Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN

An artificial neural network (ANN) is presented to predict a 28-day compressive strength of a normal and high strength self compacting concrete (SCC) and high performance concrete (HPC) with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity. Further, while predicting the strength of HPC the same data meant for SCC has been used to train in order to economise on computational effort. The compressive strengths of SCC and HPC as well as slump flow of SCC estimated by the proposed neural network are validated by experimental results.

Prediction of Ultimate Capacity of Laterally Loaded Piles in Clay: A Relevance Vector Machine Approach

This study investigates the potential of Relevance Vector Machine (RVM)-based approach to predict the ultimate capacity of laterally loaded pile in clay. RVM is a sparse approximate Bayesian kernel method. It can be seen as a probabilistic version of support vector machine. It provides much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. RVM model outperforms the two other models based on root-mean-square-error (RMSE) and mean-absolute-error (MAE) performance criteria. It also stimates the prediction variance. The results presented in this paper clearly highlight that the RVM is a robust tool for prediction Of ultimate capacity of laterally loaded piles in clay.

Pulse-echo ultrasound transit time spectroscopy: A comparison of experimental measurements and simulation prediction

Considering ultrasound propagation through complex composite media as an array of parallel sonic rays, a comparison of computer simulated prediction with experimental data has previously been reported for transmission mode (where one transducer serves as transmitter, the other as receiver) in a series of ten acrylic step-wedge samples, immersed in water, exhibiting varying degrees of transit time inhomogeneity. In this study, the same samples were used but in pulse-echo mode, where the same ultrasound transducer served as both transmitter and receiver, detecting both ‘primary’ (internal sample interface) and ‘secondary’ (external sample interface) echoes. A transit time spectrum (TTS) was derived, describing the proportion of sonic rays with a particular transit time. A computer simulation was performed to predict the transit time and amplitude of various echoes created, and compared with experimental data. Applying an amplitude-tolerance analysis, 91.7±3.7% of the simulated data was within ±1 standard deviation (STD) of the experimentally measured amplitude-time data. Correlation of predicted and experimental transit time spectra provided coefficients of determination (R2) ranging from 100.0% to 96.8% for the various samples tested. The results acquired from this study provide good evidence for the concept of parallel sonic rays. Further, deconvolution of experimental input and output signals has been shown to provide an effective method to identify echoes otherwise lost due to phase cancellation. Potential applications of pulse-echo ultrasound transit time spectroscopy (PE-UTTS) include improvement of ultrasound image fidelity by improving spatial resolution and reducing phase interference artefacts.

Sediment concentration prediction and statistical evaluation for annual load estimation

We consider the development of statistical models for prediction of constituent concentration of riverine pollutants, which is a key step in load estimation from frequent flow rate data and less frequently collected concentration data. We consider how to capture the impacts of past flow patterns via the average discounted flow (ADF) which discounts the past flux based on the time lapsed - more recent fluxes are given more weight. However, the effectiveness of ADF depends critically on the choice of the discount factor which reflects the unknown environmental cumulating process of the concentration compounds. We propose to choose the discount factor by maximizing the adjusted R-2 values or the Nash-Sutcliffe model efficiency coefficient. The R2 values are also adjusted to take account of the number of parameters in the model fit. The resulting optimal discount factor can be interpreted as a measure of constituent exhaustion rate during flood events. To evaluate the performance of the proposed regression estimators, we examine two different sampling scenarios by resampling fortnightly and opportunistically from two real daily datasets, which come from two United States Geological Survey (USGS) gaging stations located in Des Plaines River and Illinois River basin. The generalized rating-curve approach produces biased estimates of the total sediment loads by -30% to 83%, whereas the new approaches produce relatively much lower biases, ranging from -24% to 35%. This substantial improvement in the estimates of the total load is due to the fact that predictability of concentration is greatly improved by the additional predictors.

Univariate, bivariate, and multivariate methods in corpus-based lexicography : A study of synonymy

In this dissertation, I present an overall methodological framework for studying linguistic alternations, focusing specifically on lexical variation in denoting a single meaning, that is, synonymy. As the practical example, I employ the synonymous set of the four most common Finnish verbs denoting THINK, namely ajatella, miettiä, pohtia and harkita ‘think, reflect, ponder, consider’. As a continuation to previous work, I describe in considerable detail the extension of statistical methods from dichotomous linguistic settings (e.g., Gries 2003; Bresnan et al. 2007) to polytomous ones, that is, concerning more than two possible alternative outcomes. The applied statistical methods are arranged into a succession of stages with increasing complexity, proceeding from univariate via bivariate to multivariate techniques in the end. As the central multivariate method, I argue for the use of polytomous logistic regression and demonstrate its practical implementation to the studied phenomenon, thus extending the work by Bresnan et al. (2007), who applied simple (binary) logistic regression to a dichotomous structural alternation in English. The results of the various statistical analyses confirm that a wide range of contextual features across different categories are indeed associated with the use and selection of the selected think lexemes; however, a substantial part of these features are not exemplified in current Finnish lexicographical descriptions. The multivariate analysis results indicate that the semantic classifications of syntactic argument types are on the average the most distinctive feature category, followed by overall semantic characterizations of the verb chains, and then syntactic argument types alone, with morphological features pertaining to the verb chain and extra-linguistic features relegated to the last position. In terms of overall performance of the multivariate analysis and modeling, the prediction accuracy seems to reach a ceiling at a Recall rate of roughly two-thirds of the sentences in the research corpus. The analysis of these results suggests a limit to what can be explained and determined within the immediate sentential context and applying the conventional descriptive and analytical apparatus based on currently available linguistic theories and models. The results also support Bresnan’s (2007) and others’ (e.g., Bod et al. 2003) probabilistic view of the relationship between linguistic usage and the underlying linguistic system, in which only a minority of linguistic choices are categorical, given the known context – represented as a feature cluster – that can be analytically grasped and identified. Instead, most contexts exhibit degrees of variation as to their outcomes, resulting in proportionate choices over longer stretches of usage in texts or speech.

Influence of environmental and instrumental variables on the non-invasive prediction of Brix in pineapple using near infrared spectroscopy

The Brix content of pineapple fruit can be non-invasively predicted from the second derivative of near infrared reflectance spectra. Correlations obtained using a NIRSystems 6500 spectrophotometer through multiple linear regression and modified partial least squares analyses using a post-dispersive configuration were comparable with that from a pre-dispersive configuration in terms of accuracy (e.g. coefficient of determination, R2, 0.73; standard error of cross validation, SECV, 1.01°Brix). The effective depth of sample assessed was slightly greater using the post-dispersive technique (about 20 mm for pineapple fruit), as expected in relation to the higher incident light intensity, relative to the pre-dispersive configuration. The effect of such environmental variables as temperature, humidity and external light, and instrumental variables such as the number of scans averaged to form a spectrum, were considered with respect to the accuracy and precision of the measurement of absorbance at 876 nm, as a key term in the calibration for Brix, and predicted Brix. The application of post-dispersive near infrared technology to in-line assessment of intact fruit in a packing shed environment is discussed.

World Wide Web korpuksena kielitieteellisen tutkimusaineiston haku verkkosivuilta

Tämä tutkielma käsittelee World Wide Webin sisältämien verkkosivujen sisältöjen käyttöä korpusmaisesti kielitieteellisenä tutkimusaineistona. World Wide Web sisältää moninkertaisesti enemmän tekstiä kuin suurimmat olemassa olevat perinteiset tekstikorpukset, joten verkkosivuilta voi todennäköisesti löytää paljon esiintymiä sellaisista sanoista ja rakenteista, jotka ovat perinteisissä korpuksissa harvinaisia. Verkkosivuja voidaan käyttää aineistona kahdella eri tavalla: voidaan kerätä satunnainen otos verkkosivuista ja luoda itsenäinen korpus niiden sisällöistä, tai käyttää koko World Wide Webiä korpuksena verkkohakukoneiden kautta. Verkkosivuja on käytetty tutkimusaineistona monilla eri kielitieteen aloilla, kuten leksikograafisessa tutkimuksessa, syntaktisten rakenteiden tutkimuksessa, pedagogisena materiaalina ja vähemmistökielten tutkimuksessa. Verkkosivuilla on perinteisiin korpuksiin verrattuna useita haitallisia ominaisuuksia, jotka pitää ottaa huomioon, kun niitä käytetään aineistona. Kaikki sivut eivät sisällä kelvollista tekstiä, ja sivut ovat usein esimerkiksi HTML-muotoisia, jolloin ne pitää muuttaa helpommin käsiteltävissä olevaan muotoon. Verkkosivut sisältävät enemmän kielellisiä virheitä kuin perinteiset korpukset, ja niiden tekstityypit ja aihepiirit ovat runsaslukuisempia kuin perinteisten korpusten. Aineiston keräämiseen verkkosivuilta tarvitaan tehokkaita ohjelmatyökaluja. Näistä yleisimpiä ovat kaupalliset verkkohakukoneet, joiden kautta on mahdollista päästä nopeasti käsiksi suureen määrään erilaisia sivuja. Näiden lisäksi voidaan käyttää erityisesti kielitieteellisiin tarpeisiin kehitettyjä työkaluja. Tässä tutkielmassa esitellään ohjelmatyökalut WebCorp, WebAsCorpus.org, BootCaT ja Web as Corpus Toolkit, joiden avulla voi hakea aineistoa verkkosivuilta nimenomaan kielitieteellisiin tarkoituksiin.

Image reconstruction with laterally truncated projections in helical cone-beam CT: Linear prediction based projection completion techniques

Lateral or transaxial truncation of cone-beam data can occur either due to the field of view limitation of the scanning apparatus or iregion-of-interest tomography. In this paper, we Suggest two new methods to handle lateral truncation in helical scan CT. It is seen that reconstruction with laterally truncated projection data, assuming it to be complete, gives severe artifacts which even penetrates into the field of view. A row-by-row data completion approach using linear prediction is introduced for helical scan truncated data. An extension of this technique known as windowed linear prediction approach is introduced. Efficacy of the two techniques are shown using simulation with standard phantoms. A quantitative image quality measure of the resulting reconstructed images are used to evaluate the performance of the proposed methods against an extension of a standard existing technique.

Phenology of Carpophilus spp. (Coleoptera: Nitidulidae)in stone fruit orchards as determined by pheromone trapping: Implications for prediction of crop damage

Traps baited with synthetic aggregation pheromone and fermenting bread dough were used to monitor seasonal incidence and abundance of the ripening fruit pests, Carpophilus hemipterus (L.), C. mutilatus Erichson and C. davidsoni Dobson in stone fruit orchards in the Leeton district of southern New South Wales during five seasons (1991-96). Adult beetles were trapped from September-May, but abundance varied considerably between years with the amount of rainfall in December-January having a major influence on population size and damage potential during the canning peach harvest (late February-March). Below average rainfall in December-January was associated with mean trap catches of < 10 beetles/trap/week in low dose pheromone traps during the harvest period in 1991/92 and 1993/94 and no reported damage to ripening fruit. Rainfall in December-January 1992/93 was more than double the average and mean trap catches ranged from 8-27 beetles/week during the harvest period with substantial damage to the peach crop. December-January rainfall was also above average in 1994/95 and 1995/96 and means of 50-300 beetles/trap/week were recorded in high dose pheromone traps during harvest periods. Carpophilus spp. caused economic damage to peach crops in both seasons. These data indicate that it may be possible to predict the likelihood of Carpophilus beetle damage to ripening stone fruit in inland areas of southern Australia, by routine pheromone-based monitoring of beetle populations and summer temperatures and rainfall.

An experimental analysis of information acquisition in prediction markets

We study which factors in terms of trading environment and trader characteristics determine individual information acquisition in experimental asset markets. Traders with larger endowments, existing inconclusive information, lower risk aversion, and less experience in financial markets tend to acquire more information. Overall, we find that traders overacquire information, so that informed traders on average obtain negative profits net of information costs. Information acquisition and the associated losses do not diminish over time. This overacquisition phenomenon is inconsistent with predictions of rational expectations equilibrium, and we argue it resembles the overdissipation results from the contest literature. We find that more acquired information in the market leads to smaller differences between fundamental asset values and prices. Thus, the overacquisition phenomenon is a novel explanation for the high forecasting accuracy of prediction markets.

NIR model development and robustness in prediction of melon fruit total soluble solids

Near infrared spectroscopy (NIRS) can be used for the on-line, non-invasive assessment of fruit for eating quality attributes such as total soluble solids (TSS). The robustness of multivariate calibration models, based on NIRS in a partial transmittance optical geometry, for the assessment of TSS of intact rockmelons (Cucumis melo) was assessed. The mesocarp TSS was highest around the fruit equator and increased towards the seed cavity. Inner mesocarp TSS levels decreased towards both the proximal and distal ends of the fruit, but more so towards the proximal end. The equatorial region of the fruit was chosen as representative of the fruit for near infrared assessment of TSS. The spectral window for model development was optimised at 695-1045 nm, and the data pre-treatment procedure was optimised to second-derivative absorbance without scatter correction. The 'global' modified partial least squares (MPLS) regression modelling procedure of WINISI (ver. 1.04) was found to be superior with respect to root mean squared error of prediction (RMSEP) and bias for model predictions of TSS across seasons, compared with the 'local' MPLS regression procedure. Updating of the model with samples selected randomly from the independent validation population demonstrated improvement in both RMSEP and bias with addition of approximately 15 samples.

Combining propensity and influence models for product adoption prediction

This paper studies the problem of selecting users in an online social network for targeted advertising so as to maximize the adoption of a given product. In previous work, two families of models have been considered to address this problem: direct targeting and network-based targeting. The former approach targets users with the highest propensity to adopt the product, while the latter approach targets users with the highest influence potential – that is users whose adoption is most likely to be followed by subsequent adoptions by peers. This paper proposes a hybrid approach that combines a notion of propensity and a notion of influence into a single utility function. We show that targeting a fixed number of high-utility users results in more adoptions than targeting either highly influential users or users with high propensity.

Predicting the response of wheat (Triticum aestivum L.) to liquid and granular phosphorus fertilisers in Australian soils

Liquid forms of phosphorus (P) have been shown to be more effective than granular P for promoting cereal growth in alkaline soils with high levels of free calcium carbonate on Eyre Peninsula, South Australia. However, the advantage of liquid over granular P forms of fertiliser has not been fully investigated across the wide range of soils used for grain production in Australia. A glasshouse pot experiment tested if liquid P fertilisers were more effective for growing spring wheat (Triticum aestivum L.) than granular P (monoammonium phosphate) in 28 soils from all over Australia with soil pH (H2O) ranging from 5.2 to 8.9. Application of liquid P resulted in greater shoot biomass, as measured after 4 weeks' growth (mid to late tillering, Feeks growth stage 2-3), than granular P in 3 of the acidic to neutral soils and in 3 alkaline soils. Shoot dry matter responses of spring wheat to applied liquid or granular P were related to soil properties to determine if any of the properties predicted superior yield responses to liquid P. The calcium carbonate content of soil was the only soil property that significantly contributed to predicting when liquid P was more effective than granular P. Five soil P test procedures (Bray, Colwell, resin, isotopically exchangeable P, and diffusive gradients in thin films (DGT)) were assessed to determine their ability to measure soil test P on subsamples of soil collected before the experiment started. These soil test values were then related to the dry matter shoot yields to assess their ability to predict wheat yield responses to P applied as liquid or granular P. All 5 soil test procedures provided a reasonable prediction of dry matter responses to applied P as either liquid or granular P, with the resin P test having a slightly greater predictive capacity on the range of soils tested. The findings of this investigation suggest that liquid P fertilisers do have some potential applications in non-calcareous soils and confirm current recommendations for use of liquid P fertiliser to grow cereal crops in highly calcareous soils. Soil P testing procedures require local calibration for response to the P source that is going to be used to amend P deficiency.

Weed management in wide-row cropping systems: a review of current practices and risks for Australian farming systems.

Growing agricultural crops in wide row spacings has been widely adopted to conserve water, to control pests and diseases, and to minimise problems associated with sowing into stubble. The development of herbicide resistance combined with the advent of precision agriculture has resulted in a further reason for wide row spacings to be adopted: weed control. Increased row spacing enables two different methods of weed control to be implemented with non-selective chemical and physical control methods utilised in the wide inter-row zone, with or without selective chemicals used on the on-row only. However, continual application of herbicides and tillage on the inter-row zone brings risks of herbicide resistance, species shifts and/or changes in species dominance, crop damage, increased costs, yield losses, and more expensive weed management technology.