Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data

Accurate forecasting and analysis of emerging pandemics play a crucial role in effective public health management and decision-making. Traditional approaches primarily rely on epidemiological data, overlooking other valuable sources of information that could act as sensors or indicators of pandemic patterns. In this paper, we propose a novel framework called MGL4MEP that integrates temporal graph neural networks and multi-modal data for learning and forecasting. We incorporate big data sources, including social media content, by utilizing specific pre-trained language models and discovering the underlying graph structure among users. This integration provides rich indicators of pandemic dynamics through learning with temporal graph neural networks. Extensive experiments demonstrate the effectiveness of our framework in pandemic forecasting and analysis, outperforming baseline methods across different areas, pandemic situations, and prediction horizons. The fusion of temporal graph learning and multi-modal data enables a comprehensive understanding of the pandemic landscape with less time lag, cheap cost, and more potential information indicators

Guide them through: an automatic crowd control framework using multi-objective genetic programming

We propose an automatic crowd control framework based on multi-objective optimisa- tion of strategy space using genetic programming. In particular, based on the sensed local crowd densities at different segments, our framework is capable of generating control strategies that guide the individuals on when and where to slow down for opti- mal overall crowd flow in realtime, quantitatively measured by multiple objectives such as shorter travel time and less congestion along the path. The resulting Pareto-front al- lows selection of resilient and efficient crowd control strategies in different situations. We first chose a benchmark scenario as used in [1] to test the proposed method. Results show that our method is capable of finding control strategies that are not only quanti- tatively measured better, but also well aligned with domain experts’ recommendations on effective crowd control such as “slower is faster” and “asymmetric control”. We further applied the proposed framework in actual event planning with approximately 400 participants navigating through a multi-story building. In comparison with the baseline crowd models that do no employ control strategies or just use some hard-coded rules, the proposed framework achieves a shorter travel time and a significantly lower (20%) congestion along critical segments of the path

Capturing and characterising pre-failure strain on failing slopes

Effective management of slope hazards requires an understanding of the likely triggers, geometry, failure dynamics, mechanism and timing; of these the last two remain most problematic. Reducing the epistemic uncertainty of these elements is crucial, particularly for landslides that are not easily mitigated. The ‘inverse-velocity method’ utilises the linearity in inverse-strain-rate change through time in brittle materials to forecast the timing of final slope collapse. A significant body of published deformation data is available, yet to date there has been no attempt to collate a catalogue of landslide deformations from a large number of sites to examine emergent behaviour; notably variations in and controls on movement prior to failure. This thesis collates thirty-one examples of tertiary creep and related attributes from a broad literature search of over 6,000 peer-reviewed journals. Results show that tertiary creep operates over durations ranging from ~37 minutes to 3,171 days. Patterns of acceleration corroborated with published parameterisations of brittle failure; namely Voight’s (1989) model. Most examples (86%) were best-fit with hyperbolic curves, described by an α coefficient within the 1.7 and 2.2 range; indicative of deformation driven by crack growth. No significant relationships between slope and creep characteristics were found within the database of examples, however the lack of standard reporting of slope failures, particularly between industry documents and academic papers, limits the analysis. The database validates the ‘inverse-velocity method’ as a robust forecasting technique. Iterative a priori analysis of data has shown that slopes deforming in a brittle manner are more likely to predict slope collapse ‘too soon’ as a false positive prediction. Analysis has also shown that tertiary creep is typically delimited (87% of examples) within the first 25% of the total creep duration. Recommendations towards monitoring specifically highlight the need for instruments to deliver spatial accuracies to ~10mm, surface based capture and continuous measurement. Developing processing procedures for point cloud data derived from a permanent terrestrial laser scanning system is recommended as the best approach to small-scale deformation monitoring

Effect of curing conditions and harvesting stage of maturity on Ethiopian onion bulb drying properties

The study was conducted to investigate the impact of curing conditions and harvesting stageson the drying quality of onion bulbs. The onion bulbs (Bombay Red cultivar) were harvested at three harvesting stages (early, optimum, and late maturity) and cured at three different temperatures (30, 40 and 50 oC) and relative humidity (30, 50 and 70%). The results revealed that curing temperature, RH, and maturity stage had significant effects on all measuredattributesexcept total soluble solids

Architecture and the Built Environment:

This publication provides an overview of TU Delft’s most significant research achievements in the field of architecture and the built environment during the years 2010–2012. It is the first presentation of the joint research portfolio of the Faculty of Architecture and OTB Research Institute since their integration into the Faculty of Architecture and the Built Environment. As such the portfolio holds a strong promise for the future. In a time when the economy seems to be finally picking up and in which such societal issues as energy, climate and ageing are more prominent than ever before, there are plenty of fields for us to explore in the next three years

Efficient uncertainty quantification in aerospace analysis and design

The main purpose of this study is to apply a computationally efficient uncertainty quantification approach, Non-Intrusive Polynomial Chaos (NIPC) based stochastic expansions, to robust aerospace analysis and design under mixed (aleatory and epistemic) uncertainties and demonstrate this technique on model problems and robust aerodynamic optimization. The proposed optimization approach utilizes stochastic response surfaces obtained with NIPC methods to approximate the objective function and the constraints in the optimization formulation. The objective function includes the stochastic measures which are minimized simultaneously to ensure the robustness of the final design to both aleatory and epistemic uncertainties. For model problems with mixed uncertainties, Quadrature-Based and Point-Collocation NIPC methods were used to create the response surfaces used in the optimization process. For the robust airfoil optimization under aleatory (Mach number) and epistemic (turbulence model) uncertainties, a combined Point-Collocation NIPC approach was utilized to create the response surfaces used as the surrogates in the optimization process. Two stochastic optimization formulations were studied: optimization under pure aleatory uncertainty and optimization under mixed uncertainty. As shown in this work for various problems, the NIPC method is computationally more efficient than Monte Carlo methods for moderate number of uncertain variables and can give highly accurate estimation of various metrics used in robust design optimization under mixed uncertainties. This study also introduces a new adaptive sampling approach to refine the Point-Collocation NIPC method for further improvement of the computational efficiency. Two numerical problems demonstrated that the adaptive approach can produce the same accuracy level of the response surface obtained with oversampling ratio of 2 using less function evaluations. --Abstract, page iii

Guide them through: an automatic crowd control framework using multi-objective genetic programming

We propose an automatic crowd control framework based on multi-objective optimisa- tion of strategy space using genetic programming. In particular, based on the sensed local crowd densities at different segments, our framework is capable of generating control strategies that guide the individuals on when and where to slow down for opti- mal overall crowd flow in realtime, quantitatively measured by multiple objectives such as shorter travel time and less congestion along the path. The resulting Pareto-front al- lows selection of resilient and efficient crowd control strategies in different situations. We first chose a benchmark scenario as used in [1] to test the proposed method. Results show that our method is capable of finding control strategies that are not only quanti- tatively measured better, but also well aligned with domain experts’ recommendations on effective crowd control such as “slower is faster” and “asymmetric control”. We further applied the proposed framework in actual event planning with approximately 400 participants navigating through a multi-story building. In comparison with the baseline crowd models that do no employ control strategies or just use some hard-coded rules, the proposed framework achieves a shorter travel time and a significantly lower (20%) congestion along critical segments of the path

Risk Management in Environment, Production and Economy

The term "risk" is very often associated with negative meanings. However, in most cases, many opportunities can present themselves to deal with the events and to develop new solutions which can convert a possible danger to an unforeseen, positive event. This book is a structured collection of papers dealing with the subject and stressing the importance of a relevant issue such as risk management. The aim is to present the problem in various fields of application of risk management theories, highlighting the approaches which can be found in literature