PV Cell Characteristic Extraction to Verify Power Transfer Efficiency in Indoor Harvesting System

A method is proposed to verify the efficiency of low-power harvesting systems based on Photovoltaic (PV) cells for indoor applications and a Fractional Open-Circuit Voltage (FOCV) technique to track the Maximum Power Point (MPP). It relies on an algorithm to reconstruct the PV cell Power versus Voltage (P-V) characteristic measuring the open circuit voltage and the voltage/current operating point but not the short-circuit current as required by state-of-the-art algorithms. This way the characteristic is reconstructed starting from the two values corresponding to standard operation modes of dc-dc converters implementing the FOCV Maximum Power Point Tracking (MPPT) technique. The method is applied to a prototype system: an external board is connected between the transducer and the dc-dc converter to measure the open circuit voltage and the voltage/current operating values. Experimental comparisons between the reconstructed and the measured P-V characteristics validate the reconstruction algorithm. Experimental results show the method is able to clearly identify the error between the transducer operating point and the one corresponding to the maximum power transfer, whilst also suggesting corrective action on the programmable factor of the FOCV technique. The proposed technique therefore provides a possible way of estimating MPPT efficiency without sampling the full P-V characteristic

Analysis of infected human mononuclear cells by atomic force microscopy

The surfaces of the human lymphoid cells of the line H9 chronically infected with the Human Immunodeficiency Virus HIV-1, and of human monocytes acutely infected in vitro with Mycobacterium Tuberculosis (MTB) were dried, fixed and imaged with atomic force microscopy (AFM). These images were compared with those of non-infected samples. Dried and fixed samples of infected cells can be distinguished from non-infected ones by AFM technology due to their different surface structures and by the presence of pathogenic (viz al or mycobacterial) agents on the cell surface

Vaccination against Clostridium difficile using toxin fragments: Observations and analysis in animal models

Clostridium difficile is a major cause of antibiotic associated diarrhea. Recently, we have shown that effective protection can be mediated in hamsters through the inclusion of specific recombinant fragments from toxin A and B in a systemically delivered vaccine. Interestingly while neutralizing antibodies to the binding domains of both toxin A and B are moderately protective, enhanced survival is observed when fragments from the glucosyltransferase region of toxin B replace those from the binding domain of this toxin. In this addendum, we discuss additional information that has been derived from such vaccination studies. This includes observations on efficacy and cross-protection against different ribotypes mediated by these vaccines and the challenges that remain for a vaccine which prevents clinical symptoms but not colonization. The use and value of vaccination both in the prevention of infection and for treatment of disease relapse will be discussed

Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure

A gated oscillator clock and data recovery circuit for nanowatt wake-up and data receivers

This article presents a data-startable baseband logic featuring a gated oscillator clock and data recovery (GO-CDR) circuit for nanowatt wake-up and data receivers (WuRxs). At each data transition, the phase misalignment between the data coming from the analog front-end (AFE) and the clock is cleared by the GO-CDR circuit, thus allowing the reception of long data streams. Any free-running frequency mismatch between the GO and the bitrate does not limit the number of receivable bits, but only the maximum number of equal consecutive bits (Nm). To overcome this limitation, the proposed system includes a frequency calibration circuit, which reduces the frequency mismatch to ±0.5%, thus enabling the WuRx to be used with different encoding techniques up to Nm = 100. A full WuRx prototype, including an always-on clockless AFE operating in subthreshold, was fabricated with STMicroelectronics 90 nm BCD technology. The WuRx is supplied with 0.6 V, and the power consumption, excluding the calibration circuit, is 12.8 nW during the rest state and 17 nW at a 1 kbps data rate. With a 1 kbps On-Off Keying (OOK) modulated input and −35 dBm of input RF power after the input matching network (IMN), a 10^(−3) missed detection rate with a 0 bit error tolerance is measured, transmitting 63 bit packets with the Nm ranging from 1 to 63. The total sensitivity, including the estimated IMN gain at 100 MHz and 433 MHz, is −59.8 dBm and −52.3 dBm, respectively. In comparison with an ideal CDR, the degradation of the sensitivity due to the GO-CDR is 1.25 dBm. False alarm rate measurements lasting 24 h revealed zero overall false wake-ups

An 8 bit current steering DAC for offset compensation purposes in sensor arrays

Abstract. An 8 bit segmented current steering DAC is presented for the compensation of mismatch of sensors with current output arranged in a large arrays. The DAC is implemented in a 1.8 V supply voltage 180 nm standard CMOS technology. Post layout simulations reveal that the design target concerning a sampling frequency of 2.6 MHz is exceeded, worst-case settling time equals 60.6 ns. The output current range is 0–10 μA, which translates into an LSB of 40 nA. Good linearity is achieved, INL < 0.5 LSB and DNL < 0.4 LSB, respectively. Static power consumption with the outputs operated at a voltage of 0.9 V is approximately 10 μW. Dynamic power, mainly consumed by switching activity of the digital circuit parts, amounts to 100 μW at 2.6 MHz operation frequency. Total area is 38.6 × 2933.0 μm2

A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks

Graph neural networks (GNNs) have known an increasing success recently, with many GNN variants achieving state-of-the-art results on node and graph classification tasks. The proposed GNNs, however, often implement complex node and graph embedding schemes, which makes challenging to explain their performance. In this paper, we investigate the link between a GNN's expressiveness, that is, its ability to map different graphs to different representations, and its generalization performance in a graph classification setting. In particular , we propose a principled experimental procedure where we (i) define a practical measure for expressiveness, (ii) introduce an expressiveness-based loss function that we use to train a simple yet practical GNN that is permutation-invariant, (iii) illustrate our procedure on benchmark graph classification problems and on an original real-world application. Our results reveal that expressiveness alone does not guarantee a better performance, and that a powerful GNN should be able to produce graph representations that are well separated with respect to the class of the corresponding graphs

The factor H binding protein of Neisseria meningitidis interacts with xenosiderophores in vitro.

The factor H binding protein (fHbp) is a key virulence factor of Neisseria meningitidis that confers to the bacterium the ability to resist killing by human serum. The determination of its three-dimensional structure revealed that the carboxyl terminus of the protein folds into an eight-stranded ߠbarrel. The structural similarity of this part of the protein to lipocalins provided the rationale for exploring the ability of fHbp to bind siderophores. We found that fHbp was able to bind in vitro siderophores belonging to the cathecolate family and mapped the interaction site by nuclear magnetic resonance. Our results indicated that the enterobactin binding site was distinct from the site involved in binding to human factor H and stimulates new hypotheses about possible multiple activities of fHbp.Full Tex