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This paper describes the research for implementing a commercial giant magnetoresistive (GMR) sensor for detection of magnetic nanoparticles with potential applications in lab on a chip (LOC) device or for analysis of waste water. Micromagnetic simulations are performed to illustrate the behaviour of the detection system. The experimental setup focuses on the detection of polyethylene glycol (PEG6000) functionalized magnetic nanoparticles, commonly used in biosensors and LOC devices. Practical solutions for improving the GMR sensor measurement setup are detailed and discussed. From the experimental measurements we are able to detect a mass approximately 1.20 μg of pure maghemite cores which corresponds to a magnetic moment of approximately 9.098∙10-5 emu for a signal variation of 0.035 V. (in this case, a detection sensitivity of about 75.81 emu/g). Emphasis is placed on advantages in terms of setup sensitivity, flexibility and integration.

UV-nanoimprint lithography is currently seen like an alternative to the classical lithographic techniques (electron beam or optical lithography) for large scale patterning in industrial applications. The present study is oriented on the fabrication of silicon nano-cones (pyramids) by using the UV-nanoimprint technique with polymeric stamps and deep reactive ion etching using the UV-cured resist as etching mask at cryogenic temperatures. The results show that the resist can act successfully as etching mask for the cryogenic silicon etching process.

In this work is studied the possibility to integrate a Fresnel lens array in a compact infrared spectrometer with the scope of focusing and also spectral filtering the incoming radiation, by using their inherent wavelength dependence. From the simulations results obtained with Beam propagation Method one can obtain a 3X3 array formed of two-level Fresnel lenses presenting good results regarding the focusing efficiency and the spectral response. The lens array have been fabricated by DRIE with good results regarding the etch depth control and the snoothness of the etched surfaces.

In this work we investigated the influence of the carbonic materials Single Layer Graphene, Nanocrystalline Graphite and Graphene Nano Walls on the flow field in a microchannel using the experimental technique micro-Particle Image Velocimetry. The velocity and vorticity distributions show that the presence in the microchannel of the carbonic materials have an important influence and above 300 mbar inlet pressure the average velocity decreases in the NCG and GNW cases compared with SLG.

In this paper we present two algorithms that can be used for EATS (Extractive Automatic Text Summarization) of theological New Testament exegesis texts. Both algorithms represent input text as sentence embeddings (i.e. they do not rely on the classical bag-of-words TF-IDF model), the first algorithm is based on graph clustering and the other one is an evolutionary algorithm. In the graph-based algorithm, the sentence embeddings are represented as vertices in a graph, then the graph is clusterized and the best sentence from each cluster is extracted as candidate for the text summary. In the second algorithm, the sentences from the initial text are represented in a binary model and they are evolved based on a fitness function that encompasses various desired qualities of the text summary. We have tested both algorithms on a theological texts dataset, but also on general, standard, dataset DUC2002 and we have obtained good ROUGE scores.

In this work we studied the physico-chemical and antibacterial properties of silver nanoparticles (AgNPs) synthesized by green synthesis. Silver nanoparticles were obtained using as a precursor silver nitrate and sea buckthorn fruit aqueous extract as a reducing agent.

Over the years several methods were used to separate cells within a microfluidic device such as cavities, with vortex microfluidics method, or cell trapping mechanisms using size filters. However, one of the methods that is widely used to separate cancer cells from blood cells is the dielectrophoresis. In this work we employ the numerical code COMSOL Multiphysics for a dielectrophoretic separation of CTCs (circulating tumor cells) from RBCs (red blood cells) in a 3D microchannel. The microfluidic device is composed of one inlet and three outlets. The non-uniform alternative electric field is generated using interdigitated electrodes and it separates the cells within the device to the corresponding outlet. The electric field frequency was set to 100kHz. For this paper, one single CTC line is selected MDA-MB-231, breast cancer cells. The CTCs and the RBCs are considered in an ideal way in the simulation as spheres, but with the afferent characteristics. This work can be extended to testing multiple CTC lines at various flow velocities and electric field frequencies.

In the last years, there has been a big progress regarding the development of graphene-based microfluidics. Based on the excellent biocompatible properties of graphene and derivatives, a large variety of microfluidic devices with graphene integrated are used in the medical field. They are used for detection of viruses that lead to different diseases, drug delivery or for cell analyses, detection of protein and glucose, detection of contaminants, application of sensors or material preparation, gene structure and function, DNA research, cellular research, protein and amino acid research. The purpose of this paper is to show the importance of graphene as a material in fabricating sensors used at microfluidic devices and also to illustrate the costs involved in the production process.

We present the technological processes required to fabricate silicon Fresnel lenses for long infrared spectral range. The Fresnel lenses operate as image corrector in standard long infrared imaging systems and its optical surface consists of sixteen discrete levels. The Fresnel lenses are fabricated using four photolithographic processes, each process being followed by deep reactive ion etching. The characterization by the white light interferometry reveals that the geometrical parameters of the fabricated Fresnel lenses are in good agreement with the design specification. The atomic force microscopy determinations show a low value of the roughness indicating a high quality of the optical surfaces.

The relative popularity of web sites, as expressed in published rankings, is of fundamental value in many contexts including search, advertising and research. In this paper, we consider the surprisingly challenging problem of generating consistent and reliable web site rankings based on unique visitors per day. We illustrate the challenge this represents using data from three large and independently-sourced Internet user panels. We begin by showing that generating a website ranking based simply on the observed unique daily visitors produces highly inconsistent rankings–even among the most popular sites. To mitigate the problems of bias and measurement error, we introduce a general methodology that identifies “canonical panelists”: an abstract class of user that exhibits consistent behavior across panels. Our definition is based on the epistemological technique of triangulation, which refers to observing the same object from multiple perspectives at the same moment in time. We show that panelists in the canonical class exhibit desirable characteristics including improved persistence. Most significantly, we show that defining a domain’s rank as a function of the aggregate behavior of canonical panelists improves overall alignment of rankings across all three of our panels.

Two-phase embedded cooling has great potential for meeting the increasing thermal management needs of high-heat flux electronics. The use of manifold-microchannels further enhances the effectiveness of embedded cooling while maintaining low pressure drop and pumping power. Previous research has shown that thermodynamic quality drives the thermo-fluid phenomena in the channel, including the prevailing flow regime, the transition to dryout, and the local heat transfer coefficients. The flow phenomena are also strongly dependent on the properties of the working fluid. The current study investigates the performance of FC-72 and R245fa in an enlarged visualization manifold-microchannel, referred to as a "microgap" channel.

Generative models are statistical models that learn a true underlying data distribution from samples using unsupervised learning, aiming to generate new data points with some variation. In this paper, we introduce OpenNIG (Open Neural Image Generator), an open-source neural networks toolkit for image generation. It offers the possibility to easily train, validate and test state of the art models. The framework also contains a module that enables the user to directly download and process some of the most common databases used in deep learning. OpenNIG is freely available via GitHub.

This paper presents a fire detection method based on neural networks used for a Flying Ad-Hoc Network (FANET) that is part of a Public Safety Fog System Architecture. The complete architecture of this solution and the interconnection between all of the components is depicted in this work. A five hundred pictures database was build and TensorFlow from Google was used to model and train the neural network. The performance evaluation of the implemented algorithm is provided and discussed. Based on previous results, the fire detection mechanism is the last required validation step towards a real prototype implementation of the proposed system model.

Bike sharing systems are one of the proposed solutions for personal transportation in cities around the world. Clustering is a principal direction for research done on bike sharing systems with the purpose of reducing computational difficulty of problems associated with operating such systems. This paper proposes a new similarity measure for station clustering based on periods of low bike availability. The method described is tested on real world bike share data obtained for Citi Bike in New York. A time-based validation is done for the proposed algorithm.