Film Thickness Changes in EHD Sliding Contacts Lubricated by a Fatty Alcohol

This paper describes the appearance of abnormal film thickness features formed in elastohydrodynamic contacts lubricated by a fatty alcohol. Experiments were conducted by varying the slide to roll ratio between a steel ball and a glass disk in a ball-on-disk type device. Lauric alcohol was used as lubricant and film thickness was measured in the contact area by optical interferometry. Experimental results showed that the film thickness distributions under pure rolling conditions remained classical whereas the film shape changed when the slide to roll ratio was increased. The thickness in the central contact area increased and in the same time inlet and exit film thicknesses were modified. In addition, the film shapes observed when the ball surface was moving faster than the disk one and those obtained in the opposite case were different, i.e. when opposite signs but equal absolute values of the slide to roll ratio were applied

Simplicial Homology for Future Cellular Networks

Simplicial homology is a tool that provides a mathematical way to compute the connectivity and the coverage of a cellular network without any node location information. In this article, we use simplicial homology in order to not only compute the topology of a cellular network, but also to discover the clusters of nodes still with no location information. We propose three algorithms for the management of future cellular networks. The first one is a frequency auto-planning algorithm for the self-configuration of future cellular networks. It aims at minimizing the number of planned frequencies while maximizing the usage of each one. Then, our energy conservation algorithm falls into the self-optimization feature of future cellular networks. It optimizes the energy consumption of the cellular network during off-peak hours while taking into account both coverage and user traffic. Finally, we present and discuss the performance of a disaster recovery algorithm using determinantal point processes to patch coverage holes

Homology-based Distributed Coverage Hole Detection in Wireless Sensor Networks

Homology theory provides new and powerful solutions to address the coverage problems in wireless sensor networks (WSNs). They are based on algebraic objects, such as Cech complex and Rips complex. Cech complex gives accurate information about coverage quality but requires a precise knowledge of the relative locations of nodes. This assumption is rather strong and hard to implement in practical deployments. Rips complex provides an approximation of Cech complex. It is easier to build and does not require any knowledge of nodes location. This simplicity is at the expense of accuracy. Rips complex can not always detect all coverage holes. It is then necessary to evaluate its accuracy. This work proposes to use the proportion of the area of undiscovered coverage holes as performance criteria. Investigations show that it depends on the ratio between communication and sensing radii of a sensor. Closed-form expressions for lower and upper bounds of the accuracy are also derived. For those coverage holes which can be discovered by Rips complex, a homology-based distributed algorithm is proposed to detect them. Simulation results are consistent with the proposed analytical lower bound, with a maximum difference of 0.5%. Upper bound performance depends on the ratio of communication and sensing radii. Simulations also show that the algorithm can localize about 99% coverage holes in about 99% cases

Construction of the generalized Cech complex

In this paper, we introduce an algorithm which constructs the generalized Cech complex. The generalized Cech complex represents the topology of a wireless network whose cells are different in size. This complex is often used in many application to locate the boundary holes or to save energy consumption in wireless networks. The complexity of a construction of the Cech complex to analyze the coverage structure is found to be a polynomial time

Bitcoin and the Rise of Decentralized Autonomous Organizations

Bitcoin represents the first real-world implementation of a “decentralized autonomous organization” (DAO) and offers a new paradigm for organization design. Imagine working for a global business organization whose routine tasks are powered by a software protocol instead of being governed by managers and employees. Task assignments and rewards are randomized by the algorithm. Information is not channelled through a hierarchy but recorded transparently and securely on an immutable public ledger called “blockchain”. Further, the organization decides on design and strategy changes through a democratic voting process involving a previously unseen class of stakeholders called “miners”. Agreements need to be reached at the organizational level for any proposed protocol changes to be approved and activated. How do DAOs solve the universal problem of organizing with such novel solutions? What are the implications? We use Bitcoin as an example to shed light on how a DAO works in the cryptocurrency industry, where it provides a peer-to-peer, decentralized and disintermediated payment system that can compete against traditional financial institutions. We also invite commentaries from renowned organization scholars to share their views on this intriguing phenomenon

A case study on regularity in cellular network deployment

This paper aims to validate the $\beta$-Ginibre point process as a model for the distribution of base station locations in a cellular network. The $\beta$-Ginibre is a repulsive point process in which repulsion is controlled by the $\beta$ parameter. When $\beta$ tends to zero, the point process converges in law towards a Poisson point process. If $\beta$ equals to one it becomes a Ginibre point process. Simulations on real data collected in Paris (France) show that base station locations can be fitted with a $\beta$-Ginibre point process. Moreover we prove that their superposition tends to a Poisson point process as it can be seen from real data. Qualitative interpretations on deployment strategies are derived from the model fitting of the raw data