Climate change impacts on lakes: an integrated ecological perspective based on a multi-faceted approach, with special focus on shallow lakes

Freshwater ecosystems and their biodiversity are presently seriously threatened by global development and population growth, leading to increases in nutrient inputs and intensification of eutrophication-induced problems in receiving fresh waters, particularly in lakes. Climate change constitutes another threat exacerbating the symptoms of eutrophication and species migration and loss. Unequivocal evidence of climate change impacts is still highly fragmented despite the intensive research, in part due to the variety and uncertainty of climate models and underlying emission scenarios but also due to the different approaches applied to study its effects. We first describe the strengths and weaknesses of the multi-faceted approaches that are presently available for elucidating the effects of climate change in lakes, including space-for-time substitution, time series, experiments, palaeoecology and modelling. Reviewing combined results from studies based on the various approaches, we describe the likely effects of climate changes on biological communities, trophic dynamics and the ecological state of lakes. We further discuss potential mitigation and adaptation measures to counteract the effects of climate change on lakes and, finally, we highlight some of the future challenges that we face to improve our capacity for successful prediction.

Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology.

PURPOSE OF REVIEW: Current computational neuroanatomy based on MRI focuses on morphological measures of the brain. We present recent methodological developments in quantitative MRI (qMRI) that provide standardized measures of the brain, which go beyond morphology. We show how biophysical modelling of qMRI data can provide quantitative histological measures of brain tissue, leading to the emerging field of in-vivo histology using MRI (hMRI). RECENT FINDINGS: qMRI has greatly improved the sensitivity and specificity of computational neuroanatomy studies. qMRI metrics can also be used as direct indicators of the mechanisms driving observed morphological findings. For hMRI, biophysical models of the MRI signal are being developed to directly access histological information such as cortical myelination, axonal diameters or axonal g-ratio in white matter. Emerging results indicate promising prospects for the combined study of brain microstructure and function. SUMMARY: Non-invasive brain tissue characterization using qMRI or hMRI has significant implications for both research and clinics. Both approaches improve comparability across sites and time points, facilitating multicentre/longitudinal studies and standardized diagnostics. hMRI is expected to shed new light on the relationship between brain microstructure, function and behaviour, both in health and disease, and become an indispensable addition to computational neuroanatomy.

Evaluating the Performance of a Regional-Scale Photochemical Modelling System: Part I Ozone Predictions

We present a detailed evaluation of the seasonal performance of the Community Multiscale Air Quality (CMAQ) modelling system and the PSU/NCAR meteorological model coupled to a new Numerical Emission Model for Air Quality (MNEQA). The combined system simulates air quality at a fine resolution (3 km as horizontal resolution and 1 h as temporal resolution) in north-eastern Spain, where problems of ozone pollution are frequent. An extensive database compiled over two periods, from May to September 2009 and 2010, is used to evaluate meteorological simulations and chemical outputs. Our results indicate that the model accurately reproduces hourly and 1-h and 8-h maximum ozone surface concentrations measured at the air quality stations, as statistical values fall within the EPA and EU recommendations. However, to further improve forecast accuracy, three simple bias-adjustment techniques mean subtraction (MS), ratio adjustment (RA), and hybrid forecast (HF) based on 10 days of available comparisons are applied. The results show that the MS technique performed better than RA or HF, although all the bias-adjustment techniques significantly reduce the systematic errors in ozone forecasts.

Visual Measurement and Modelling of Tool Wear in Rough Turning

Huolimatta korkeasta automaatioasteesta sorvausteollisuudessa, muutama keskeinen ongelma estää sorvauksen täydellisen automatisoinnin. Yksi näistä ongelmista on työkalun kuluminen. Tämä työ keskittyy toteuttamaan automaattisen järjestelmän kulumisen, erityisesti viistekulumisen, mittaukseen konenäön avulla. Kulumisen mittausjärjestelmä poistaa manuaalisen mittauksen tarpeen ja minimoi ajan, joka käytetään työkalun kulumisen mittaukseen. Mittauksen lisäksi tutkitaan kulumisen mallinnusta sekä ennustamista. Automaattinen mittausjärjestelmä sijoitettiin sorvin sisälle ja järjestelmä integroitiin onnistuneesti ulkopuolisten järjestelmien kanssa. Tehdyt kokeet osoittivat, että mittausjärjestelmä kykenee mittaamaan työkalun kulumisen järjestelmän oikeassa ympäristössä. Mittausjärjestelmä pystyy myös kestämään häiriöitä, jotka ovat konenäköjärjestelmille yleisiä. Työkalun kulumista mallinnusta tutkittiin useilla eri menetelmillä. Näihin kuuluivat muiden muassa neuroverkot ja tukivektoriregressio. Kokeet osoittivat, että tutkitut mallit pystyivät ennustamaan työkalun kulumisasteen käytetyn ajan perusteella. Parhaan tuloksen antoivat neuroverkot Bayesiläisellä regularisoinnilla.

Combining niche modelling and landscape genetics to study local adaptation: A novel approach illustrated using alpine plants

Understanding the factors that shape adaptive genetic variation across species niches has become of paramount importance in evolutionary ecology, especially to understand how adaptation to changing climate affects the geographic range of species. The distribution of adaptive alleles in the ecological niche is determined by the emergence of novel mutations, their fitness consequences and gene flow that connects populations across species niches. Striking demographical differences and source sink dynamics of populations between the centre and the margin of the niche can play a major role in the emergence and spread of adaptive alleles. Although some theoretical predictions have long been proposed, the origin and distribution of adaptive alleles within species niches remain untested. In this paper, we propose and discuss a novel empirical approach that combines landscape genetics with species niche modelling, to test whether alleles that confer local adaptation are more likely to occur in either marginal or central populations of species niches. We illustrate this new approach by using a published data set of 21 alpine plant species genotyped with a total of 2483 amplified fragment length polymorphisms (AFLP), distributed over more than 1733 sampling sites across the Alps. Based on the assumption that alleles that were statistically associated with environmental variables were adaptive, we found that adaptive alleles in the margin of a species niche were also present in the niche centre, which suggests that adaptation originates in the niche centre. These findings corroborate models of species range evolution, in which the centre of the niche contributes to the emergence of novel adaptive alleles, which diffuse towards niche margins and facilitate niche and range expansion through subsequent local adaptation. Although these results need to be confirmed via fitness measurements in natural populations and functionally characterised genetic sequences, this study provides a first step towards understanding how adaptive genetic variation emerges and shapes species niches and geographic ranges along environmental gradients.

Animal modelling for inherited central vision loss.

Disease-causing variants of a large number of genes trigger inherited retinal degeneration leading to photoreceptor loss. Because cones are essential for daylight and central vision such as reading, mobility, and face recognition, this review focuses on a variety of animal models for cone diseases. The pertinence of using these models to reveal genotype/phenotype correlations and to evaluate new therapeutic strategies is discussed. Interestingly, several large animal models recapitulate human diseases and can serve as a strong base from which to study the biology of disease and to assess the scale-up of new therapies. Examples of innovative approaches will be presented such as lentiviral-based transgenesis in pigs and adeno-associated virus (AAV)-gene transfer into the monkey eye to investigate the neural circuitry plasticity of the visual system. The models reported herein permit the exploration of common mechanisms that exist between different species and the identification and highlighting of pathways that may be specific to primates, including humans.

Modelling body mass index and endometrial cancer risk in a pooled-analysis of three case-control studies.

OBJECTIVE: To quantify the relation between body mass index (BMI) and endometrial cancer risk, and to describe the shape of such a relation. DESIGN: Pooled analysis of three hospital-based case-control studies. SETTING: Italy and Switzerland. POPULATION: A total of 1449 women with endometrial cancer and 3811 controls. METHODS: Multivariate odds ratios (OR) and 95% confidence intervals (95% CI) were obtained from logistic regression models. The shape of the relation was determined using a class of flexible regression models. MAIN OUTCOME MEASURE: The relation of BMI with endometrial cancer. RESULTS: Compared with women with BMI 18.5 to <25 kg/m(2) , the odds ratio was 5.73 (95% CI 4.28-7.68) for women with a BMI ≥35 kg/m(2) . The odds ratios were 1.10 (95% CI 1.09-1.12) and 1.63 (95% CI 1.52-1.75) respectively for an increment of BMI of 1 and 5 units. The relation was stronger in never-users of oral contraceptives (OR 3.35, 95% CI 2.78-4.03, for BMI ≥30 versus <25 kg/m(2) ) than in users (OR 1.22, 95% CI 0.56-2.67), and in women with diabetes (OR 8.10, 95% CI 4.10-16.01, for BMI ≥30 versus <25 kg/m(2) ) than in those without diabetes (OR 2.95, 95% CI 2.44-3.56). The relation was best fitted by a cubic model, although after the exclusion of the 5% upper and lower tails, it was best fitted by a linear model. CONCLUSIONS: The results of this study confirm a role of elevated BMI in the aetiology of endometrial cancer and suggest that the risk in obese women increases in a cubic nonlinear fashion. The relation was stronger in never-users of oral contraceptives and in women with diabetes. TWEETABLE ABSTRACT: Risk of endometrial cancer increases with elevated body weight in a cubic nonlinear fashion.

Landslide detection and monitoring capability of boat-based mobile laser scanning along Dieppe coastal cliffs, Normandy

Integrated in a wide research assessing destabilizing and triggering factors to model cliff dynamic along the Dieppe's shoreline in High Normandy, this study aims at testing boat-based mobile LiDAR capabilities by scanning 3D point clouds of the unstable coastal cliffs. Two acquisition campaigns were performed in September 2012 and September 2013, scanning (1) a 30-km-long shoreline and (2) the same test cliffs in different environmental conditions and device settings. The potentials of collected data for 3D modelling, change detection and landslide monitoring were afterward assessed. By scanning during favourable meteorological and marine conditions and close to the coast, mobile LiDAR devices are able to quickly scan a long shoreline with median point spacing up to 10cm. The acquired data are then sufficiently detailed to map geomorphological features smaller than 0.5m2. Furthermore, our capability to detect rockfalls and erosion deposits (>m3) is confirmed, since using the classical approach of computing differences between sequential acquisitions reveals many cliff collapses between Pourville and Quiberville and only sparse changes between Dieppe and Belleville-sur-Mer. These different change rates result from different rockfall susceptibilities. Finally, we also confirmed the capability of the boat-based mobile LiDAR technique to monitor single large changes, characterizing the Dieppe landslide geometry with two main active scarps, retrogression up to 40m and about 100,000m3 of eroded materials.

Reaction Kinetics and Viscosity Modelling in the Fusion Syntheses of Ca- and Ca/Mg-resinates

Rosin is a natural product from pine forests and it is used as a raw material in resinate syntheses. Resinates are polyvalent metal salts of rosin acids and especially Ca- and Ca/Mg- resinates find wide application in the printing ink industry. In this thesis, analytical methods were applied to increase general knowledge of resinate chemistry and the reaction kinetics was studied in order to model the non linear solution viscosity increase during resinate syntheses by the fusion method. Solution viscosity in toluene is an important quality factor for resinates to be used in printing inks. The concept of critical resinate concentration, c crit, was introduced to define an abrupt change in viscosity dependence on resinate concentration in the solution. The concept was then used to explain the non-inear solution viscosity increase during resinate syntheses. A semi empirical model with two estimated parameters was derived for the viscosity increase on the basis of apparent reaction kinetics. The model was used to control the viscosity and to predict the total reaction time of the resinate process. The kinetic data from the complex reaction media was obtained by acid value titration and by FTIR spectroscopic analyses using a conventional calibration method to measure the resinate concentration and the concentration of free rosin acids. A multivariate calibration method was successfully applied to make partial least square (PLS) models for monitoring acid value and solution viscosity in both mid-infrared (MIR) and near infrared (NIR) regions during the syntheses. The calibration models can be used for on line resinate process monitoring. In kinetic studies, two main reaction steps were observed during the syntheses. First a fast irreversible resination reaction occurs at 235 °C and then a slow thermal decarboxylation of rosin acids starts to take place at 265 °C. Rosin oil is formed during the decarboxylation reaction step causing significant mass loss as the rosin oil evaporates from the system while the viscosity increases to the target level. The mass balance of the syntheses was determined based on the resinate concentration increase during the decarboxylation reaction step. A mechanistic study of the decarboxylation reaction was based on the observation that resinate molecules are partly solvated by rosin acids during the syntheses. Different decarboxylation mechanisms were proposed for the free and solvating rosin acids. The deduced kinetic model supported the analytical data of the syntheses in a wide resinate concentration region, over a wide range of viscosity values and at different reaction temperatures. In addition, the application of the kinetic model to the modified resinate syntheses gave a good fit. A novel synthesis method with the addition of decarboxylated rosin (i.e. rosin oil) to the reaction mixture was introduced. The conversion of rosin acid to resinate was increased to the level necessary to obtain the target viscosity for the product at 235 °C. Due to a lower reaction temperature than in traditional fusion synthesis at 265 °C, thermal decarboxylation is avoided. As a consequence, the mass yield of the resinate syntheses can be increased from ca. 70% to almost 100% by recycling the added rosin oil.

Estimating the effects of fiscal policy under the budget constraint

I reconsider the short-term effects of fiscal policy when both government spending and taxes are allowed to respond to the level of public debt. I embed the long-term government budget constraint in a VAR, and apply this common trends model to US quarterly data. The results overturn some widely held beliefs on fiscal policy effects. The main finding is that expansionary fiscal policy has contractionary effects on output and inflation. Ricardian effects may dominate when fiscal expansions are expected to be adjusted by future tax rises or spending cuts. The evidence supports RBC models with distortionary taxation. We can discard some alternative interpretations that are based on monetary policy reactions or supply-side effects.

Ultrafine grinding of FGD and phosphogypsum with an attrition bead mill and a jet mill : optimisation and modelling of grinding and mill comparison

Fine powders of minerals are used commonly in the paper and paint industry, and for ceramics. Research for utilizing of different waste materials in these applications is environmentally important. In this work, the ultrafine grinding of two waste gypsum materials, namely FGD (Flue Gas Desulphurisation) gypsum and phosphogypsum from a phosphoric acid plant, with the attrition bead mill and with the jet mill has been studied. The ' objective of this research was to test the suitability of the attrition bead mill and of the jet mill to produce gypsum powders with a particle size of a few microns. The grinding conditions were optimised by studying the influences of different operational grinding parameters on the grinding rate and on the energy consumption of the process in order to achieve a product fineness such as that required in the paper industry with as low energy consumption as possible. Based on experimental results, the most influential parameters in the attrition grinding were found to be the bead size, the stirrer type, and the stirring speed. The best conditions, based on the product fineness and specific energy consumption of grinding, for the attrition grinding process is to grind the material with small grinding beads and a high rotational speed of the stirrer. Also, by using some suitable grinding additive, a finer product is achieved with a lower energy consumption. In jet mill grinding the most influential parameters were the feed rate, the volumetric flow rate of the grinding air, and the height of the internal classification tube. The optimised condition for the jet is to grind with a small feed rate and with a large rate of volumetric flow rate of grinding air when the inside tube is low. The finer product with a larger rate of production was achieved with the attrition bead mill than with the jet mill, thus the attrition grinding is better for the ultrafine grinding of gypsum than the jet grinding. Finally the suitability of the population balance model for simulation of grinding processes has been studied with different S , B , and C functions. A new S function for the modelling of an attrition mill and a new C function for the modelling of a jet mill were developed. The suitability of the selected models with the developed grinding functions was tested by curve fitting the particle size distributions of the grinding products and then comparing the fitted size distributions to the measured particle sizes. According to the simulation results, the models are suitable for the estimation and simulation of the studied grinding processes.

Rule-based machine translation between Bulgarian and Macedonian

This paper describes the development of a two-way shallow-transfer rule-based machine translation system between Bulgarian and Macedonian. It gives an account of the resources and the methods used for constructing the system, including the development of monolingual and bilingual dictionaries, syntactic transfer rules and constraint grammars. An evaluation of thesystem's performance was carried out and compared to another commercially available MT system for the two languages. Some future work was suggested.

Salient Pole Synchronous Machine Modelling in an Industrial Direct Torque Controlled Drive Application

Synchronous motors are used mainly in large drives, for example in ship propulsion systems and in steel factories' rolling mills because of their high efficiency, high overload capacity and good performance in the field weakening range. This, however, requires an extremely good torque control system. A fast torque response and a torque accuracy are basic requirements for such a drive. For large power, high dynamic performance drives the commonly known principle of field oriented vector control has been used solely hitherto, but nowadays it is not the only way to implement such a drive. A new control method Direct Torque Control (DTC) has also emerged. The performance of such a high quality torque control as DTC in dynamically demanding industrial applications is mainly based on the accurate estimate of the various flux linkages' space vectors. Nowadays industrial motor control systems are real time applications with restricted calculation capacity. At the same time the control system requires a simple, fast calculable and reasonably accurate motor model. In this work a method to handle these problems in a Direct Torque Controlled (DTC) salient pole synchronous motor drive is proposed. A motor model which combines the induction law based "voltage model" and motor inductance parameters based "current model" is presented. The voltage model operates as a main model and is calculated at a very fast sampling rate (for example 40 kHz). The stator flux linkage calculated via integration from the stator voltages is corrected using the stator flux linkage computed from the current model. The current model acts as a supervisor that prevents only the motor stator flux linkage from drifting erroneous during longer time intervals. At very low speeds the role of the current model is emphasised but, nevertheless, the voltage model always stays the main model. At higher speeds the function of the current model correction is to act as a stabiliser of the control system. The current model contains a set of inductance parameters which must be known. The validation of the current model in steady state is not self evident. It depends on the accuracy of the saturated value of the inductances. Parameter measurement of the motor model where the supply inverter is used as a measurement signal generator is presented. This so called identification run can be performed prior to delivery or during drive commissioning. A derivation method for the inductance models used for the representation of the saturation effects is proposed. The performance of the electrically excited synchronous motor supplied with the DTC inverter is proven with experimental results. It is shown that it is possible to obtain a good static accuracy of the DTC's torque controller for an electrically excited synchronous motor. The dynamic response is fast and a new operation point is achieved without oscillation. The operation is stable throughout the speed range. The modelling of the magnetising inductance saturation is essential and cross saturation has to be considered as well. The effect of cross saturation is very significant. A DTC inverter can be used as a measuring equipment and the parameters needed for the motor model can be defined by the inverter itself. The main advantage is that the parameters defined are measured in similar magnetic operation conditions and no disagreement between the parameters will exist. The inductance models generated are adequate to meet the requirements of dynamically demanding drives.