Towards an Architecture of Self-reliance

This research project focuses on how decisions made by practitioners, articulating rural housing in Sub-Sahara Africa, contribute to the decreasing level of self-reliance inhabitants have regarding their housing. Multiple case studies on Mt. Elgon proved that inhabitants have a significantly higher self-reliance level, comparing traditional to modern housing. To study this phenomenon in practice and to articulate suitable design support the Design Research Methodology was chosen. The research clarification pinpointed inhabitant capacities as the key-contributor to self-reliant housing. Household survey outcomes proved that large numbers of rural inhabitant’s desire housing which they have insufficient capacities for. Indicating that the inhabitants experience a disparity between existing and desired housing capacities, moreover an inability to bridge this disparity independently, and consequently require external help. Architect seemed most appropriate to offer this help as it consist of sociocultural, engineering and design tasks. Architects are not trained in inhabitant capacity evaluation and as no suitable design tools existed, this research project developed the required design support, its application requirements and the impact measurements. These were then tested in a pilot project on Mt. Elgon. The findings were used to evaluate the support’s impact and suitability. The support tool users found it suitable to assess and integrate inhabitant capacities into housing solutions. The impact shows that the support group families have sustained their family’s level of self-reliance unlike the control group. The developed technological design, with modifications, could be used not only in rural Kenyan communities, but also help others around the continent

Generalized Multivariate Extreme Value Models for Explicit Route Choice Sets

This paper analyses a class of route choice models with closed-form probability expressions, namely, Generalized Multivariate Extreme Value (GMEV) models. A large group of these models emerge from different utility formulas that combine systematic utility and random error terms. Twelve models are captured in a single discrete choice framework. The additive utility formula leads to the known logit family, being multinomial, path-size, paired combinatorial and link-nested. For the multiplicative formulation only the multinomial and path-size weibit models have been identified; this study also identifies the paired combinatorial and link-nested variations, and generalizes the path-size variant. Furthermore, a new traveller's decision rule based on the multiplicative utility formula with a reference route is presented. Here the traveller chooses exclusively based on the differences between routes. This leads to four new GMEV models. We assess the models qualitatively based on a generic structure of route utility with random foreseen travel times, for which we empirically identify that the variance of utility should be different from thus far assumed for multinomial probit and logit-kernel models. The expected travellers' behaviour and model-behaviour under simple network changes are analysed. Furthermore, all models are estimated and validated on an illustrative network example with long distance and short distance origin-destination pairs. The new multiplicative models based on differences outperform the additive models in both tests

80 Years of Aerospace Engineering Education in the Netherlands

This year, 2020, the Faculty of Aerospace Engineering at Delft University of Technology in the Netherlands celebrates its 80th birthday. This paper describes the history of the department since its founding in early 1940, just before the start of World War II in the Netherlands, until present day. The paper will highlight how its research and education developed within the socio-economic context of the Netherlands and the developments in aerospace over the past 80 years

Quasi-dynamic network loading: Adding queuing and spillback to static traffic assignment

For many years, static traffic assignment models have been widely applied in transport planning studies and will continue to be an important tool for strategic policy decisions. As is well known, in the traditional approach, the location of the delays and queues are not predicted correctly, and the resulting travel times do not correspond well with reality. Dynamic models can approach reality much better, but come at a computational cost. In this paper we propose a quasi-dynamic model which inherits most of the computational efficiency of static models, but aims to keep most of the important dynamic features, such as queuing, spillback, and shockwaves. Instead of adjusting the traditional static model or using heuristics, we theoretically derive the model from the dynamic link transmission model, assuming stationary travel demand and instantaneous flow. Furthermore, we present algorithms for solving the model. On a corridor network we illustrate the feasibility and compare it with other approaches, and on a larger network of Amsterdam we discuss the computational efficiency

Requirements for traffic assignment models for strategic transport planning: A critical assessment

Transport planning models are used all over the world to assist in the decision making regarding investments in infrastructure and transport services. Traffic assignment is one of the key components of transport models, which relate travel demand to infrastructure supply, by simulating (future) route choices and network conditions, resulting in traffic flows, congestion, travel times, and emissions. Cost benefit analyses rely on outcomes of such models, and since very large monetary investments are at stake, these outcomes should be as accurate and reliable as possible. However, the vast majority of strategic transport models still use traditional static traffic assignment procedures with travel time functions in which traffic flow can exceed capacity, delays are predicted in the wrong locations, and intersections are not properly handled. On the other hand, microscopic dynamic traffic simulation models can simulate traffic very realistically, but are not able to deal with very large networks and may not have the capability of providing robust results for scenario analysis. In this paper we discuss and identify the important characteristics of traffic assignment models for transport planning. We propose a modelling framework in which the traffic assignment model exhibits a good balance between traffic flow realism, robustness, consistency, accountability, and ease of use. Furthermore, case studies on several large networks of Dutch and Australian cities will be presented

New insights in gastroesophageal reflux, esophageal function and gastric emptying in relation to dysphagia before and after anti-reflux surgery in children.

This item is under embargo for a period of 12 months from the date of publication, in accordance with the publisher's policy.In children with gastroesophageal reflux (GER) disease refractory to pharmacological therapies, anti-reflux surgery (fundoplication) may be a treatment of last resort. The applicability of fundoplication has been hampered by the inability to predict which patient may benefit from surgery and which patient is likely to develop post-operative dysphagia. pH impedance measurement and conventional manometry are unable to predict dysphagia, while the role of gastric emptying remains poorly understood. Recent data suggest that the selection of patients who will benefit from surgery might be enhanced by automated impedance manometry pressure-flow analysis (AIM) analysis, which relates bolus movement and pressure generation within the esophageal lumen

Capacity constrained stochastic static traffic assignment with residual point queues incorporating a proper node model

Static traffic assignment models are still widely applied for strategic transport planning purposes in spite of the fact that such models produce implausible traffic flows that exceed link capacities and predict incorrect congestion locations. There have been numerous attempts in the literature to add capacity constraints to obtain more realistic traffic flows and bottleneck locations, but so far there has not been a satisfactory model formulation. After reviewing the literature, we come to the conclusion that an important piece of the puzzle has been missing so far, namely the inclusion of a proper node model. In this paper we propose a novel path-based static traffic assignment model for finding a stochastic user equilibrium in which we include a first order node model that yields realistic turn capacities, which are then used to determine consistent traffic flows and residual point queues. The route choice part of the model is specified as a variational inequality problem, while the network loading part is formulated as a fixed point problem. Both problems are solved using existing techniques. We illustrate the model using hypothetical examples, and also demonstrate feasibility on large-scale networks

Resting-State Electroencephalography Functional Connectivity Networks Relate to Pre- and Postoperative Language Functioning in Low-Grade Glioma and Meningioma Patients

Introduction: Preservation of language functioning in patients undergoing brain tumor surgery is essential because language impairments negatively impact the quality of life. Brain tumor patients have alterations in functional connectivity (FC), the extent to which brain areas functionally interact. We studied FC networks in relation to language functioning in glioma and meningioma patients. Method: Patients with a low-grade glioma (N = 15) or meningioma (N = 10) infiltrating into/pressing on the language-dominant hemisphere underwent extensive language testing before and 1 year after surgery. Resting-state EEG was registered preoperatively, postoperatively (glioma patients only), and once in healthy individuals. After analyzing FC in theta and alpha frequency bands, weighted networks and Minimum Spanning Trees were quantified by various network measures. Results: Pre-operative FC network characteristics did not differ between glioma patients and healthy individuals. However, hub presence and higher local and global FC are associated with poorer language functioning before surgery in glioma patients and predict worse language performance at 1 year after surgery. For meningioma patients, a greater small worldness was related to worse language performance and hub presence; better average clustering and global integration were predictive of worse outcome on language function 1 year after surgery. The average eccentricity, diameter and tree hierarchy seem to be the network metrics with the more pronounced relation to language performance. Discussion: In this exploratory study, we demonstrated that preoperative FC networks are informative for pre- and postoperative language functioning in glioma patients and to a lesser extent in meningioma patients

Distinct Slow-Wave Activity Patterns in Resting-State Electroencephalography and Their Relation to Language Functioning in Low-Grade Glioma and Meningioma Patients

INTRODUCTION: Brain tumours frequently cause language impairments and are also likely to co-occur with localised abnormal slow-wave brain activity. However, it is unclear whether this applies specifically to low-grade brain tumours. We investigate slow-wave activity in resting-state electroencephalography (EEG) in low-grade glioma and meningioma patients, and its relation to pre- and postoperative language functioning. METHOD: Patients with a glioma (N = 15) infiltrating the language-dominant hemisphere and patients with a meningioma (N = 10) with mass effect on this hemisphere underwent extensive language testing before and 1 year after surgery. EEG was registered preoperatively, postoperatively (glioma patients only), and once in healthy individuals. Slow-wave activity in delta- and theta- frequency bands was evaluated visually and quantitatively by spectral power at three levels over the scalp: the whole brain, the affected hemisphere, and the affected region. RESULTS: Glioma patients had increased delta activity (affected area) and increased theta activity (all levels) before and after surgery. In these patients, increased preoperative theta activity was related to the presence of language impairment, especially to poor word retrieval and grammatical performance. Preoperative slow-wave activity was also related to postoperative language outcomes. Meningioma patients showed no significant increase in EEG slow-wave activity compared to healthy individuals, but they presented with word retrieval, grammatical, and writing problems preoperatively, as well as with writing impairments postoperatively. DISCUSSION: Although the brain-tumour pathology in low-grade gliomas and meningiomas has a different effect on resting-state brain activity, patients with low-grade gliomas and meningiomas both suffer from language impairments. Increased theta activity in glioma patients can be considered as a language-impairment marker, with prognostic value for language outcome after surgery