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Chemical detection systems based on chemo-resistive sensors usually include a gas chamber to control the sample air flow and to minimize turbulence. However, such a kind of experimental setup does not reproduce the gas concentration fluctuations observed in natural environments and destroys the spatio-temporal information contained in gas plumes. Aiming at reproducing more realistic environments, we utilize a wind tunnel with two independent gas sources that get naturally mixed along a turbulent flow. For the first time, chemo-resistive gas sensors are exposed to dynamic gas mixtures generated with several concentration levels at the sources. Moreover, the ground truth of gas concentrations at the sensor location was estimated by means of gas chromatography-mass spectrometry. We used a support vector machine as a tool to show that chemo-resistive transduction can be utilized to reliably identify chemical components in dynamic turbulent mixtures, as long as sufficient gas concentration coverage is used. We show that in open sampling systems, training the classifiers only on high concentrations of gases produces less effective classification and that it is important to calibrate the classification method with data at low gas concentrations to achieve optimal performance.

We address the problem of detecting changes in the activity of a distant gas source from the response of an array of metal oxide (MOX) gas sensors deployed in an open sampling system. The main challenge is the turbulent nature of gas dispersion and the response dynamics of the sensors. We propose a change point detection approach and evaluate it on individual gas sensors in an experimental setup where a gas source changes in intensity, compound, or mixture ratio. We also introduce an efficient sensor selection algorithm and evaluate the change point detection approach with the selected sensor array subsets.

In this paper, we address the task of gas distribution modeling in scenarios where multiple heterogeneous compounds are present. Gas distribution modeling is particularly useful in emission monitoring applications where spatial representations of the gaseous patches can be used to identify emission hot spots. In realistic environments, the presence of multiple chemicals is expected and therefore, gas discrimination has to be incorporated in the modeling process. The approach presented in this work addresses the task of gas distribution modeling by combining different non selective gas sensors. Gas discrimination is addressed with an open sampling system, composed by an array of metal oxide sensors and a probabilistic algorithm tailored to uncontrolled environments. For each of the identified compounds, the mapping algorithm generates a calibrated gas distribution model using the classification uncertainty and the concentration readings acquired with a photo ionization detector. The meta parameters of the proposed modeling algorithm are automatically learned from the data. The approach was validated with a gas sensitive robot patrolling outdoor and indoor scenarios, where two different chemicals were released simultaneously. The experimental results show that the generated multi compound maps can be used to accurately predict the location of emitting gas sources.

The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions.

In this paper, we introduce a Bayesian time series model approach for gas concentration estimation using Metal Oxide (MOX) sensors in Open Sampling System (OSS). Our approach focuses on the compensation of the slow response of MOX sensors, while concurrently solving the problem of estimating the gas concentration in OSS. The proposed Augmented Switching Linear System model allows to include all the sources of uncertainty arising at each step of the problem in a single coherent probabilistic formulation. In particular, the problem of detecting on-line the current sensor dynamical regime and estimating the underlying gas concentration under environmental disturbances and noisy measurements is formulated and solved as a statistical inference problem. Our model improves, with respect to the state of the art, where system modeling approaches have been already introduced, but only provided an indirect relative measures proportional to the gas concentration and the problem of modeling uncertainty was ignored. Our approach is validated experimentally and the performances in terms of speed of and quality of the gas concentration estimation are compared with the ones obtained using a photo-ionization detector.

Many applications of metal oxide gas sensors can benefit from reliable algorithms to detect significant changes in the sensor response. Significant changes indicate a change in the emission modality of a distant gas source and occur due to a sudden change of concentration or exposure to a different compound. As a consequence of turbulent gas transport and the relatively slow response and recovery times of metal oxide sensors, their response in open sampling configuration exhibits strong fluctuations that interfere with the changes of interest. In this paper we introduce TREFEX, a novel change point detection algorithm, especially designed for metal oxide gas sensors in an open sampling system. TREFEX models the response of MOX sensors as a piecewise exponential signal and considers the junctions between consecutive exponentials as change points. We formulate non-linear trend filtering and change point detection as a parameter-free convex optimization problem for single sensors and sensor arrays. We evaluate the performance of the TREFEX algorithm experimentally for different metal oxide sensors and several gas emission profiles. A comparison with the previously proposed GLR method shows a clearly superior performance of the TREFEX algorithm both in detection performance and in estimating the change time.

Dynamic multi-relational graphs are an expressive relational representation for data enclosing entities and relations of different types, and where relationships are allowed to vary in time. Addressing predictive tasks over such data requires the ability to find structure embeddings that capture the diversity of the relationships involved, as well as their dynamic evolution. In this work, we establish a novel class of challenging tasks for dynamic multi-relational graphs involving out-of-domain link prediction, where the relationship being predicted is not available in the input graph. We then introduce a novel Graph Neural Network model, named GOOD, designed specifically to tackle the out-of-domain generalization problem. GOOD introduces a novel design concept for multi-relation embedding aggregation, based on the idea that good representations are such when it is possible to disentangle the mixing proportions of the different relational embeddings that have produced it. We also propose five benchmarks based on two retail domains, where we show that GOOD can effectively generalize predictions out of known relationship types and achieve state-of-the-art results. Most importantly, we provide insights into problems where out-of-domain prediction might be preferred to an in-domain formulation, that is, where the relationship to be predicted has very few positive examples.

Global retailers have assortments that contain hundreds of thousands of products that can be linked by several types of relationships like style compatibility, \"bought together\", \"watched together\", etc. Graphs are a natural representation for assortments, where products are nodes and relations are edges. Relations like style compatibility are often produced by a manual process and therefore do not cover uniformly the whole graph. We propose to use inductive learning to enhance a graph encoding style compatibility of a fashion assortment, leveraging rich node information comprising textual descriptions and visual data. Then, we show how the proposed graph enhancement improves substantially the performance on transductive tasks with a minor impact on graph sparsity.

Background Adult mesenchymal stem cells (MSCs) play a crucial role in the maintenance of tissue homeostasis and in regenerative processes. Among the different MSC types, the gingiva-derived mesenchymal stem cells (GMSCs) have arisen as a promising tool to promote the repair of damaged tissues secreting trophic mediators that affect different types of cells involved in regenerative processes. Tumour necrosis factor (TNF)-α is one of the key mediators of inflammation that could affect tissue regenerative processes and modify the MSC properties in in-vitro applications. To date, no data have been reported on the effects of TNF-α on GMSC trophic activities and how its modulation with anti-inflammatory agents from natural sources could modulate the GMSC properties. Methods GMSCs were isolated and characterized from healthy subjects. The effects of TNF-α were evaluated on GMSCs and on the well-being of endothelial cells. The secretion of cytokines was measured and related to the modification of GMSC-endothelial cell communication using a conditioned-medium method. The ability to modify the inflammatory response was evaluated in the presence of Ribes nigrum bud extract (RBE). Results TNF-α differently affected GMSC proliferation and the expression of inflammatory-related proteins (interleukin (IL)-6, IL-10, transforming growth factor (TGF)-β, and cyclooxygenase (COX)-2) dependent on its concentration. A high TNF-α concentration decreased the GMSC viability and impaired the positive cross-talk between GMSCs and endothelial cells, probably by enhancing the amount of pro-inflammatory cytokines in the GMSC secretome. RBE restored the beneficial effects of GMSCs on endothelial viability and motility under inflammatory conditions. Conclusions A high TNF-α concentration decreased the well-being of GMSCs, modifying their trophic activities and decreasing endothelial cell healing. These data highlight the importance of controlling TNF-α concentrations to maintain the trophic activity of GMSCs. Furthermore, the use of natural anti-inflammatory agents restored the regenerative properties of GMSCs on endothelial cells, opening the way to the use and development of natural extracts in wound healing, periodontal regeneration, and tissue-engineering applications that use MSCs.

Adult mesenchymal stem cells (MSCs) play a crucial role in the maintenance of tissue homeostasis and in regenerative processes. Among the different MSC types, the gingiva-derived mesenchymal stem cells (GMSCs) have arisen as a promising tool to promote the repair of damaged tissues secreting trophic mediators that affect different types of cells involved in regenerative processes. Tumour necrosis factor (TNF)-[alpha] is one of the key mediators of inflammation that could affect tissue regenerative processes and modify the MSC properties in in-vitro applications. To date, no data have been reported on the effects of TNF-[alpha] on GMSC trophic activities and how its modulation with anti-inflammatory agents from natural sources could modulate the GMSC properties. GMSCs were isolated and characterized from healthy subjects. The effects of TNF-[alpha] were evaluated on GMSCs and on the well-being of endothelial cells. The secretion of cytokines was measured and related to the modification of GMSC-endothelial cell communication using a conditioned-medium method. The ability to modify the inflammatory response was evaluated in the presence of Ribes nigrum bud extract (RBE). TNF-[alpha] differently affected GMSC proliferation and the expression of inflammatory-related proteins (interleukin (IL)-6, IL-10, transforming growth factor (TGF)-[beta], and cyclooxygenase (COX)-2) dependent on its concentration. A high TNF-[alpha] concentration decreased the GMSC viability and impaired the positive cross-talk between GMSCs and endothelial cells, probably by enhancing the amount of pro-inflammatory cytokines in the GMSC secretome. RBE restored the beneficial effects of GMSCs on endothelial viability and motility under inflammatory conditions. A high TNF-[alpha] concentration decreased the well-being of GMSCs, modifying their trophic activities and decreasing endothelial cell healing. These data highlight the importance of controlling TNF-[alpha] concentrations to maintain the trophic activity of GMSCs. Furthermore, the use of natural anti-inflammatory agents restored the regenerative properties of GMSCs on endothelial cells, opening the way to the use and development of natural extracts in wound healing, periodontal regeneration, and tissue-engineering applications that use MSCs.