat: 0.04m RMSE ja 0.07deg RMSE

Mid-infrared spectroscopic analysis of raw milk to predict the blood nonesterified fatty acid concentrations in dairy cows

In high-yielding dairy cattle, severe postpartum negative energy balance is often associated with metabolic and infectious disorders that negatively affect production, fertility, and welfare. Mobilization of adipose tissue associated with negative energy balance is reflected through an increased level of nonesterified fatty acids (NEFA) in the blood plasma. Earlier, identification of negative energy balance through detection of increased blood plasma NEFA concentration required laborious and stressful blood sampling. More recently, attempts have been made to predict blood NEFA concentration from milk samples. In this study, we aimed to develop and validate a model to predict blood plasma NEFA concentration using the milk mid-infrared (MIR) spectra that are routinely measured in the context of milk recording. To this end, blood plasma and milk samples were collected in wk 2, 3, and 20 postpartum for 192 lactations in 3 herds. The blood plasma samples were taken in the morning, and representative milk samples were collected during the morning and evening milk sessions on the same day. To predict plasma NEFA concentration from the milk MIR spectra, partial least squares regression models were trained on part of the observations from the first herd. The models were then thoroughly validated on all other observations of the first herd and on the observations of the 2 independent herds to explore their robustness and wide applicability. The final model could accurately predict blood plasma NEFA concentrations 1.2 mmol/L NEFA, the model clearly underestimated the true level. Additionally, we found that morning blood plasma NEFA levels were predicted with significantly higher accuracy using MIR spectra of evening milk samples compared with MIR spectra of morning samples, with root mean square error of prediction values of, respectively, 0.182 and 0.197 mmol/L, and R-2 values of 0.613 and 0.502. These results suggest a time delay between variations in blood plasma NEFA and related milk biomarkers. Based on the MIR spectra of evening milk samples, cows at risk for negative energy status, indicated by detrimental morning blood plasma NEFA levels (>0.6 mmol/L), could be identified with a sensitivity and specificity of, respectively, 0.831 and 0.800. As this model can be applied to millions of historical and future milk MIR spectra, it opens an opportunity for regular metabolic screening and improved resilience phenotyping.Peer reviewe

Lypsylehmien muuntokelpoisen energian saannin mallinnus rehuhyötysuhdeominaisuuksien kehittämistä varten

201

Reliability of breeding values for feed intake and feed efficiency traits in dairy cattle: When dry matter intake recordings are sparse under different scenarios

In Press, Corrected Proof201

The effects of dietary resin acid inclusion on productive, physiological and rumen microbiome responses of dairy cows during early lactation

Dairy cows have intense fluctuations in digestive, metabolic and hormonal systems around calving which predispose them to various disorders and health problems. The aim of the current experiment was to investigate feed and nutrient intake, rumen fermentation, rumen bacterial communities, milk production, milk fatty acid composition and plasma biomarker profiles of dairy cows to assess the modulation of these functions by in-feed resin acid inclusion. Thirty-six Nordic Red cows were used in a continuous feeding trial starting 3 weeks prepartum and lasting for 10 weeks into the lactation. The cows were fed grass silage ad libitum and the dietary treatments were 1) control with basal concentrate (CON), 2) CON supplemented with tall oil fatty acids (TOFA; 90 % fatty acids and 9% resin acids) at 7.0 g/cow/day and 3) CON supplemented with resin acid concentrate (RAC; 37.5% resin acids) at 1.7 g/cow/day. The mixture of resin acids in TOFA and RAC, consisting mostly of abietic and dehydroabietic acids, originated from coniferous tree species Pinus sylvestris L. and Picea abies L. Feed intake and milk production were measured throughout the experimental period. Milk and blood samples were collected at weeks 2, 3, 6 and 10, and rumen fluid was sampled at weeks 2 and 10 of lactation to analyse rumen fermentation and rumen bacterial communities. The dynamics in feed intake and milk production with progressing lactation showed typical curvilinear trends (P for timePeer reviewe