On‐Chip Magnetic Platform for Single‐Particle Manipulation with Integrated Electrical Feedback

Methods for the manipulation of single magnetic particles have become very interesting, in particular for in vitro biological studies. Most of these studies require an external microscope to provide the operator with feedback for controlling the particle motion, thus preventing the use of magnetic particles in high‐throughput experiments. In this paper, a simple and compact system with integrated electrical feedback is presented, implementing in the very same device both the manipulation and detection of the transit of single particles. The proposed platform is based on zig‐zag shaped magnetic nanostructures, where transverse magnetic domain walls are pinned at the corners and attract magnetic particles in suspension. By applying suitable external magnetic fields, the domain walls move to the nearest corner, thus causing the step by step displacement of the particles along the nanostructure. The very same structure is also employed for detecting the bead transit. Indeed, the presence of the magnetic particle in suspension over the domain wall affects the depinning field required for its displacement. This characteristic field can be monitored through anisotropic magnetoresistance measurements, thus implementing an integrated electrical feedback of the bead transit. In particular, the individual manipulation and detection of single 1‐μm sized beads is demonstrated.     

Simulated and measured X‐band radar reflectivity of land in mountainous terrain using a fan‐beam antenna

A computer program for meteorological radar siting, previously developed for pencil‐beam antenna, long‐range, C‐band radars, has been adapted for fan‐beam antenna, short‐range, X‐band radars. The simulator uses topographic information in the form of a raster digital elevation model and a surface backscattering cross‐section per unit area at grazing angles derived from the literature. It is independent of specific radar sites and radar systems. Its use will enhance optimized scanning strategies, antenna design tailored to mountainous terrain and knowledge of land‐clutter measurements at low grazing angles. The raster‐based approach used in implementing the program makes it compatible with any desired spatial resolution. Simplicity and short simulation times have been pursued as primary goals during the planning and implementation phases of the routines. A preliminary validation of the simulator using backscattering data acquired in the Alps is presented in this letter.     

Discrimination of vegetation types in alpine sites with ALOS PALSAR-, RADARSAT-2-, and lidar-derived information

Natural vegetation monitoring in the alpine mountain range is a priority in the European Union in view of climate change effects. Many potential monitoring tools, based on advanced remote sensing sensors, are still not fully integrated in operational activities, such as those exploiting very high-resolution synthetic aperture radar (SAR) or light detection and ranging (lidar) data. Their testing is important for possible incorporation in routine monitoring and to increase the quantity and quality of environmental information. In this study the potential of ALOS PALSAR and RADARSAT-2 SAR scenes' synergic use for discrimination of different vegetation types was tested in an alpine heterogeneous and fragmented landscape. The integration of a lidar-based canopy height model (CHM) with SAR data was also tested. A SPOT image was used as a benchmark to evaluate the results obtained with different input data. Discrimination of vegetation types was performed with maximum likelihood classification and neural networks. Six tested data combinations obtained more than 85% overall accuracy, and the most complex input which integrates the two SARs with lidar CHM outperformed the result based on SPOT. Neural network algorithms provided the best results. This study highlights the advantages of integrating SAR sensors with lidar CHM for vegetation monitoring in a changing environment.     

The New Mind: thinking beyond the head

Throughout much of the modern period, the human mind has been regarded as a property of the brain and therefore something confined to the inside of the head—a view commonly known as ‘internalism’. But recent works in cognitive science, philosophy, and anthropology, as well as certain trends in the development of technology, suggest an emerging view of the mind as a process not confined to the brain but spread through the body and world—an outlook covered by a family of views labelled ‘externalism’. In this paper, we will suggest there is now sufficient momentum in favour of externalism of various kinds to mark a historical shift in the way the mind is understood. We dub this emerging externalist tendency the ‘New Mind’. Key properties of the New Mind will be summarised and some of its implications considered in areas such as art and culture, technology, and the science of consciousness.     

Decorrelation of quantized signals induced by filtering and requantization

In Very Long Baseline Interferometry, signals from far radio sources are simultaneously recorded at different antennas, with the purpose of investigating their physical properties. The recorded signals are generally modeled as realizations of Gaussian processes, whose power is dominated by the system noise at the receiving antennas. The actual signal coming from the radio source can be detected only after cross-correlation of the various data-streams. The signals received at each antenna are digitized after low noise amplification and frequency down-conversion, in order to allow subsequent digital post-processing. The applied quantization is coarse, 1 or 2 bits being generally associated to the signal amplitude. In modern applications the sampling is typically performed at a high rate, and subchannels are then generated by filtering, followed by decimation and requantization of the signal streams. The redigitized streams are then cross-correlated to extract the physical observables. While the classical effect of quantization has widely been studied in the past, the decorrelation induced by the filtering and requantization process is still characterized experimentally, mainly due to its inherent mathematical complexity. In the present work we analyze the above problem, and provide algorithms and analytical formulas aimed at predicting the induced decorrelation for a wide class of quantization schemes, with the unique assumption of weakly correlated signals, typically fulfilled in VLBI and radio astronomy applications.     

Schedulability analysis in hard real-time systems under thermal constraints

In this paper, we study thermal-constrained hard real-time systems, where real-time guarantees must be met without exceeding safe temperature levels within the processor. Dynamic speed scaling is one of the major techniques to manage power so as to maintain safe temperature levels. As example, we adopt a reactive speed control technique in our work. We design an extended busy-period analysis methodology to perform schedulability analysis for general task arrivals under reactive speed control with First-In-First-Out (FIFO), Static-Priority (SP), and Earliest-Deadline-First (EDF) scheduling. As a special case, we obtain a closed-form formula for the worst-case response time of jobs under the leaky-bucket task arrival model. Our data show how reactive speed control can decrease the worst-case response time of tasks in comparison with any constant-speed scheme.     

From global to local and viceversa: uses of associative rule learning for classification in imprecise environments

We propose two models for improving the performance of rule-based classification under unbalanced and highly imprecise domains. Both models are probabilistic frameworks aimed to boost the performance of basic rule-based classifiers. The first model implements a global-to-local scheme, where the response of a global rule-based classifier is refined by performing a probabilistic analysis of the coverage of its rules. In particular, the coverage of the individual rules is used to learn local probabilistic models, which ultimately refine the predictions from the corresponding rules of the global classifier. The second model implements a dual local-to-global strategy, in which single classification rules are combined within an exponential probabilistic model in order to boost the overall performance as a side effect of mutual influence. Several variants of the basic ideas are studied, and their performances are thoroughly evaluated and compared with state-of-the-art algorithms on standard benchmark datasets.     

Synthesis of 8-hydroxyquinoline functionalised DO3A ligand and Eu(III) and Er(III) complexes: Luminescence properties

The synthesis of a new DO3A-based macrocyclic ligand bearing a 8-hydroxyquinoline residue together with the preparation of its Eu³⁺ and Er³⁺ neutral complexes are described. In a previous report [F. Rizzo, A. Papagni, F. Meinardi, R. Tubino, M. Ottonelli, G.F. Musso, G. Dellepiane, Synth. Met. 147 (2004) 143], we have shown that lanthanide complexes display very high stability combined with a good luminescence in aqueous solution under UV radiation, which indicate an energy transfer process from the excited 8-hydroxyquinoline moiety to the metal. In this work, we correlate the ability to transfer the energy from the sensitizer to lanthanide ion with pH behaviour of the antenna. Furthermore, the variation of pH in Eu³⁺ complex supports the hypothesis of presence of charge-transfer transitions. The good solubility and sensitized emission in different solvents (organic and water) are very important aspects for their technologic applications as luminescent probes or NIR-emitting devices.     

Towards single atom analysis of biological structures.

Mapping single atoms in biological structures is now becoming within the reach of analytical electron microscopy. Electron energy-loss spectroscopy (EELS) in the field-emission scanning transmission electron microscope (STEM) provides a particularly high sensitivity for detecting the biologically important element, phosphorus. Imaging can be performed at low dose with dark-field STEM prior to analysis at high dose, so that structures of macromolecular assemblies can be correlated with the numbers of specific atoms that they contain. Measurements confirm theoretical predictions that single atom detection requires a nanometer-sized probe. Although phosphorus atoms may have moved several nanometers from their original positions by beam-induced structural degradation at the high required dose of approximately 10(9) e/nm2, damaged molecules are nevertheless stable enough to be analyzed at 1 or 2 nm resolution. Such analyses can only be achieved by means of spectrum-imaging with correction for specimen drift. Optimal strategies for mapping small numbers of phosphorus atoms have been investigated using well-characterized specimens of DNA plasmids and tobacco mosaic virus.