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<p> This study investigates the use of large scale ensemble weather predictions provided by the National Centers for Environmental Prediction (NCEP) medium range forecast (MRF) modeling system, for short-term hydrologic forecasting. The weather predictors are used to downscale daily precipitation and temperature series at two meteorological stations in the Saguenay watershed in northeastern Canada. Three data-driven methods, namely, statistical downscaling model (SDSM), time lagged feedforward neural network (TLFN), and evolutionary polynomial regression (EPR), are used as downscaling models and their downscaling results are compared. The downscaled results of the best models are used as additional inputs in two hydrological models, Hydrologiska Byrans Vattenbalansavdelning (HBV) and Bayesian neural networks (BNN), for up to 14 day ahead reservoir inflow and river flow forecasting. The performance of the two hydrological forecasting models is compared, the ultimate objective being to improve 7 to 14 day ahead forecasts. </p> <p> The downscaling results show that all the three models have good performance in downscaling temperature time series, the correlation between the observed and downscaled data is more than 0.90, however the downscaling results are less accurate for precipitation, the correlation coefficient is no more than 0.62. TLFN and EPR models have quite close performance in most cases, and they both perform better than SDSM. </p> <p> Therefore the TLFN downscaled meteorological data are used as predictors in the HBV and BNN hydrological models for up to 14 day ahead reservoir inflow and river flow forecasting, and the forecasting results are compared with the case where no downscaled data is included. The results show that for both reservoir inflow and river flow, HBV models have better performance when including downscaled meteorological data, while there is no significant improvement for the BNN models. When comparing the performance of HBV and BNN models through scatter plots, it can be found that BNN models perform better in low flow forecasting than HBV models, while less good in peak flow forecasting. </p> / Thesis / Master of Science (MSc)

This thesis aims to explore the nature of predictions through examining the ways in which they are employed to the frameworks of assumptions that generate and in turn provide a context for interpretation. These frameworks, be they scientific or even religious/spiritual in nature utilise predictions (e.g. demonstrable hypotheses or prophecies) as a means of ascertaining knowledge and understanding about the world. There exists a problem, however, if the status of knowledge derived from the less logical or intuitively based predictive processes is viewed within many mainstream scientific frameworks as being either without validity or wholly impossible. The reason as to why predictions are formed is generally due to a lack of information about the state of a system under observation. The use of predictions within our lives then is often so prevalent that we can take for granted the extent to which we base our behaviour upon possibilities and not actualities through the anticipation of what might be. The primary reason for this is due to the passage of time, in that we would not be able to perceive the future (or the past) without the construct of time. This enables us to then establish models or frameworks of events to project into the future. The other inherent phenomenon then associated with predictions is the formation of expectations that are generated from these models, frameworks or even assumptions. These expectations can be formulated and described in a variety of ways, from the very well defined mathematical descriptions that constitute statistical information about the likelihood of a correct prediction, to the comparatively vague impressions of feelings about the future that are characterized as intuitions or gut feelings.

Indiana University-Purdue University Indianapolis (IUPUI) / The G-quadruplexes (G4s) are guanine-rich four-stranded DNA/RNA structures, which have been found throughout the human genome. G4s have been reported to affect chromatin structure and are involved in important biological processes at transcriptional and epigenetic levels. However, the underlying molecular mechanisms and locating of G4 still remain elusive due to the complexity of G4s. Taking advantage of the development of high-throughput sequencing technologies and machine learning approaches, we constructed this comprehensive investigation on G4 structures, including discovery of a novel marker for functional human hematopoietic stem cells and gained interest in G4 structure, exploring association between G4 and genomic factors by incorporating multi-omics data, and development of a deep-learningbased G4 prediction tool with G4 motif. First, we discovered ADGRG1 as a novel marker for functional human hematopoietic stem cells and its regulation through transcription activities. Our interest in G4s was stimulated while the transcription-related investigations. Next, we analyzed the genome-wide distribution properties of G4s and uncovered the associations of G4 with other epigenetic and transcriptional mechanisms to coordinate gene transcription. We explored that different-confidence G4 groups correlated differently with epigenetic regulatory elements and revealed that G4 structures could correlate with gene expression in two opposite ways depending on their locations and forming strands. Some transcription factors were identified to be over-represented with G4 emergence. We found distinct consensus sequences enriched in the G4 feet, with a high GC content in the feet of high-confidence G4s and a high TA content in solely predicted G4 feet. As for the last part, we developed a novel deep-learning-based prediction tool for DNA G4s with G4 motifs. Considering the classical G4 motif, we applied bi-directional LSTM model with attention method, which captures sequential information, and showed good performance in whole-genome level prediction of DNA G4s with the certified G4 pattern. Our comprehensive work investigated G4 with its functions and predictions and provided a better understanding of G4s on multi-omics level and computational information capture riding the wave of deep learning. / 2023-04-03

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It is difficult for a small scaled local farmer to support him- or herself. In this investigation a program was devloped to help the small scaled farmer Janne from Sala to keep an energy efficient greenhouse. The program applied machine learning to make predictions of future temperatures in the greenhouse. When the temperature was predicted to be dangerously low for the plants and crops Janne was warned via a HTML web page. To make an as accurate prediction as possible different machine learning algorithm methods were evaluated. XGBoost was the most efficient and accurate method with an cross validation value at 2.33 and was used to make the predictions. The data to train the method with was old data inside and outside the greenhouse provided from the consultancy Bitroot and SMHI. To make predictions in real time weather forecast was collectd from SMHI via their API. The program can be useful for a farmer and can be further developed in the future.

The LHCb experiment at the Large Hadron Collider (LHC) at CERN is designed to make precise measurements of processes including B and D mesons to test the Standard Model (SM) predictions for CP violation, and to search for new physics. From its inception one of the key aims of the LHCb collaboration has been to precisely measure the CP violating phase ϕs, the weak phase due to the interference between B⁰/s -B¯⁰/s mixing and decay. Having collected 3 fb-1 of data in Run 1, the combined results of LHCb measurements of ϕs from various decay modes are in agreement with SM predictions. The aim now is to improve the precision of the LHCb measurement to be sensitive to any small deviation from the SM prediction of ϕs. One strategy to achieve this, in addition to collecting more data, is to expand the number of modes used to measure ϕs to improve the sensitivity of the combination. This thesis presents the measurement of the CP violating phase ϕs in the yet unstudied B⁰/s→ ψ(2S)ϕ decay mode. In addition to providing a measurement of ϕs the study of this mode presents an opportunity to confirm the lifetime difference of the B⁰/s mass eigenstates ∆Γs, currently only measured in the B⁰/s→ Jψϕ decay mode. The results from 3 fb-1 of LHCb data are; ϕs = 0:23+0:29-0:28 ± 0:02 rad, ∆Γs = 0:066+0:041-0:044 ± 0:007 ps-1. which are in agreement with the SM and the results from the LHCb measurement from B⁰/s→ Jψϕ decays.

This thesis presents the research work carried out in an experimental study on High Speed Milling and a predictive force model. The Oxley??s machining theory [36] that can be considered a purely theoretical approach, which has not yet been applied to the high speed milling process is used to model this process in order to predict the cutting forces. An experimental programme was carried out in order to study and understand the high speed milling process and to collect force data for machining of AISI 1020 plain carbon steel at speeds from 250 to 500m/min, feed rates 0.025 to 0.075mm/tooth and 0.5 and 0.8mm depths of cut, using three different tool configurations with different nose radii. The model developed by Young [5] using the Oxley??s machining theory, for conventional milling, was first applied to the high speed milling operation. The force predictions were satisfactory compared to the measured forces. Using this as the basis, a theoretical model was developed to predict the cutting forces in high speed milling. A smaller chip element was considered in applying the machining theory to satisfy the assumption of two dimensional deformation in the machining theory. Using the flow stress properties for plain carbon steels obtained by Oxley and his co-workers, the cutting force components: tangential, radial and vertical, were predicted with the new developed model for AISI 1020 steel for the same cutting conditions used in the experiment. The model was able to accurately predict the tangential force, while the other two components showed a good agreement with the experimental forces. Then the model was verified using two other materials namely, AISI 1045 plain carbon steel and AISI 4140 alloy steel. The alloy steel was used in both the states, virgin and hardened (heat treated) for the experiment. The comparison of predictions with experimental forces showed good results for these additional two materials. From the results obtained, it is concluded that the developed model can be used to predict the tangential cutting force accurately, while predicting the other force components with a favourable accuracy.