Data-driven meal events detection using blood glucose response patterns

BackgroundIn the Diabetes domain, events such as meals and exercises play an important role in the disease management. For that, many studies focus on automatic meal detection, specially as part of the so-called artificial β-cell systems. Meals are associated to blood glucose (BG) variations, however such variations are not peculiar to meals, it mostly comes as a combination of external factors. Thus, general approaches such as the ones focused on glucose signal rate of change are not enough to detect personalized influence of such factors. By using a data-driven individualized approach for meal detection, our method is able to fit real data, detecting personalized meal responses even when such external factors are implicitly present.MethodsThe method is split into model training and selection. In the training phase, we start observing meal responses for each individual, and identifying personalized patterns. Occurrences of such patterns are searched over the BG signal, evaluating the similarity of each pattern to each possible signal subsequence. The most similar occurrences are then selected as possible meal event candidates. For that, we include steps for excluding less relevant neighbors per pattern, and grouping close occurrences in time globally. Each candidate is represented by a set of time and response signal related qualitative variables. These variables are used as input features for different binary classifiers in order to learn to classify a candidate as MEAL or NON-MEAL. In the model selection phase, we compare all trained classifiers to select the one that performs better with the data of each individual.ResultsThe results show that the method is able to detect daily meals, providing a result with a balanced proportion between detected meals and false alarms. The analysis on multiple patients indicate that the approach achieves good outcomes when there is enough reliable training data, as this is reflected on the testing results.ConclusionsThe approach aims at personalizing the meal detection task by relying solely on data. The premise is that a model trained with data that contains the implicit influence of external factors is able to recognize the nuances of the individual that generated the data. Besides, the approach can also be used to improve data quality by detecting meals, opening opportunities to possible applications such as detecting and reminding users of missing or wrongly informed meal events

Tractor and Semitrailer Routing Problem of Highway Port Networks under Unbalanced Demand

In China, highway port networks are essential in carrying out tractor and semitrailer transportation operations. To analyze the characteristics of tractor and semitrailer routing in highway port networks, this study examined the situation in which the demands at both ends of the operation might be unbalanced and multiple requirements might be raised in the operation of tractor and semitrailer transportation. An optimal tractor and semitrailer routing model for an entire network was established to reduce the total transportation costs and the number of towing vehicles in the network. Moreover, a heuristic algorithm was designed to solve the model. The comparisons of Strategy 1 and Strategy 2 for a two-stage network swap trailer show that the number of pure network swaps trailer tractors decreases by 21.6% and 18.6%, respectively; and that the cost drops by 7.8% and 7.9%, respectively. In other words, swap trailer transport enterprises can abandon the original swap trailer transportation mode for a two-stage network and adopt a routing optimization strategy for an entire network to achieve superior operation performance, reduce costs, and enhance profits. The study provides a reference for optimizing tractor and semitrailer routing in highway port networks with balanced and multiple demands

Datil: Learning Fuzzy Ontology Datatypes

International audienceReal-world applications using fuzzy ontologies are increasing in the last years, but the problem of fuzzy ontology learning has not received a lot of attention. While most of the previous approaches focus on the problem of learning fuzzy subclass axioms, we focus on learning fuzzy datatypes. In particular, we describe the Datil system, an implementation using unsupervised clustering algorithms to automatically obtain fuzzy datatypes from different input formats. We also illustrate the practical usefulness with an application: semantic lifestyle profiling

Application of vehicle-based sensors assessing highway pavement conditions subject to extreme temperature variation

The technique of using vibration sensors to monitor pavement roughness has been expanding in pavement engineering. The primary objective of this study is to implement cost-effective vibration sensors to predict asphalt roughness and identify critical cracking locations. It has been an increasing discussion in industry whether temperature changes due to climate change will have considerable influence on infrastructure resilience and sustainability. The method presented here uses vehicle-based sensors to assess pavement roughness during extreme hot and cold temperatures in Phoenix, AZ. This project consisted of developing vehicle-based accelerometers and taking monthly road surveys for a year. Five sensors were mounted to a vehicle, four on the tires and one inside the car, as well a sixth smartphone sensor inside the car. This project covers collecting data at pavement temperatures from 40ºF - 150ºF. The analysis consists of converting accelerometer data into international roughness index values using Fourier transforms and using statistical analysis to verify a relationship between pavement temperature and accelerometer vibration. The results show that hot asphalt concrete temperatures increase the amount of observable accelerometer vibration from a vehicle. Sensors mounted near the tires showed to be more reliable than sensors inside the vehicle. This project demonstrates that accelerometer sensing technology is a cost-effective way to advance the day-to-day operations in highway pavement maintenance and management

Economic Epistemology

Most economic research aims to reveal truths about economic relationships. This thesis is different because it aims to progress how economists reveal those truths. It consists of three self-contained chapters, each about different elements of the research process. Chapter 1 explores a new econometric approach to data analysis. As the field of economics currently stands, researchers usually inform policymakers with conclusions that come from studying statistical expectations, or arithmetic means, of potential outcomes. Here I introduce other types of means to study, and show that often they will better respect the needs of policymakers. Chapter 2 is about a task that matters for macroeconomic research in particular: measuring price indexes that exclude the effects of quality change. I translate the task into the language of econometrics, showing that it amounts to estimating the fixed effects in a general model of quality adjustment. Earlier translations are less general and suffer from a misspecification relating to product weighting. To exemplify the value of the translation, I use it to challenge influential criticisms of the so-called "time-dummy hedonic" method of quality adjustment. Chapter 3 is about a behavioural element of research. With four collaborators, I investigate the credibility of central bank discussion papers by searching for traces of "researcher bias", which is a tendency to use undisclosed analytical procedures that raise measured levels of statistical significance in artificial ways. To conduct our investigation we compile a new dataset and borrow two popular bias detection methods. The results are mixed and, alone, do not call for changes in research practices. But they do challenge the merits of one of the bias detection methods we borrow

Congress UPV Proceedings of the 21ST International Conference on Science and Technology Indicators

This is the book of proceedings of the 21st Science and Technology Indicators Conference that took place in València (Spain) from 14th to 16th of September 2016. The conference theme for this year, ‘Peripheries, frontiers and beyond’ aimed to study the development and use of Science, Technology and Innovation indicators in spaces that have not been the focus of current indicator development, for example, in the Global South, or the Social Sciences and Humanities. The exploration to the margins and beyond proposed by the theme has brought to the STI Conference an interesting array of new contributors from a variety of fields and geographies. This year’s conference had a record 382 registered participants from 40 different countries, including 23 European, 9 American, 4 Asia-Pacific, 4 Africa and Near East. About 26% of participants came from outside of Europe. There were also many participants (17%) from organisations outside academia including governments (8%), businesses (5%), foundations (2%) and international organisations (2%). This is particularly important in a field that is practice-oriented. The chapters of the proceedings attest to the breadth of issues discussed. Infrastructure, benchmarking and use of innovation indicators, societal impact and mission oriented-research, mobility and careers, social sciences and the humanities, participation and culture, gender, and altmetrics, among others. We hope that the diversity of this Conference has fostered productive dialogues and synergistic ideas and made a contribution, small as it may be, to the development and use of indicators that, being more inclusive, will foster a more inclusive and fair world