A genetic approach to joint routing and link scheduling for wireless mesh networks

Wireless mesh networks are an attractive technology for providing broadband connectivity to mobile clients who are just on the edge of wired networks, and also for building self-organized networks in places where wired infrastructures are not available or not deemed to be worth deploying. This paper investigates the joint link scheduling and routing issues involved in the delivery of a given backlog from any node of a wireless mesh network towards a specific node (which acts as a gateway), within a given deadline. Scheduling and routing are assumed to be aware of the physical interference among nodes, which is modeled in the paper by means of a signal-to-interference ratio. Firstly, we present a theoretical model which allows us to formulate the task of deriving joint routing and scheduling as an integer linear programming problem. Secondly, since the problem cannot be dealt with using exact methods, we propose and use a technique based on genetic algorithms. To the best of our knowledge, these algorithms have never been used before for working out these kinds of optimization problems in a wireless mesh environment. We show that our technique is suitable for this purpose as it provides a good trade-off between fast computation and the overall goodness of the solution found. Our experience has in fact shown that genetic algorithms would seem to be quite promising for solving more complex models than the one dealt with in this paper, such as those including multiple flows and multi-radio multi-channels.     

Anomalous weak values via a single photon detection

Is it possible that a measurement of a spin component of a spin-1/2 particle yields the value 100? In 1988 Aharonov, Albert and Vaidman argued that upon pre-and...     

Modelling of electricity savings in the Danish households sector: from the energy system to the end-user

In this paper, we examine the value of investing in energy-efficient household appliances from both an energy system and end-user perspectives. We consider a set of appliance categories constituting the majority of the electricity consumption in the private household sector, and focus on the stock of products which need to be replaced. First, we look at the energy system and investigate whether investing in improved energy efficiency can compete with the cost of electricity supply from existing or new power plants. To assess the analysis, Balmorel, a linear optimization model for the heat and power sectors, has been extended in order to endogenously determine the best possible investments in more efficient home appliances. Second, we propose a method to relate the optimal energy system solution to the end-user choices by incorporating consumer behaviour and electricity price addition due to taxes. The model is non-exclusively tested on the Danish energy system under different scenarios. Computational experiments show that several energy efficiency measures in the household sector should be regarded as valuable investments (e.g. an efficient lighting system) while others would require some form of support to become profitable. The analysis quantifies energy and economic savings from the consumer side and reveals the impacts on the Danish power system and surrounding countries. Compared to a business-as-usual energy scenario, the end-user attains net economic savings in the range of 30–40 EUR per year, and the system can benefit of an annual electricity demand reduction of 140–150 GWh. The paper enriches the existing literature about energy efficiency modelling in households, contributing with novel models, methods, and findings related to the Danish case.     

Calcium phosphate-based nanosystems for advanced targeted nanomedicine

Synthetic calcium phosphates (CaPs) are the most widely accepted bioceramics for the repair and reconstruction of bone tissue defects. The recent advancements in materials science have prompted a rapid progress in the preparation of CaPs with nanometric dimensions, tailored surface characteristics, and colloidal stability opening new perspectives in their use for applications not strictly related to bone. In particular, the employment of CaPs nanoparticles as carriers of therapeutic and imaging agents has recently raised great interest in nanomedicine. CaPs nanoparticles, as well as other kinds of nanoparticles, can be engineered to specifically target the site of the disease (cells or organs), thus minimizing their dispersion in the body and undesired organism-nanoparticles interactions. The most promising and efficient approach to improve their specificity is the ‘active targeting’, where nanoparticles are conjugated with a targeting moiety able to recognize and bind with high efficacy and selectivity to receptors that are highly expressed only in the therapeutic site. The aim of this review is to give an overview on advanced targeted nanomedicine with a focus on the most recent reports on CaP nanoparticles-based systems, specifically designed for the active targeting. The distinctive characteristics of CaP nanoparticles with respect to the other kinds of nanomaterials used in nanomedicine are also discussed.     

The Unbearable Shallow Understanding of Deep Learning

This paper analyzes the rapid and unexpected rise of deep learning within Artificial Intelligence and its applications. It tackles the possible reasons for this remarkable success, providing candidate paths towards a satisfactory explanation of why it works so well, at least in some domains. A historical account is given for the ups and downs, which have characterized neural networks research and its evolution from “shallow” to “deep” learning architectures. A precise account of “success” is given, in order to sieve out aspects pertaining to marketing or sociology of research, and the remaining aspects seem to certify a genuine value of deep learning, calling for explanation. The alleged two main propelling factors for deep learning, namely computing hardware performance and neuroscience findings, are scrutinized, and evaluated as relevant but insufficient for a comprehensive explanation. We review various attempts that have been made to provide mathematical foundations able to justify the efficiency of deep learning, and we deem this is the most promising road to follow, even if the current achievements are too scattered and relevant for very limited classes of deep neural models. The authors’ take is that most of what can explain the very nature of why deep learning works at all and even very well across so many domains of application is still to be understood and further research, which addresses the theoretical foundation of artificial learning, is still very much needed.

     

Ageing, Demand for Care and the Role of Migrant Care Workers in the UK

In recent decades, rapid population ageing has dramatically increased the need for older adult care provision in the UK. A prominent role in meeting the care needs of the older population has been played by migrant workers. The aim of this paper is to explore the characteristics of the UK social care system that shape demand for migrant labour, the conditions under which migrant care workers are employed, and older people’s and migrant care workers’ experiences of the quality of care. Our analysis draws on the findings of a survey of providers of social care for older people, in-depth interviews with migrant care workers, and focus groups with older people. The findings show that the underfunding of social care and interrelated workforce shortages are largely responsible for the extensive reliance on migrant workers among social care providers, and raise concerns for workforce inequalities and for the quality of care.     

Low-intensity optical bistability in an active Fabry-Perot mirror induced by input-phase-insensitive parametric downconversion

A Fabry-Perot resonator filled with second-order nonlinear optical material is investigated. Attention is devoted to making the resonator act as a nonlinear mirror for ultralow-intensity light signals that can be switched by a control beam at the second-harmonic frequency. The interaction process is an input-phase-independent parametric downconversion. The theoretical problem is solved through implementation of a dummy variable method optimized for a parametric process. Efficient amplification and bistability of low-intensity signals have been found.     

On structuring proof search for first order linear logic

Full first-order linear logic can be presented as an abstract logic programming language in Miller's system Forum, which yields a sensible operational interpretation in the ‘proof search as computation’ paradigm. However, Forum still has to deal with syntactic details that would normally be ignored by a reasonable operational semantics. In this respect, Forum improves on Gentzen systems for linear logic by restricting the language and the form of inference rules. We further improve on Forum by restricting the class of formulae allowed, in a system we call G-Forum, which is still equivalent to full first-order linear logic. The only formulae allowed in G-Forum have the same shape as Forum sequents: the restriction does not diminish expressiveness and makes G-Forum amenable to proof theoretic analysis. G-Forum consists of two (big) inference rules, for which we show a cut elimination procedure. This does not need to appeal to finer detail in formulae and sequents than is provided by G-Forum, thus successfully testing the internal symmetries of our system.