The distributed p-median problem in computer networks

Many distributed services in computer networks rely on a set of active facilities that are selected among a potentially large number of candidates. The active facilities then contribute and cooperate to deliver a specific service to the users of the distributed system. In this scenario graph partitioning or clustering is often adopted to determine the most efficient locations of the facilities. The identification of the optimal set of facility locations is known as the p-median problem in networks, is NP-hard and is typically solved by using heuristic methods. The goal is to select p locations among all candidate network nodes such that some cost function is minimised. A typical example of such a function is the overall communication cost to deliver the service to the users of the distributed system. Locating facilities in near-optimal locations has been extensively studied for different application domains. Most of these studies have investigated sequential algorithms and centralised approaches. However, centralised approaches are practically infeasible in large-scale and dynamic networks, where the problem is inherently distributed or because of the large communication overhead and memory requirements for gathering complete information about the network topology and the users. In this work distributed approaches to the p-median problem are investigated. Two solutions are proposed for addressing the facility locations problem in a fully distributed environment. Two different iterative heuristic approaches are applied to gradually improve a random initial solution and to converge to a final solution with a local minimum of the overall cost. While the first approach adopts a fine granularity by identifying a single change to improve the solution at each iteration, the second approach applies changes to every component of the solution at each iteration. An experimental comparative analysis based on simulations has shown that the approach with a finer granularity is able to deliver a better optimisation of the overall cost with longer convergence time. Both approaches have excellent scalability and provide an effective tool to optimise the facility locations from within the network. No prior knowledge of the system is required, no data needs to be gathered in a centralised server and the same process is used to identify and to deploy the facility locations solution in the network since the process is fully decentralised

Immunosuppressive effects of radiation on human dendritic cells: reduced IL-12 production on activation and impairment of naïve T-cell priming

Dendritic cells (DC) are professional antigen-presenting cells (APC) of the immune system, uniquely able to prime naïve T-cell responses. They are the focus of a range of novel strategies for the immunotherapy of cancer, a proportion of which include treating DC with ionising radiation to high dose. The effects of radiation on DC have not, however, been fully characterised. We therefore cultured human myeloid DC from CD14+ precursors, and studied the effects of ionising radiation on their phenotype and function. Dendritic cells were remarkably resistant against radiation-induced apoptosis, showed limited changes in surface phenotype, and mostly maintained their endocytic, phagocytic and migratory capacity. However, irradiated DC were less effective in a mixed lymphocyte reaction, and on maturation produced significantly less IL-12 than unirradiated controls, while IL-10 secretion was maintained. Furthermore, peptide-pulsed irradiated mature DC were less effective at naïve T-cell priming, stimulating fewer effector cells with lower cytotoxicity against antigen-specific targets. Hence irradiation of DC in vitro, and potentially in vivo, has a significant impact on their function, and may shift the balance between T-cell activation and tolerisation in DC-mediated immune responses

Iliotibial band release as an adjunct to the surgical management of patellar stress fracture in the athlete: a case report and review of the literature

Stress fracture of the patella is rare. In this report, a case of patellar stress fracture occurring in an amateur athlete is presented, and an operative adjunct to the surgical management of this condition is proposed

Optimizing the location of helicopter emergency medical service operating sites

The European Commission Regulation (EU) No 965/2012, now completely operative in all the European countries, allows helicopter night landings for emergency medical service in dedicated spaces, provided with a minimum amount of facilities, called HEMS Operating Sites. This possibility opens new scenarios for the mixed, ambulance/ helicopter, rescue procedure, today not fully exploited. The paper studies the problem of optimal positioning for HEMS sites, where the transfer of the patient from ambulance to helicopter takes place, through the use of Geographic Information System (GIS) and optimization algorithms integrated in the software ArcGIS for Desktop. The optimum is defined in terms of the minimum intervention time. The solution approach has been applied to the area of competence of “SOREU dei Laghi”, in Lombardia region, with a catchment area of almost two million people

An Evolutionary Algorithm for the Leader-Follower Facility Location Problem with Proportional Customer Behavior

The leader-follower facility location problem arises in the context of two non-cooperating companies, a leader and a follower, competing for market share from a given set of customers. In our work we assume that the rms place a given number of facilities on locations taken from a discrete set of possible points. The customers are assumed to split their demand inversely proportional to their distance to all opened facilities. In this work we present an evolutionary algorithm with an embedded tabu search to optimize the location selection for the leader. A complete solution archive is used to detect already visited candidate solutions and convert them into not yet considered ones. This avoids unnecessary time-consuming re-evaluations, reduces premature convergence and increases the population diversity at the same time. Results show significant advantages of our approach over an existing algorithm from the literature