Contributions in Merlin

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The world we know has changed over a brief period, with the ascent and spread of Covid-19. This affected the education sector resulting in offline classes to online classes. Technologies made it easy with numerous websites, materials, video lectures, courses, and techniques for the students. In this situation, the main problem arises with students not participating in the class having many reasons such as illness, being introverted, and feeling that they may be wrong. If we are not interested in something to talk about or are shy, we must face it so that we will make the best out of it. People remember the things they listen to carefully, so we can probably study less if we listen. In this research paper, we proposed a Blockchain based application, so students who are going to attend online classes would be able to participate more in the class. They will be able to gain incentives that are based on crypto currency and by using those cryptocurrencies they can spend it on fees or any other resources in the university which would be beneficial for students. They will be able to gain incentives that are based on cryptocurrency and by using those crypto currencies they can spend it on fees or any other resources in the university. We are using the most popular technology which is Blockchain technology to make sure that students who are attending online classes will be able to pay more attention. Also, we are using the most famous functionality of Blockchain which is incentivization. To give rewards to the students we will be using incentivization so they can pay more attention to the classes. This design is beneficial for teachers also to look at the status of each student and get in contact with them.

Studying networked systems in a variety of domains, including biology, social science and internet of things, has recently received a surge of attention. For a networked system, there are usually multiple types of interactions between its components, and such interaction type information is crucial since it always associated with important features. However, some interaction types which actually exist in the network may not be observed in the metadata collected in practice. This paper proposes an approach aiming to detect previously undiscovered interaction types (PUITs) in networked systems. The first step in our proposed PUIT detection approach is to answer the following fundamental question: is it possible to effectively detect PUITs without utilizing metadata other than the existing incomplete interaction type information and the connection information of the system? Here, we first propose a temporal network model which can be used to mimic any real network and then discover that some special networks which fit the model shall a common topological property. Supported by this discovery, we finally develop a PUIT detection method for networks which fit the proposed model. Both analytical and numerical results show this detection method is more effective than the baseline method, demonstrating that effectively detecting PUITs in networks is achievable. More studies on PUIT detection are of significance and in great need since this approach should be as essential as the previously undiscovered node type detection which has gained great success in the field of biology.

Children’s self-regulation abilities are key predictors of educational success and other life outcomes such as income and health. However, self-regulation is not a school subject, and knowledge about how to generate lasting improvements in self-regulation and academic achievements with easily scalable, low-cost interventions is still limited. Here we report the results of a randomized controlled field study that integrates a short self-regulation teaching unit based on the concept of mental contrasting with implementation intentions into the school curriculum of first graders. We demonstrate that the treatment increases children’s skills in terms of impulse control and self-regulation while also generating lasting improvements in academic skills such as reading and monitoring careless mistakes. Moreover, it has a substantial effect on children’s long-term school career by increasing the likelihood of enroling in an advanced secondary school track three years later. Thus, self-regulation teaching can be integrated into the regular school curriculum at low cost, is easily scalable, and can substantially improve important abilities and children’s educational career path.

Kompletter Ausstieg aus klimaschädlichen Anlagen oder die Unternehmen positiv beeinflussen? Die Meinungen sind geteilt.

Die Unsummen von Geldern, die in nachhaltige Anlagen gesteckt werden, sind zwar kein unwirksames Placebo. In diesen Krisenmonaten muss diese Anlageform allerdings den ersten echten Härtetest bestehen, wie sich an einer Konferenz in Zürich herausstellte.

The crew diagramming problem has been a challenging task in the railway industry. The massive amount of tasks, requiring multiple crew members to work together under a large set of constrains, create a large scale optimization problem. The common approach to solve this problem which is widely used by the railway companies, is to formulate an integer linear program and decompose it by column generation into a master problem and a sub-problem. However, the sub-problem is often a resource constrained shortest path problem which is NP-hard and the computational time is in the order of hours to days.  We designed an alternative algorithmic approach for the crew diagramming by performing Danztig-Wolfe decomposition on a flow based linear program with set partition constraint. Even though the master problem is still a set partition LP with column generation and minimum cost flow pricing problem, we reduced the number of constraints and avoided solving an NP-hard problem. In order to speed up solving the pricing problem, we split the original network into multiple sub-networks and paralellized the computation of the pricing models. In the end we solved the master problem as an integer programming problem only with the variables selected by the column generation.  This optimization approach was then implemented on real world data provided by Swiss Railways. We compared the results in terms of computational time with the flow based optimizer developed by Algomia GmbH and found that for small and medium-sized depots, our approach is faster on average when compared to the computation time required to arrive only at the LP-solution with Algomia's algorithm. When comparing our results in terms of the number of diagrams with those produced by manual planners, we found a consistent improvement for larger-size depots, where slight adjustments to the constraints often performed by planners have less of an impact on the overall result.

Atrial fibrillation (AF) is the most common type of arrhythmia occurring as an irregular excitation from the ventricles that affects the function of the heart and increases the risk of stroke and heart attack, leading to an extremely high mortality rate worldwide. Nearly half of all patients with AF are those with paroxysmal atrial fibrillation (PAF), and the chances of cure with medical intervention at this stage are very high compared to later stages. Therefore, the early detection and prediction of PAF are clinically important. However, due to the asymptomatic and interim episodic nature of PAF, its early detection and onset prediction have been challenging topics. Recent advances in the field of artificial intelligence, particularly machine learning techniques based on electrocardiogram(ECG) data, including deep learning, have enabled the development of PAF prediction. The goal of this work is to review the published studies related to PAF prediction, and predict the onset of PAF in advance with the shortened ECG signals and a high degree of accuracy. We systematically review the publications over the past 10 years, focusing on the prediction of PAF enabled by ECG-based machine learning models. Totally 15 studies of both traditional machine learning and deep learning models proposed over the past decade are covered and reviewed in this work. We propose a novel neural network framework PAFNet based on a convolution neural networks (CNN) with residual structure, a bi-directional Long-short-term memory network (LSTM) and the attention mechanism to predict the onset of PAF in advance. The PAFNet model is evaluated on public datasets and compared with previous studies with good performance. We describe the ideas behind the model design and analyze and discuss the results of the experiments. Finally we also discuss the potential future progress and challenges in the field.

This bachelor thesis investigates different use cases of using artificial intelligence to reduce city transport emissions with a focus on using machine learning to forecast passenger demand. We first conducted a literature review to gather the different uses of artificial intelligence in city transport emission reduction. Then we developed a prototype that uses artificial intelligence to predict the public transport passenger demand for the city of Zurich using the design science research method. Last we presented and evaluated the usability of the prototype in a workshop. Our results highlight applications of artificial intelligence that can be used in city transport emission reduction. Our prototype shows how artificial intelligence can be used to predict public transport passenger demand in the city of Zurich. The findings from the evaluation show how transport decision makers can use our prototype to reduce city transport emissions, We conclude that artificial intelligence methods can be used to support measures that reduce transport emissions in cities, by providing citiesí decision makers with information about traffic, passenger demand, mobility patterns, transport network design, electrification, and emissions. Passenger demand predictions made by artificial intelligence can be used by transport companies to reduce transport emissions through better public transport planning.

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