15. European parties:

The party system of the European Parliament (EP) has been dominated by the two main European party families: centre-right conservatives and Christian democrats, on the one hand, and centre-left social democrats on the other, which controlled the majority of the seats until the 2019 elections. In the early 1950s, members of the European Parliament (MEPs) decided to form party-political groups, instead of national blocs, to counterbalance the dominance of national interests in the Council. Over the decades, the shape of the EP party system has become more stable, and traditional levels of group cohesion and coalition formation have not really been affected by the rise of populism and the increasing politicization of European integration. National parties remain influential within party groups, not least through their control of candidate selection. Outside of the Parliament, Europarties—parties operating at the European level—influence both the broader development of integration and the choice of the Commission president.

A Coalition Formation Game for Cooperative Spectrum Sensing in Cognitive Radio Network under the Constraint of Overhead

Cooperative spectrum sensing improves the sensing performance of secondary users by exploiting spatial diversity in cognitive radio networks. However, the cooperation of secondary users introduces some overhead also that may degrade the overall performance of cooperative spectrum sensing. The trade-off between cooperation gain and overhead plays a vital role in modeling cooperative spectrum sensing. This paper considers overhead in terms of reporting energy and reporting time. We propose a cooperative spectrum sensing based coalitional game model where the utility of the game is formulated as a function of throughput gain and overhead. To achieve a rational average throughput of secondary users, the overhead incurred is to be optimized. This work emphasizes on optimization of the overhead incurred. In cooperative spectrum sensing, the large number of cooperating users improve the detection performance, on the contrary, it increases overhead too. So, to limit the maximum coalition size we propose a formulation under the constraint of the probability of false alarm. An efficient fusion center selection scheme and an algorithm to select eligible secondary users for reporting are proposed to reduce the reporting overhead. We also outline a distributed cooperative spectrum sensing algorithm using the properties of the coalition formation game and prove that the utility of the proposed game has non-transferable properties. The simulation results show that the proposed schemes reduce the overhead of reporting without compromising the overall detection performance of cooperative spectrum sensing.

The Power of Bipartisan Mobilization: The Success of Tunisia’s Feminist Movement During the Coronavirus Pandemic

The Tunisian government, which is deeply divided, especially along ideological lines, responded to growing concerns over increased violence against women during the Coronavirus pandemic by establishing a new domestic violence shelter and 24/7 hotline. This article asks: Why did the state respond to gender-based violence(gbv) concerns during the Coronavirus pandemic in Tunisia, despite ideological and political divisions? We argue that the state addressed some concerns around violence during the pandemic because combatting gbv has bipartisan support in Tunisia. Tunisian Islamist and secularist women’s rights organizations succeeded in building a bipartisan coalition of support on this issue because they worked either together in a short-lived coalition or in tandem with similar goals over the past decade during the democratic transition in Tunisia. Building on the existing coalition literature, we show that feminist coalition formation before a pandemic has implications for feminists’ success in times of crisis.

The Fisher Market Equilibrium Price Based Multi-Access Edge Computing Coalition Formation

The emergence of multi-access edge computing (MEC) aims at extending cloud computing capabilities to the edge of the radio access network. As the large-scale IoT services are rapidly growing, a single edge infrastructure provider (EIP) may not be sufficient to handle the data traffic generated by these services. The coalition method has been used in MEC for resource optimization, latency, energy consumption reduction, computation offloading, etc. However, the majority of research does not consider the price of computing resources corresponded to a container. Moreover, each SP does not choose EIP with the highest cost-performance to sign a medium/long-term computing resource purchase or lease contract. In this work, we consider a scenario with a collection of SPs with different budgets and several EIPs distributed in geographical locations. During the first phase, we get the market equilibrium price and select the optimal EIPs to make a deal by solving the Eisenberg-Gale convex program. In the second stage, using a mathematical model, we maximize EIP's profits and form stable coalitions between EIPs by a distributed coalition formation algorithm. Numerical results demonstrate that the effectiveness of our method is significantly better than the existing model.