Much older soils data and soils information lies idle in libraries and archives and is largely unused, especially in developing countries like Kenya. We demonstrated the usefulness of a stepwise approach to bring legacy soils data ‘back to life’ using the 1980 <i>Reconnaissance Soil Map of the Busia Area</i> <i>(quarter degree sheet No. 101)</i> in western Kenya as an example. Three studies were conducted by using agronomic information, field observations, and laboratory data available in the published soil survey report as inputs to several digital soil mapping techniques. In the first study, the agronomic information in the survey report was interpreted to generate 10 land quality maps. The maps represented the ability of the land to perform specific agronomic functions. Nineteen crop suitability maps that were not previously available were also generated. In the second study, a dataset of 76 profile points mined from the survey report was used as input to three spatial prediction models for soil organic carbon (SOC) and texture. The three predictions models were (i) ordinary kriging, (ii) stepwise multiple linear regression, and (iii) the Soil Land Inference Model (SoLIM). Statistically, ordinary kriging performed better than SoLIM and stepwise multiple linear regression in predicting SOC (RMSE = 0.02), clay (RMSE = 0.32), and silt (RMSE = 0.10), whereas stepwise multiple linear regression performed better than SoLIM and ordinary kriging for predicting sand content (RSME = 0.11). Ordinary kriging had the narrowest 95% confidence interval while stepwise multiple linear regression had, the widest. From a pedological standpoint, SoLIM conformed better to the soil forming factors model than ordinary kriging and had a narrower confidence interval compared to stepwise multiple linear regression. In the third study, rules generated from the map legend and map unit descriptions were used to generate a soil class map. Information about soil distribution and parent material from the map unit polygon descriptions were combined with six terrain attributes, to generate a disaggregated fuzzy soil class map. The terrain attributes were multiresolution ridgetop flatness (MRRTF), multiresolution valley bottom flatness (MRVBF), topographic wetness index (TWI), topographic position index (TPI), planform curvature, and profile curvature. The final result was a soil class map with a spatial resolution of 30 m, an overall accuracy of 58% and a Kappa coefficient of 0.54. Motivated by the wealth of soil agronomic information generated by this study, we successfully tested the feasibility of delivering this information in rural western Kenya using the cell phone-based Soil Explorer app (<a href="https://soilexplorer.net/">https://soilexplorer.net/</a>). This study demonstrates that legacy soil data can play a critical role in providing sustainable solutions to some of the most pressing agronomic challenges currently facing Kenya and most African countries.<div><p></p></div>

No description available.

This study investigates one of the Polish language’s most arbitrary cases: the genitive masculine inanimate singular. It collects and ranks several guidelines to help language learners discern its proper usage and also introduces a framework to provide detailed feedback regarding arbitrary cases. The study tests this framework by implementing and evaluating a hybrid grammar checker called PLPrepare. PLPrepare performs similarly to other grammar checkers and is able to detect genitive case usages and provide feedback based on a number of error classifications.

GPS tracking data are widely used to understand human travel behavior and to evaluate the impact of travel. A major advantage with the usage of GPS tracking devices for collecting data is that it enables the researcher to collect large amounts of highly accurate and detailed human mobility data. However, unlabeled GPS tracking data does not easily lend itself to detecting transportation mode and this has given rise to a range of methods and algorithms for this purpose. The algorithms used vary in design and functionality, from defining specific rules to advanced machine learning algorithms. There is however no previous comprehensive review of these algorithms and this thesis aims to identify their essential features and methods and to develop and demonstrate a method for the detection of transport mode in GPS tracking data. To do this, it is necessary to have a detailed description of the particular journey undertaken by an individual. Therefore, as part of the investigation, a microdata analytic approach is applied to the problem areas, including the stages of data collection, data processing, analyzing the data, and decision making. In order to fill the research gap, Paper I consists of a systematic literature review of the methods and essential features used for detecting the transport mode in unlabeled GPS tracking data. Selected empirical studies were categorized into rule-based methods, statistical methods, and machine learning methods. The evaluation shows that machine learning algorithms are the most common. In the evaluation, I compared the methods previously used, extracted features, types of dataset, and model accuracy of transport mode detection. The results show that there is no standard method used in transport mode detection. In the light of these results, I propose in Paper II a stepwise methodology to detect five transport modes taking advantage of the unlabeled GPS data by first using an unsupervised algorithm to detect the five transport modes. A GIS multi-criteria process was applied to label part of the dataset. The performance of the five supervised algorithms was evaluated by applying them to different portions of the labeled dataset. The results show that stepwise methodology can achieve high accuracy in detecting the transport mode by labeling only 10% of the data from the entire dataset.  For the future, one interesting area to explore would be the application of the stepwise methodology to a balanced and larger dataset. A semi-supervised deep-learning approach is suggested for development in transport mode detection, since this method can detect transport modes with only small amounts of labeled data. Thus, the stepwise methodology can be improved upon for further studies.

Automatiskt rättning av korta texter är ett område som spänner allt från naturlig språkbehandling till maskininlärning. Projektet behandlar maskininlärning för att förutsäga korrektheten av svar i fritext. Naturlig språkbehandling används för att analysera text och utvinna viktiga underliggande relationer i texten. Det finns idag flera approximativa lösningar för automatiskt rättning av korta svar i fritext. Två framstående metoder är maskininlärning och regelbaserad metod. Vi kommer att framföra en alternativ metod som kombinerar maskininlärning med en regelbaserad metod för att approximativt lösa förenämnda problemet. Studien handlar om att implementera en regelbaserad metod, maskininlärning metod och en slutgiltig kombination av båda dessa metoder. Utvärderingen av den kombinerade metoden utförs genom att titta på de relativa ändringarna i prestanda då vi jämför med den regelbaserade och maskininlärning metoden. De erhållna resultaten har visat att det inte finns någon ökning av noggrannheten hos den kombinerade metoden jämfört med endast maskininlärning metoden. Den kombinerade metoden använder emellertid en liten mängd märkta data med en noggrannhet som är nästan lika metoden med maskininlärning, vilket är positivt. Ytterligare undersökning inom detta område behövs, denna uppsats är bara ett litet bidrag till nya metoder i automatisk rättning. / Automatic correction of short text answers is an area that involves everything from natural language processing to machine learning. Our project deals with machine learning for predicting the correctness of candidate answers and natural language processing to analyse text and extract important underlying relationships in the text. Given that today there are several approximative solutions for automatically correcting short answers, ranging from rule-based methods to machine learning methods. We intend to look at how automatic answer scoring can be solved through a clever combination of both machine learning methods and rule-based method for a given dataset. The study is about implementing a rule-based method, a machine learning method and a final combination of both these methods. The evaluation of the combined method is done by measuring its relative performance compared to the rule-based method and machine learning method. The results obtained have shown that there is no increase in the accuracy of the combined method compared to the machine learning method alone. However, the combined method uses a small amount of labeled data with an accuracy almost equal to the machine learning, which is positive. Further investigation in this area is needed, this thesis is only a small contribution, with a new approaches and methods in automatic short answer scoring.

The present work reports a study related to the potential improvement of the energy performances of a heat pump based heating system for a Swedish single-family house. The analysis is focused on the design of new rule-based control strategies which employ perfect predictions of weather forecast and human behaviour information. In particular, the considered signals are the outdoor temperature, the solar radiation, the internal gain due to inhabitants’ activities and the Domestic Hot Water (DHW) consumption. The study is performed by means of the TRNSYS® simulation software in which the model of the heating system is implemented. More specifically, it is composed by a Ground Source Heat Pump (GSHP) unit, a stratified storage tank of three hundred litres and the building element. The performances of the developed control logics are evaluated using a degree-minute on/off controller as reference case. The results show that the improved control logics yield to an increase of the energy efficiency of the system as well as an enhancement of the indoor and DHW temperatures stability. / EffSys Expand P18: Smart Cotnrol Strategies for Heat Pump Systems

The present work deals with a study related to the analysis of weather data for heat pump system control improvement based on both instantaneous and forecasted measurements. In particular, the analysis is firstly focused on the comparison of multiple weather sources for the assessment of weather forecast uncertainties, based on the evaluation of errors in prediction with respect to measured values. Afterwards, the results are compared with the ones related to persistent predictions methods that assumes the state of the atmosphere to be stationary over the considered time interval. The development and testing of a new preliminary “predictive” control logic is also performed, thanks to TRNSYS numerical simulations, considering a typical Swedish single-family house located in Stockholm, with the aim of optimizing the operation of a heat pump heating system based on solar radiation prediction to yield energy and cost savings. With the crucial points of accuracy and precision by which the local weather processes can be predicted, the same TRNSYS model is run accounting for perfect predictions and solar radiation forecasted values. From this perspective, given the fact that forecast of solar radiation are usually absent within most of the weather forecast datasets, a deep analysis is also performed on hourly measurements of solar radiation to define a simple and effective methods to calculate hourly solar radiation predictions. The results show that, when a short-time horizon is considered, persistent predictions allow to provide forecasts with a sufficient accuracy, whereas, when longer horizon time are considered, significantly higher errors are calculated when persistent prediction techniques are adopted. Independently of the uncertainties considered for weather forecasts, the improved control logics demonstrated a potential for energy savings and improvements in indoor temperature stability when compared with a reference case of variable speed compressor with PID controller. / EffSys Expand P18: Smart Cotnrol Strategies for Heat Pump Systems

In category learning, explicit processes function through the prefrontal cortex (PFC) and implicit processes function through the basal ganglia. Research suggested that these two systems compete with each other. The goal of this study was to shed light on this theory. 15 undergraduate subjects took part in an event-related experiment that required them to categorize computer-generated line-stimuli, which varied in length and/or angle depending on condition. Subjects participated in an explicit "rule-based" (RB) condition and an implicit "information-integration" (II) condition while connected to a functional near-infrared spectroscopy (fNIRS) apparatus, which measured the hemodynamic response (HR) in their PFC. Each condition contained 2 blocks. We hypothesized that the competition between explicit and implicit systems (COVIS) would be demonstrated if, by block 2, task-accuracy was approximately equal across conditions with PFC activity being comparatively higher in the II condition. This would indicate that subjects could learn the categorization task in both conditions but were only able to decipher an explicit rule in the RB condition; their PFC would struggle to do so in the II condition, resulting in perpetually high activation. In accordance with predictions, results revealed no difference in accuracy across conditions with significant difference in channel activation. There were channel trends (p < .1) which showed PFC activation decrease in the RB condition and increase in the II condition by block 2. While these results support our predictions, they are largely nonsignificant, which could be attributed to the event-related design. Future research should utilize a larger samples size for improved statistical power.

No description available.

A bigraph-driven vending machine is implemented. The application is realized as a Spring-based webservice. Actions can be initiated by REST endpoints. The system follows a rule-based architecture, where possible operations are grounded on a rule set. Bigraphical Reactive Systems are used for the specification and execution. The actual state of the application is a bigraph stored in a database, which can be viewed and altered directly in the database. A history of states is kept - the application can be transferred to any prior state. The application can be updated or extended by merely changing the bigraphical database model.:First Part: A system of a vending machine is specified and analyzed using BDSL. This concerns the static and dynamic aspects of the system. Second Part: The analysis results are re-used for the implementation using Bigraph Framework. The application is realized as a webservice that is built using the Spring framework. / Ein bigraph-gesteuerter Verkaufsautomat wird implementiert. Die Anwendung ist als Spring-basierter Webservice realisiert. Aktionen können über REST-Endpunkte initiiert werden. Das System folgt einer regelbasierten Architektur, bei der die möglichen Operationen auf einem Regelsatz beruhen. Für die Spezifikation und Ausführung werden Bigraphical Reactive Systems verwendet. Der aktuelle Zustand der Anwendung ist ein in einer Datenbank gespeicherter Bigraph, der direkt in der Datenbank eingesehen und verändert werden kann. Es wird eine Historie der Zustände geführt - die Anwendung kann in einen beliebigen früheren Zustand überführt werden. Die Anwendung kann aktualisiert oder erweitert werden, indem lediglich das bigraphische Datenbankmodell geändert wird.:First Part: A system of a vending machine is specified and analyzed using BDSL. This concerns the static and dynamic aspects of the system. Second Part: The analysis results are re-used for the implementation using Bigraph Framework. The application is realized as a webservice that is built using the Spring framework.