Assessing and identifying high-impact potential climate tech start-ups

With the urgent threat facing humanity due to the climate emergency, decision-makers need tools to assess the future potential climate impact of emerging technologies. To understand the impact of these technologies two types of assessments were studied: GHG Footprint and GHG Impact. It became clear more work needs to be done on the GHG Impact side, so a detailed review of existing forward-looking GHG Impact frameworks is performed. Then a new methodology, combining two of the existing frameworks, to identify high-impact potential start-ups is introduced and implemented. This led to the identification of 39 high-impact potential climate tech start-ups out of a database of 30,000. Then an analysis of these start-ups was undertaken to understand their performanceCom a ameaça latente que a humanidade enfrenta devido à emergência climática, os decisores precisam de ferramentas para avaliar o impacto climático potencial das tecnologias emergentes. Para entender o impacto dessas tecnologias, dois tipos de avaliações foram estudadas: "GHG Footprint" e "GHG Impact". É hoje claro que é necessário fazer um esforço adicional para diminuir o impacto dos gases de efeito de estufa (GEE), pelo que uma revisão detalhada das plataformas sobre o impacto dos GEE é realizada de forma prospectiva. Em seguida, uma nova metodologia, combinando duas das plataformas existentes, para identificar potenciais start-ups de alto impacto climático é introduzida e implementada. Isso levou à identificação de 39 "start-ups" de tecnologia climática de alto impacto a partir de um banco de dados de 30.000. Em seguida, uma análise dessas "start-ups" foi realizada para compreender o respetivo desempenh

Observations on pulsating auroras

Photometric observations of pulsating aurora

Engineering Algorithms for Dynamic and Time-Dependent Route Planning

Efficiently computing shortest paths is an essential building block of many mobility applications, most prominently route planning/navigation devices and applications. In this thesis, we apply the algorithm engineering methodology to design algorithms for route planning in dynamic (for example, considering real-time traffic) and time-dependent (for example, considering traffic predictions) problem settings. We build on and extend the popular Contraction Hierarchies (CH) speedup technique. With a few minutes of preprocessing, CH can optimally answer shortest path queries on continental-sized road networks with tens of millions of vertices and edges in less than a millisecond, i.e. around four orders of magnitude faster than Dijkstra’s algorithm. CH already has been extended to dynamic and time-dependent problem settings. However, these adaptations suffer from limitations. For example, the time-dependent variant of CH exhibits prohibitive memory consumption on large road networks with detailed traffic predictions. This thesis contains the following key contributions: First, we introduce CH-Potentials, an A*-based routing framework. CH-Potentials computes optimal distance estimates for A* using CH with a lower bound weight function derived at preprocessing time. The framework can be applied to any routing problem where appropriate lower bounds can be obtained. The achieved speedups range between one and three orders of magnitude over Dijkstra’s algorithm, depending on how tight the lower bounds are. Second, we propose several improvements to Customizable Contraction Hierarchies (CCH), the CH adaptation for dynamic route planning. Our improvements yield speedups of up to an order of magnitude. Further, we augment CCH to efficiently support essential extensions such as turn costs, alternative route computation and point-of-interest queries. Third, we present the first space-efficient, fast and exact speedup technique for time-dependent routing. Compared to the previous time-dependent variant of CH, our technique requires up to 40 times less memory, needs at most a third of the preprocessing time, and achieves only marginally slower query running times. Fourth, we generalize A* and introduce time-dependent A* potentials. This allows us to design the first approach for routing with combined live and predicted traffic, which achieves interactive running times for exact queries while allowing live traffic updates in a fraction of a minute. Fifth, we study extended problem models for routing with imperfect data and routing for truck drivers and present efficient algorithms for these variants. Sixth and finally, we present various complexity results for non-FIFO time-dependent routing and the extended problem models

NP-hardness of shortest path problems in networks with non-FIFO time-dependent travel times

We study the complexity of earliest arrival problems on time-dependent networks with non-FIFO travel time functions, i.e. when departing later might lead to an earlier arrival. In this paper, we present a simple proof of the weak NP-hardness of the problem for travel time functions defined on integers. This simplifies and reproduces an earlier result from Orda and Rom. Our proof generalizes to travel time functions defined on rational numbers and also implies that, in this case, the problem becomes harder, i.e. is strongly NP-hard. As arbitrary functions are impractical for applications, we also study a more realistic problem model where travel time functions are piecewise linear and represented by a sequence of breakpoints with integer coordinates. We show that this problem formulation is strongly NP-hard, too. As an intermediate step for this proof, we also show the strong NP-completeness of SubsetProduct on rational numbers

Fast Computation of Shortest Smooth Paths and Uniformly Bounded Stretch with Lazy RPHAST

We study the shortest smooth path problem (SSPP), which is motivated by traffic-aware routing in road networks. The goal is to compute the fastest route according to the current traffic situation while avoiding undesired detours, such as briefly using a parking area to bypass a jammed highway. Detours are prevented by limiting the uniformly bounded stretch (UBS) with respect to a second weight function which disregards the traffic situation. The UBS is a path quality metric which measures the maximum relative length of detours on a path. In this paper, we settle the complexity of the SSPP and show that it is strongly NP-complete. We then present practical algorithms to solve the problem on continental-sized road networks both heuristically and exactly. A crucial building block of these algorithms is the UBS evaluation. We propose a novel algorithm to compute the UBS with only a few shortest path computations on typical paths. All our algorithms utilize Lazy RPHAST, a recently proposed technique to incrementally compute distances from many vertices towards a common target. An extensive evaluation shows that our algorithms outperform competing SSPP algorithms by up to two orders of magnitude and that our new UBS algorithm is the first to consistently compute exact UBS values in a matter of milliseconds

The Arctic Commons

The Arctic Commons envisions a world where geopolitical cooperation and transnational friendship generate an ethos of planetary collectivism promoting the future stability in the Arctic and rest of the world. This book will be a guide to understanding the Arctic at a range of scales, from governmental to regional, and finally the experiential and phenomenal that engages the unique ground conditions. The Arctic Commons encourages political action to create a new network of infrastructure that operates as a model for retrofiting global systems which currently fail to represent the common interests of the everyday citizen. Humankind’s current standards for social and environmental politics are underachieving at a moral and ethical level, but also failing at the spatial scale that operates in the realm of landscape architecture. Landscape architects have the power to envision futures at multiple scales and from a range of perspectives. This project seeks to traverse these scales and propose a collective perspective for the Arctic

Feedback Mechanism for Microtubule Length Regulation by Stathmin Gradients

We formulate and analyze a theoretical model for the regulation of microtubule (MT) polymerization dynamics by the signaling proteins Rac1 and stathmin. In cells, the MT growth rate is inhibited by cytosolic stathmin, which, in turn, is inactivated by Rac1. Growing MTs activate Rac1 at the cell edge, which closes a positive feedback loop. We investigate both tubulin sequestering and catastrophe promotion as mechanisms for MT growth inhibition by stathmin. For a homogeneous stathmin concentration in the absence of Rac1, we find a switch-like regulation of the MT mean length by stathmin. For constitutively active Rac1 at the cell edge, stathmin is deactivated locally, which establishes a spatial gradient of active stathmin. In this gradient, we find a stationary bimodal MT length distributions for both mechanisms of MT growth inhibition by stathmin. One subpopulation of the bimodal length distribution can be identified with fast growing and long pioneering MTs in the region near the cell edge, which have been observed experimentally. The feedback loop is closed through Rac1 activation by MTs. For tubulin sequestering by stathmin, this establishes a bistable switch with two stable states: one stable state corresponds to upregulated MT mean length and bimodal MT length distributions, i.e., pioneering MTs; the other stable state corresponds to an interrupted feedback with short MTs. Stochastic effects as well as external perturbations can trigger switching events. For catastrophe promoting stathmin we do not find bistability

Using Incremental Many-to-One Queries to Build a Fast and Tight Heuristic for A* in Road Networks

We study exact, efficient, and practical algorithms for route planning applications in large road networks. On the one hand, such algorithms should be able to answer shortest path queries within milliseconds. On the other hand, routing applications often require integrating the current traffic situation, planning ahead with predictions for future traffic, respecting forbidden turns, and many other features depending on the specific application. Therefore, such algorithms must be flexible and able to support a variety of problem variants. In this work, we revisit the A* algorithm to build a simple, extensible, and unified algorithmic framework applicable to many route planning problems. A* has been previously used for routing in road networks. However, its performance was not competitive because no sufficiently fast and tight distance estimation function was available. We present a novel, efficient, and accurate A* heuristic using Contraction Hierarchies, another popular speedup technique. The core of our heuristic is a new Contraction Hierarchies query algorithm called Lazy RPHAST, which can efficiently compute shortest distances from many incrementally provided sources toward a common target. Additionally, we describe A* optimizations to accelerate the processing of low-degree vertices, which are typical in road networks, and present a new pruning criterion for symmetrical bidirectional A*. An extensive experimental study confirms the practicality of our approach for many applications

Combining Predicted and Live Traffic with Time-Dependent A* Potentials

We study efficient and exact shortest path algorithms for routing on road networks with realistic traffic data. For navigation applications, both current (i.e., live) traffic events and predictions of future traffic flows play an important role in routing. While preprocessing-based speedup techniques have been employed successfully to both settings individually, a combined model poses significant challenges. Supporting predicted traffic typically requires expensive preprocessing while live traffic requires fast updates for regular adjustments. We propose an A*-based solution to this problem. By generalizing A* potentials to time dependency, i.e. the estimate of the distance from a vertex to the target also depends on the time of day when the vertex is visited, we achieve significantly faster query times than previously possible. Our evaluation shows that our approach enables interactive query times on continental-sized road networks while allowing live traffic updates within a fraction of a minute. We achieve a speedup of at least two orders of magnitude over Dijkstra's algorithm and up to one order of magnitude over state-of-the-art time-independent A* potentials.Comment: 19 pages, 5 figures. Full version of ESA22 pape

Therapie der extraintestinalen CED-Manifestationen: Eine schwierige Herausforderung

Zusammenfassung: Morbus Crohn und Colitis ulcerosa sind chronisch-entzündliche Darmerkrankungen (CED), die nicht auf das Gastrointestinalsystem beschränkt sind. Zusätzlich können diverse Organsysteme mit betroffen sein, was die CED zu einer Systemerkrankung macht. Die häufigsten extraintestinalen Manifestationen beinhalten muskuloskelettale, ophthalmologische, dermatologische und hepatobiliäre Erkrankungen, obwohl prinzipiell jedes Organsystem betroffen sein kann. Sie können signifikant zur Morbidität von CED-Patienten beitragen und die Lebensqualität deutlich einschränken. Die Betreuung sollte aufgrund der Vielfalt der betroffenen Organsysteme interdisziplinär durch ein in der CED-Behandlung geschultes medizinisches Personal erfolgen. Ein frühes Erkennen von extraintestinalen Manifestationen ermöglicht eine gezielte Therapie und verringert die Gesamtmorbidität der betroffenen Patienten. Insbesondere kann eine effektive Erhaltungstherapie das Auftreten von Manifestationen, die eng mit der Krankheitsaktivität der zugrunde liegenden CED verknüpft sind, vermeiden helfen. Neben spezifischen Interventionen, die nicht mit der Krankheitsaktivität der CED verknüpft sind, spielt eine antiinflammatorische oder immunmodulatorische Therapie eine entscheidende Rolle. Zudem gewinnt die Verwendung einer TNF-Hemmer-Therapie in der Behandlung von verschiedenen extraintestinalen Manifestationen zunehmend an Bedeutun