Upstream Horizontal Mergers, Bargaining and Vertical Contracts

Contrary to the seminal paper of Horn and Wolinsky (1988), we demonstrate that upstream firms, which sell their products to competing downstream firms, do not always have incentives to merge horizontally. In particular, we show that when bargaining takes place over two-part tariffs, and not over wholesale prices, upstream firms prefer to act as independent suppliers rather than as a monopolist supplier. Moreover, we show that horizontal mergers can be procompetitive, even in the absence of efficiency gains.horizontal mergers; bargaining; vertical relations; two-part tariffs; wholesale

(In)efficient trading forms in competing vertical chains

We study competing vertical chains where upstream and downstream firms bargain over their form and terms of trading. Both (conditionally) inefficient wholesale price contracts and efficient contracts that take the form of price-quantity bundles (and not of two-tariffs) arise in equilibrium under different parameter configurations. Changes in bargaining power distribution affect market outcomes by altering the trading terms and, more importantly, the trading form. As a result, a firm might benefit by a reduction in its bargaining power and consumers could benefit from an increase in the downstream �countervailing power� or from a more uneven bargaining power distribution.Vertical chains; strategic contracting; bargaining; two-part tariffs; price-quantity bundles; wholesale prices; vertical integration

Measuring the Internet Skills of Gen Z Students in Higher Education: Validation of the Internet Skills Scale in University Settings

[EN] Internet technologies have infiltrated higher education institutions around the world. At the same time, the latest generation of students, the so-called Generation Z (Gen Z), are entering higher education. Gen Z is the first generation born in an Internet-connected world, and digital devices are a seamless part of its life. As a result, Gen Z students have already been engaged with informal digital learning via internet-based technologies outside of formalized education settings. However, previous research has shown that their engagement with these technologies is limited and might not sufficiently cover the knowledge and skills needed to perform internet activities effectively in higher education. Additionally, their familiarity with digital devices and tools varies. Consequently, there is a need for higher education institutions to close the skills gap by applying assessment processes that will assist them in forming policies and training resources for undergraduate students. To achieve the above, research efforts need to focus on developing theoretically informed and valid instruments that measure internet skills. This study has contributed to the validation of a self-assessment questionnaire, the Internet Skills Scale, that can be used in university settings. The questionnaire measures five types of internet skills: operational, information-navigation, social, creative, and critical. The results presented herein provide directions for future research in the field.Miliou, O.; Angeli, C. (2021). Measuring the Internet Skills of Gen Z Students in Higher Education: Validation of the Internet Skills Scale in University Settings. En 7th International Conference on Higher Education Advances (HEAd'21). Editorial Universitat Politècnica de València. 1359-1368. https://doi.org/10.4995/HEAd21.2021.13070OCS1359136

Reduced-order modelling of vortex-induced vibration of catenary riser

A new reduced-order model capable of analyzing the vortex-induced vibration of catenary riser in the ocean current has been developed. This semi analytical-numerical approach is versatile and allows for a significant reduction in computational effort for the analysis of fluid-riser interactions. The incoming current flow is assumed to be steady, uniform, unidirectional and perpendicular to the riser plane of initial equilibrium curvatures. The equations of riser 3-D motion are based on a pinned-pinned, tensioned-beam or flexural cable, modelling which accounts for overall effects of riser bending, extensibility, sag, inclination and structural nonlinearities. The unsteady hydrodynamic forces associated with cross-flow and in-line vibrations are modelled as distributed van der Pol wake oscillators. This hydrodynamic model has been modified in order to capture the effect of varying initial curvatures of the inclined flexible cylinder and to describe the space-time fluctuation of lift and drag forces. Depending on the vortex-excited in-plane/out-of-plane modes and system fluid-structure parameters, the parametric studies are carried out to determine the maximum response amplitudes of catenary risers, along with the occurrence of uni-modal lock-in phenomenon. The obtained results highlight the effect of initial curvatures and geometric nonlinearities on the nonlinear dynamics of riser undergoing vortex-induced vibration

Efficient AIS Data Processing for Environmentally Safe Shipping

Reducing ship accidents at sea is important to all economic, environmental, and cultural sectors of Greece. Despite an increase in traffic and national monitoring, ships formulate routes according to their best judgment risking an accident. In this study we take a dataset spanning in 3 years from the AIS (Automatic Identification System) network, which is transmitting in public a ship's identity and location with an interval of seconds, and we load it in a trajectory database supported by the Hermes Moving Objects Database (MOD) system. Presented analysis begins by extracting statistics for the dataset, both general (number of ships and position reports) as well as safety related ones. Simple queries on the dataset illustrate the capabilities of Hermes and allow to gain insight on how the ships move in the Greek Seas. Analysis of movement based on an Origin-Destination matrix between interesting areas in the Greek territory is presented. One of the newest challenges that emerged during this process is that the amount of the positioning data is becoming more and more massive. As a conclusion, a preliminary review of possible solutions to this challenge along with others such as dealing with the noise in AIS data is mentioned and we also briefly discuss the need for interdisciplinary cooperation.This research was partially supported by AMINESS project funded by the Greek government (www.aminess.eu). Cyril Ray was supported by a Short Term Scientific Mission performed at the University of Piraeus by the COST Action IC0903 on “Knowledge Discovery from Moving Objects” (http://www.move-cost.info). IMIS Hellas (www.imishel las.gr) kindly provided the AIS dataset for research purposes

Understanding peace through the world news

Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peace through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country’s profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peace.Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peace through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country’s profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peace