Sustainable Smart Cities and Smart Villages Research

ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [There is ever more research on smart cities and new interdisciplinary approaches proposed on the study of smart cities. At the same time, problems pertinent to communities inhabiting rural areas are being addressed, as part of discussions in contigious fields of research, be it environmental studies, sociology, or agriculture. Even if rural areas and countryside communities have previously been a subject of concern for robust policy frameworks, such as the European Union’s Cohesion Policy and Common Agricultural Policy Arguably, the concept of ‘the village’ has been largely absent in the debate. As a result, when advances in sophisticated information and communication technology (ICT) led to the emergence of a rich body of research on smart cities, the application and usability of ICT in the context of a village has remained underdiscussed in the literature. Against this backdrop, this volume delivers on four objectives. It delineates the conceptual boundaries of the concept of ‘smart village’. It highlights in which ways ‘smart village’ is distinct from ‘smart city’. It examines in which ways smart cities research can enrich smart villages research. It sheds light on the smart village research agenda as it unfolds in European and global contexts.

Measuring Globalization: Better Trade Statistics for Better Policy

Understanding the impacts of globalization requires good data, and national statistical systems were not designed to measure many of the transactions occurring in today’s global economy. The chapters in this volume and its companion volume identify biases and gaps in national statistics, examine the magnitude of the problems they pose, and propose solutions to address significant biases and fill key data gaps.https://research.upjohn.org/up_press/1250/thumbnail.jp

Dynamic operation, efficient calibration, and advanced data analysis of gas sensors : from modelling to real-world operation

This thesis demonstrates the use of dynamic operation, efficient calibration and advanced data analysis using metal oxide semiconductor (MOS) gas sensors as an example – from modeling to real-world operation. The necessary steps for an applicationspecific, selective indoor volatile organic compound (VOC) measurement system are addressed, analyzed and improved. Factors such as sensors, operation, electronics and calibration are considered. The developed methods and tools are universally transferable to other gas sensors and applications. The basis for selective measurement is temperature cyclic operation (TCO). The model-based understanding of a semiconductor gas sensor in TCO for the optimized development of operating modes and data evaluation is addressed and, for example, the tailored and stable detection of short gas pulses is developed. Two successful interlaboratory tests for the measurement of VOCs in independent laboratories are described. Selective measurements of VOCs in the laboratory and in the field are successfully demonstrated. Calibrations using the proposed techniques of randomized design of experiment (DoE), model-based data evaluation and calibration with machine learning methods are employed. The calibrated models are compared with analytical measurements using release tests. The high agreement of the results is unique in current research.Diese Thesis zeigt den Einsatz von dynamischem Betrieb, effizienter Kalibrierung, und fortschrittlicher Datenanalyse am Beispiel von Metalloxid Halbleiter (MOS) Gassensoren – von der Modellierung bis zum realen Betrieb. Die notwendigen Schritte für ein anwendungsspezifisches, selektives Messystem für flüchtige organische Verbindungen (VOC) im Innenraum werden adressiert, analysiert und verbessert. Faktoren wie z.B. Sensoren, Funktionsweise, Elektronik und Kalibrierung werden berücksichtigt. Die entwickelten Methoden und Tools sind universell auf andere Gassensoren und Anwendungen übertragbar. Grundlage für die selektive Messung ist der temperaturzyklische Betrieb (TCO). Auf das modellbasierte Verständnis eines Halbleitergassensors im TCO für die optimierte Entwicklung von Betriebsmodi und Datenauswertung wird eingegangen und z.B. die maßgeschneiderte und stabile Detektion von kurzen Gaspulsen entwickelt. Zwei erfolgreiche Ringversuche zur Messung von VOCs in unabhängigen Laboren werden beschrieben. Selektive Messungen verschiedener VOCs im Labor und im Feld werden erfolgreich demonstriert. Dabei kommen Kalibrierungen mit den vorgeschlagenen Techniken des randomisierten Design of Experiment (DoE), der modellbasierten Datenauswertung und Kalibrierung mit Methoden des maschinellen Lernens zum Einsatz. Die kalibrierten Modelle werden anhand von Freisetzungstests mit analytischen Messungen verglichen. Die hohe Übereinstimmung der Ergebnisse ist einzigartig in der aktuellen Forschung

MODELING OF QUALITY PROFILE DATA WITH APPLICATION IN MANUFACTURING AND BIOMEDICAL ENGINEERING

The quality of the output of a complex system is often recorded as multidimensional profile data with panel structure. In such structure, the quality of each individual in the output is measured repeatedly based on time or other variables. In this dissertation, the quality profile data are modeled to address two types of problems: (a) to explore the underlying relationship between the parameter of interest in the complex system and the resulting quality under the condition that the principal mechanism is not fully known and (b) to quantify the uncertainties among the output. For the first type of problem, we consider a constrained semiparametric varying coefficient model. The system parameter of interest is treated as a covariate whose effect upon the resulting quality is modeled nonparametrically as a function of time. Any existing physicochemical knowledge related to other factors in the system that affect the resulting output quality is modeled parametrically as an additive term in the model. In the situation that expert knowledge about the effect of the parameter is available, some constraints can be incorporated in the model such that the estimated effect aligns with the given knowledge. For the second type of problem, mixed-effect model is developed to quantify the uncertainties among output using random effects. These random effects can be utilized for anomaly detection or for variation quantification where deviation among individuals is of interest depending on the context of the data. Three case studies from manufacturing and biomedical engineering domains are presented in the dissertation where the above two types of problems are discussed