Mind the Gap – Passenger Arrival Patterns in Multi-agent Simulations

In most studies mathematical models are developed finding the expected waiting time to be a function of the headway. These models have in common that the proportion of passengers that arrive randomly at a public transport stop is less as headway in-creases. Since there are several factors of influence, such as social demographic or regional aspects, the reliability of public transport service and the level of passenger information, the threshold headway for the transition from random to coordinated passenger arrivals vary from study to study. This study's objective is to investigate if an agent-based model exhibits realistic passenger arrival behavior at transit stops. This objective is approached by exploring the sensitivity of the agents' arrival behavior towards (1) the degree of learning, (2) the reliability of the experienced transit service, and (3) the service headway. The simulation experiments for a simple transit corridor indicate that the applied model is capable of representing the complex passenger arrival behavior observed in reality. (1) For higher degrees of learning, the agents tend to over-optimize, i.e. they try to obtain the latest possible departure time exact to the second. An approach is presented which increases the diversity in the agents' travel alternatives and results in a more realistic behavior. (2) For a less reliable service the agents' time adaptation changes in that a buffer time is added between their arrival at the stop and the actual departure of the vehicle. (3) For the modification of the headway the simulation outcome is consistent with the literature on arrival patterns. Smaller headways yield a more equally distributed arrival pattern whereas larger headways result in more coordinated arrival patterns

Effects of UV and Elevation on Flavonoid Production in Juvenile Landrace Maize Leaf Tissue

Many plant populations are locally adapted to conditions that vary across the landscape. At the maize center of diversity in Southern Mexico, maize landraces are locally adapted to the environments in which they grow. Maize from higher elevation zones has been selected under conditions of greater exposure to UV-B light compared to lower elevation landraces. This could be important as climate change urges farmers to plant varieties from warmer, lower climes at higher elevations with higher UV-exposure. Our goal was to understand the biochemical mechanisms that may maintain high performance under local conditions, and how this biochemistry changes under novel conditions. In this study, we investigated UV-B protective flavonoids produced by maize landraces collected at three elevations (600 m, 1550 m, and 2050 m) and planted in common gardens at 1550 m and 2050 m in Chiapas, Mexico. Using high performance liquid chromatography (HPLC), we examined the presence and quantity of maize leaf flavonoids maysin, quercetin-3-glucoside (q-3-glucoside), and quercetin-3-rutinoside (q-3-rutinoside) produced across 12 maize collections from three elevations (i.e. maize types) and garden locations. Due to results from a previous gene expression study and the likely adaptive importance of UV-B protection, we hypothesized that highland maize may have higher production of certain UV-B protective compounds than lower elevational types, with all types inducing greater production in response to increases in UV-B. Our results demonstrate that increases in elevation increased flavonoid production. This response was similar across maize types from different elevations, although highland types seemed to have a greater induced response. These results may have implications for maize production with climate change since moving of crop populations to higher elevations to address warming may be disrupted by differential UV-B adaptation. Future work could investigate the fitness effects on plants that employ these different flavonoid pathways under higher and lower UV-B conditions to better understand desired chemotypes and their biological consequences.ECOSUROSUA one-year embargo was granted for this item.Academic Major: Evolution and Ecolog

Developing a green curriculum for introduction to information technology course

© Springer Nature Switzerland AG 2020. No one can deny that information technology courses can be an excellent initiative for increasing the awareness of environmental and health problems. Hence, information technology instructors play a critical role in spreading awareness of green technology and reinforcing sustainability. The Introduction to Information Technology (IIT) course contains many topics that can be directed in an intelligent way to spread awareness about sustainability. In this paper, we suggest a detailed curriculum for the IT course that converts a standard curriculum into a green one. The goal of this curriculum is to produce a new generation that is aware enough about the role of technology in our life and its impact on the environment. The course topics will be the answers to the following questions. First, how to avoid the negative impact of technology on health and the environment. Second, how to decrease the negative impact that cannot be avoided or find a solution for it. Finally, what are the alternative green computing techniques that can be used to increase sustainability? The proposed curriculum should involve the instructor and the students. Both should collaborate in a set of activities that will give society a generation with high awareness of green computing and sustainability

Evaluation of Machine Learning Algorithm on Drinking Water Quality for Better Sustainability

Water has become intricately linked to the United Nations\u27 sixteen sustainable development goals. Access to clean drinking water is crucial for health, a fundamental human right, and a component of successful health protection policies. Clean water is a significant health and development issue on a national, regional, and local level. Investments in water supply and sanitation have been shown to produce a net economic advantage in some areas because they reduce adverse health effects and medical expenses more than they cost to implement. However, numerous pollutants are affecting the quality of drinking water. This study evaluates the efficiency of using machine learning (ML) techniques in order to predict the quality of water. Thus, in this paper, a machine learning classifier model is built to predict the quality of water using a real dataset. First, significant features are selected. In the case of the used dataset, all measured characteristics are chosen. Data are split into training and testing subsets. A set of existing ML algorithms is applied, and the results are compared in terms of precision, recall, F1 score, and ROC curve. The results show that support vector machine and k-nearest neighbor are better according to F1-score and ROC AUC values. However, The LASSO LARS and stochastic gradient descent are better based on recall values

Self-Organizing Networks use cases in commercial deployments

These measurements can be obtained from different sources, but these sources are either expensive or not applicable to any network. To solve this problem, this thesis proposes a method that uses information available in any network so that the calibration of predictive maps is converted into universal without losing accuracy with respect to current methods. Furthermore, the complexity of today's networks makes them prone to failure. To save costs, operators employ network self-healing techniques so that networks are able to self-diagnose and even self-fix when possible. Among the various failures that can occur in mobile communication networks, a common case is the existence of sectors whose radiated signal has been exchanged. This issue appears during the network roll-out when engineers accidentally cross feeders of several antennas. Currently, manual methodology is used to identify this problem. Therefore, this thesis presents an automatic system to detect these cases. Finally, special attention has been paid to the computational efficiency of the algorithms developed in this thesis since they have finally been integrated into commercial tools.Ince their origins, mobile communication networks have undergone major changes imposed by the need for networks to adapt to user demand. To do this, networks have had to increase in complexity. In turn, complexity has made networks increasingly difficult to design and maintain. To mitigate the impact of network complexity, the concept of self-organizing networks (SON) emerged. Self-organized networks aim at reducing the complexity in the design and maintenance of mobile communication networks by automating processes. Thus, three major blocks in the automation of networks are identified: self-configuration, self-optimization and self-healing. This thesis contributes to the state of the art of self-organized networks through the identification and subsequent resolution of a problem in each of the three blocks into which they are divided. With the advent of 5G networks and the speeds they promise to deliver to users, new use cases have emerged. One of these use cases is known as Fixed Wireless Access. In this type of network, the last mile of fiber is replaced by broadband radio access of mobile technologies. Until now, regarding self-configuration, greenfield design methodologies for wireless networks based on mobile communication technologies are based on the premise that users have mobility characteristics. However, in fixed wireless access networks, the antennas of the users are in fixed locations. Therefore, this thesis proposes a novel methodology for finding the optimal locations were to deploy network equipment as well as the configuration of their radio parameters in Fixed Wireless Access networks. Regarding self-optimization of networks, current algorithms make use of signal maps of the cells in the network so that the changes that these maps would experience after modifying any network parameter can be estimated. In order to obtain these maps, operators use predictive models calibrated through real network measurements

Agent-based Congestion Pricing and Transport Routing with Heterogeneous Values of Travel Time Savings

An existing agent-based simulation framework and congestion pricing methodology is extended towards a consistent consideration of non-linear, user- and trip-specific values of travel time savings (VTTS). The heterogeneous VTTS are inherent to the model and result from each agent's individual time pressure. An innovative approach is presented which accounts for the non-linear, user- and trip-specific VTTS (i) when converting external delays into congestion tolls and (ii) when generating new transport routes. The innovative pricing and routing methodology is applied to a real-world case study of the Greater Berlin area, Germany. The proposed methodology performs better than assuming a constant value of travel time savings or randomizing the routing relevant costs. The improved consistency of setting congestion toll levels, identifying transport routes and evaluating travel plans is found to result in a higher system welfare

Activity-Based Computation of Marginal Noise Exposure Costs : Implications for Traffic Management

In this paper, an innovative simulation-based approach is presented to calculate optimal dynamic, road- and vehicle-specific tolls on the basis of marginal traffic noise exposures. The proposed approach combines the advantages of an activity-based simulation with the economically optimal way of price setting. Temporal and spatial differences of traffic noise levels and population densities are considered. Moreover, noise exposures at work and educational activities are accounted for. The results of a case study for the area of Berlin showed that transport users avoided marginal noise cost payments by shifting to road stretches in areas with lower population densities, typically major roads. The simulation experiments indicated that the marginal cost approach could be used to improve the overall system welfare and to derive traffic control strategies