Review – Cities and Agriculture

As people increasingly migrate to urban settings and with more than half of the world’s population now living in cities, it is vital to plan and provide for sustainable and resilient food systems which reflect this challenge. The book ‘Cities and Agriculture’ presents experience and evidence on key dimensions of urban food challenges and types of intra-and peri-urban agriculture, in 15 extremely well-researched and written chapters. The book has shed light on an urban challenge that has been ignored for a long time in urban studies as well as in urban policies and planning, i.e. food-provisioning. Neglecting the dynamics and sustainability of food provisioning in scientific research on sustainable urban development is a serious omission, because feeding cities arguably has a greater social and physical impact on us and our planet than anything else we do

An Extended Mean Field Game for Storage in Smart Grids

We consider a stylized model for a power network with distributed local power generation and storage. This system is modeled as network connection a large number of nodes, where each node is characterized by a local electricity consumption, has a local electricity production (e.g. photovoltaic panels), and manages a local storage device. Depending on its instantaneous consumption and production rates as well as its storage management decision, each node may either buy or sell electricity, impacting the electricity spot price. The objective at each node is to minimize energy and storage costs by optimally controlling the storage device. In a non-cooperative game setting, we are led to the analysis of a non-zero sum stochastic game with $N$ players where the interaction takes place through the spot price mechanism. For an infinite number of agents, our model corresponds to an Extended Mean-Field Game (EMFG). In a linear quadratic setting, we obtain and explicit solution to the EMFG, we show that it provides an approximate Nash-equilibrium for $N$-player game, and we compare this solution to the optimal strategy of a central planner.Comment: 27 pages, 5 figures. arXiv admin note: text overlap with arXiv:1607.02130 by other author

Cell biology and immunology of malaria.

Malaria is a vector-borne infectious disease caused by unicellular parasites of the genus Plasmodium. These obligate intracellular parasites have the unique capacity to infect and replicate within erythrocytes, which are terminally differentiated host cells that lack antigen presentation pathways. Prior to the cyclic erythrocytic infections that cause the characteristic clinical symptoms of malaria, the parasite undergoes an essential and clinically silent expansion phase in the liver. By infecting privileged host cells, employing programs of complex life stage conversions and expressing varying immunodominant antigens, Plasmodium parasites have evolved mechanisms to downmodulate protective immune responses against ongoing and even future infections. Consequently, anti-malaria immunity develops only gradually over many years of repeated and multiple infections in endemic areas. The identification of immune correlates of protection among the abundant non-protective host responses remains a research priority. Understanding the molecular and immunological mechanisms of the crosstalk between the parasite and the host is a prerequisite for the rational discovery and development of a safe, affordable, and protective anti-malaria vaccine

On the Activity Privacy of Blockchain for IoT

Security is one of the fundamental challenges in the Internet of Things (IoT) due to the heterogeneity and resource constraints of the IoT devices. Device classification methods are employed to enhance the security of IoT by detecting unregistered devices or traffic patterns. In recent years, blockchain has received tremendous attention as a distributed trustless platform to enhance the security of IoT. Conventional device identification methods are not directly applicable in blockchain-based IoT as network layer packets are not stored in the blockchain. Moreover, the transactions are broadcast and thus have no destination IP address and contain a public key as the user identity, and are stored permanently in blockchain which can be read by any entity in the network. We show that device identification in blockchain introduces privacy risks as the malicious nodes can identify users' activity pattern by analyzing the temporal pattern of their transactions in the blockchain. We study the likelihood of classifying IoT devices by analyzing their information stored in the blockchain, which to the best of our knowledge, is the first work of its kind. We use a smart home as a representative IoT scenario. First, a blockchain is populated according to a real-world smart home traffic dataset. We then apply machine learning algorithms on the data stored in the blockchain to analyze the success rate of device classification, modeling both an informed and a blind attacker. Our results demonstrate success rates over 90\% in classifying devices. We propose three timestamp obfuscation methods, namely combining multiple packets into a single transaction, merging ledgers of multiple devices, and randomly delaying transactions, to reduce the success rate in classifying devices. The proposed timestamp obfuscation methods can reduce the classification success rates to as low as 20%