MOBILIZING A CROWD OF SARDINES: MEDIATED FRAMING DYNAMICS IN SOCIAL MOVEMENTS’ EMERGENCE

Research on framing has devoted attention, in the last decade, on social movements and on how they use frames to mobilize consensus, gain resources and build and consolidate their identity. However, the ongoing meaning construction process of the social movements’ and how it is mediated by social media is not investigated in-depth from an empirical point of view. Furthermore, the role of the crowd in these processes is still less known. Our research addresses these two main gaps by looking at the dynamics of a social movement emergence and of its mediated framing processes. We develop a longitudinal case study of the Italian social movement of “6000 sardine”, that scaled-up quickly and successfully online and by flash mobs organized in more than 125 cities in Italy and abroad in just a few weeks. Our study aims at contributing to the understanding of the framing mechanisms at play during the early phase of a movement development, putting central stage the role of social media and the crowd

Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

In contemporary urban logistics, drones will become a preferred transportation mode for last-mile deliveries, as they have shown commercial potential and triple-bottom-line performance. Drones, in fact, address many challenges related to congestion and emissions and can streamline the last leg of the supply chain, while maintaining economic performance. Despite the common conviction that drones will reshape the future of deliveries, numerous hurdles prevent practical implementation of this futuristic vision. The sharing economy, referred to as a collaborative business model that foster sharing, exchanging and renting resources, could lead to operational improvements and enhance the cost control ability and the flexibility of companies using drones. For instance, the Amazon patent for drone beehives, which are fulfilment centers where drones can be restocked before flying out again for another delivery, could be established as a shared delivery systems where different freight carriers jointly deliver goods to customers. Only a few studies have addressed the problem of operating such facilities providing services to retail companies. In this paper, we formulate the problem as a deterministic location-routing model and derive its robust counterpart under the travel time uncertainty. To tackle the computational complexity of the model caused by the non-linear energy consumption rates in drone battery, we propose a tailored matheuristic combining variable neighborhood descent with a cut generation approach. The computational experiments show the efficiency of the solution approach especially compared to the Gurobi solver

Energy Efficient UAV-Based Last-Mile Delivery: A Tactical-Operational Model With Shared Depots and Non-Linear Energy Consumption

In this paper, we have investigated a drone delivery problem to address the tactical decisions arising in last-mile applications where the connection with operational plans is taken into account. The problem deals with the tactical selection of a subset of FCs to launch and retrieve the drones, and the fleet sizing decisions on the optimal number of drones to be employed. We have incorporated the non-linear and load-dependent energy consumption function into the definition of a load-indexed layered network, leading to the definition of a MILP that can be efficiently solved for instances with 50 and 75 customers. There are several fruitful directions for future research. The use of shared depots implies for the drones the freedom to choose different FCs for departure and arrival. Anyway, a drawback may exist in the considered scenario, since we should have enough drones in each FC for the next period. The extension of the present model to the multi-period location routing case, where the location decisions are taken once and the routing plans are addressed within each period, is an interesting issue for future research. Moreover, the design of heuristic and self-adaptive approaches to alleviate the computational burden for larger instances deserves further attention, as well as the extension of the present model to en-route drone charging

The influence of lateral transport on sedimentary alkenone paleoproxy signals

[EN]Alkenone signatures preserved in marine sedimentary records are considered one of the most robust paleothermometers available and are often used as a proxy for paleoproductivity. However, important gaps remain regarding the provenance and fate of alkenones, as well as their impact on derived environmental signals in marine sediments. Here, we analyze the abundance, distribution and radiocarbon (14C) age of alkenones in bulk sediments and corresponding grain-size fractions in surficial sediments from seven continental margin settings in the Pacific and Atlantic oceans to evaluate the impact of organo-mineral associations and hydrodynamic sorting on sedimentary alkenone signals. We find that alkenones preferentially reside within fine-grained mineral fractions of continental margin sediments, with the preponderance of alkenones residing within the fine-silt fraction (2–10 µm) and most strongly influencing alkenone-14C age and sea surface temperature (SST) signals from bulk sediments as a consequence of their proportional abundance and higher degree of organic matter protection relative to other fractions. Our results provide further evidence for the key role of selective association of alkenones with mineral surfaces and associated hydrodynamic mineral sorting processes on the reliability of alkenone signals encoded in marine sediments (14C age, content and distribution) and the fidelity of corresponding proxy records (productivity and sea SST) in the spatial and temporal domain

the optimal electric energy procurement problem under reliability constraints

Abstract We consider the problem faced by a large consumer that has to define the procurement plan to cover its energy needs. The uncertain nature of the problem, related to the spot price and energy needs, is dealt by the stochastic programming framework. The proposed approach provides the decision maker with a proactive strategy that covers the energy needs with a high reliability level and integrates the Conditional Value at Risk (CVaR) measure to control potential losses. We apply the approach to a real case study and emphasize the effect of the reliability value choice and the difference between risk neutral and adverse positions

A machine learning optimization approach for last-mile delivery and third-party logistics

Third-party logistics is now an essential component of efficient delivery systems, enabling companies to purchase carrier services instead of an expensive fleet of vehicles. However, carrier contracts have to be booked in advance without exact knowledge of what orders will be available for dispatch. The model describing this problem is the variable cost and size bin packing problem with stochastic items. Since it cannot be solved for realistic instances by means of exact solvers, in this paper, we present a new heuristic algorithm able to do so based on machine learning techniques. Several numerical experiments show that the proposed heuristics achieve good performance in a short computational time, thus enabling its real-world usage. Moreover, the comparison against a new and efficient version of progressive hedging proves that the proposed heuristic achieves better results. Finally, we present managerial insights for a case study on parcel delivery in Turin, Italy

The multi-vehicle profitable pick up and delivery routing problem with uncertain travel times

Abstract This paper addresses a variant of the known selective pickup and delivery problem with time windows. In this problem, a fleet composed of several vehicles with a given capacity should satisfy a set of customers requests consisting in transporting goods from a supplier (pickup location) to a customer (delivery location). The selective aspect consists in choosing the customers to be served on the basis of the profit collected for the service. Motivated by urban settings, wherein road congestion is an important issue, in this paper, we address the profitable pickup and delivery problem with time windows with uncertain travel times. The problem under this assumption, becomes much more involved. The goal is to find the solution that maximizes the net profit, expressed as the difference between the collected revenue, the route cost and the cost associated to the violation the time windows. This study introduces the problem and develops a solution approach to solve it. Very preliminary tests are performed in order to show the efficiency of developed method to cope with the problem at hand

Decentralizing Electric Vehicle Supply Chains: Value Proposition and System Design

Distributed ledger technologies are transforming existing business models and business relationships. In particular, blockchain allows non-trusting parties to manage a shared database in a decentralized way and improve the transparency, authenticity, and reliability of the exchanged data. Nonetheless, decentralized paradigms are not yet well established, resulting in only a fraction of blockchain-based applications being successful in the long term.In this paper, we present a blockchain-based solution for the electric vehicle supply chain that we designed in the context of the CONCORDIA project of the European Cybersecurity Competence Network. We describe the goals, the value proposition, the main design choices, and the architecture of our system. Moreover, we discuss the electric vehicle supply chain, analyzing the improvements and limitations introduced by our blockchain-based solution. We analyze our solution from the managerial and technical points of view through a lean business methodology for blockchain solutions. In particular, we developed an economic impact assessment to evaluate the potential costs and revenues of the application of blockchain technology in a supply chain context. Although the blockchain system is inspired by the supply chain of a multinational automotive company, it can be applied to any other multi-actor supply chain

Coronavirus–associated enteritis in a quail farm

An enteric syndrome observed in semi-intensively reared quails is described. The affected birds showed depression, severe diarrhoea and dehydration. The mortality occurred particularly in young birds. At necropsy, the prominent lesion was catarrhal enteritis. Laboratory investigations demonstrated the presence of coronavirus in the gut of dead animals. No additional pathogens were detected. To our knowledge, this is the first evidence for the presence of CoVs in quail with enteritis