Near-Surface Interface Detection for Coal Mining Applications Using Bispectral Features and GPR

The use of ground penetrating radar (GPR) for detecting the presence of near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo from the near-surface interface is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques such as background subtraction largely ineffective in the unsupervised case. As a solution to this detection problem, we develop a novel pattern recognition-based algorithm which utilizes a neural network to classify features derived from the bispectrum of 1D early time radar data. The binary classifier is used to decide between two key cases, namely whether an interface is within, for example, 5 cm of the surface or not. This go/no-go detection capability is highly valuable for underground coal mining operations, such as longwall mining, where the need to leave a remnant coal section is essential for geological stability. The classifier was trained and tested using real GPR data with ground truth measurements. The real data was acquired from a testbed with coal-clay, coal-shale and shale-clay interfaces, which represents a test mine site. We show that, unlike traditional second order correlation based methods such as matched filtering which can fail even in known conditions, the new method reliably allows the detection of interfaces using GPR to be applied in the near-surface region. In this work, we are not addressing the problem of depth estimation, rather confining ourselves to detecting an interface within a particular depth range

SPIT IN MY MOUTH: Queer Intimacies, Material Intra-actions, and Sensuous Becoming

This document describes my multidisciplinary art practice as it intersects with New Materialism, Queer and Affect theory, Ecology, and my embodied and experiential knowledge as a queer subject. The writing is divided into two categories. One is more theoretical, thinking through these different discourses. The other realizes them through relationships and intra-actions between my material kin and me. With these two modes of writing,I propose that embodied and felt knowing is as valid and illuminating as more traditional forms of knowledge. These sections are interdependent and resist linear logic, offering relational meanings to each reader as they find their way through a terrain of text and image offering a multiplicity of readings. Renaming difficulties with articulation as a legitimate tension within my own way of thinking and experiencing, this document pushes against such exactitude of ideas. Ultimately the artworks in my thesis exhibition and this outlining document work to reveal queerness, or queering, as a basic tenet for existence. This text uses Open Dyslexic, an open-source font designed for readers with dyslexia, a learning difference whose wide range of effects alters the way a person takes in, processes, and utilizes language and graphic symbols

Elevating microRNA-122 in blood improves outcomes after temporary middle cerebral artery occlusion in rats.

Because our recent studies have demonstrated that miR-122 decreased in whole blood of patients and in whole blood of rats following ischemic stroke, we tested whether elevating blood miR-122 would improve stroke outcomes in rats. Young adult rats were subjected to a temporary middle cerebral artery occlusion (MCAO) or sham operation. A polyethylene glycol-liposome-based transfection system was used to administer a miR-122 mimic after MCAO. Neurological deficits, brain infarction, brain vessel integrity, adhesion molecule expression and expression of miR-122 target and indirect-target genes were examined in blood at 24 h after MCAO with or without miR-122 treatment. miR-122 decreased in blood after MCAO, whereas miR-122 mimic elevated miR-122 in blood 24 h after MCAO. Intravenous but not intracerebroventricular injection of miR-122 mimic decreased neurological deficits and brain infarction, attenuated ICAM-1 expression, and maintained vessel integrity after MCAO. The miR-122 mimic also down-regulated direct target genes (e.g. Vcam1, Nos2, Pla2g2a) and indirect target genes (e.g. Alox5, Itga2b, Timp3, Il1b, Il2, Mmp8) in blood after MCAO which are predicted to affect cell adhesion, diapedesis, leukocyte extravasation, eicosanoid and atherosclerosis signaling. The data show that elevating miR-122 improves stroke outcomes and we postulate this occurs via downregulating miR-122 target genes in blood leukocytes

A Dynamic Neural Network Architecture with immunology Inspired Optimization for Weather Data Forecasting

Recurrent neural networks are dynamical systems that provide for memory capabilities to recall past behaviour, which is necessary in the prediction of time series. In this paper, a novel neural network architecture inspired by the immune algorithm is presented and used in the forecasting of naturally occurring signals, including weather big data signals. Big Data Analysis is a major research frontier, which attracts extensive attention from academia, industry and government, particularly in the context of handling issues related to complex dynamics due to changing weather conditions. Recently, extensive deployment of IoT, sensors, and ambient intelligence systems led to an exponential growth of data in the climate domain. In this study, we concentrate on the analysis of big weather data by using the Dynamic Self Organized Neural Network Inspired by the Immune Algorithm. The learning strategy of the network focuses on the local properties of the signal using a self-organised hidden layer inspired by the immune algorithm, while the recurrent links of the network aim at recalling previously observed signal patterns. The proposed network exhibits improved performance when compared to the feedforward multilayer neural network and state-of-the-art recurrent networks, e.g., the Elman and the Jordan networks. Three non-linear and non-stationary weather signals are used in our experiments. Firstly, the signals are transformed into stationary, followed by 5-steps ahead prediction. Improvements in the prediction results are observed with respect to the mean value of the error (RMS) and the signal to noise ratio (SNR), however to the expense of additional computational complexity, due to presence of recurrent links

Maternal immune activation disrupts dopamine system in the offspring

Background: In utero exposure to maternal viral infections is associated with a higher incidence of psychiatric disorders with a supposed neurodevelopmental origin, including schizophrenia. Hence, immune response factors exert a negative impact on brain maturation that predisposes the offspring to the emergence of pathological phenotypes later in life. Although ventral tegmental area dopamine neurons and their target regions play essential roles in the pathophysiology of psychoses, it remains to be fully elucidated how dopamine activity and functionality are disrupted in maternal immune activation models of schizophrenia. Methods: Here, we used an immune-mediated neurodevelopmental disruption model based on prenatal administration of the polyriboinosinic-polyribocytidilic acid in rats, which mimics a viral infection and recapitulates behavioral abnormalities relevant to psychiatric disorders in the offspring. Extracellular dopamine levels were measured by brain microdialysis in both the nucleus accumbens shell and the medial prefrontal cortex, whereas dopamine neurons in ventral tegmental area were studied by in vivo electrophysiology. Results: Polyriboinosinic-polyribocytidilic acid-treated animals, at adulthood, displayed deficits in sensorimotor gating, memory, and social interaction and increased baseline extracellular dopamine levels in the nucleus accumbens, but not in the prefrontal cortex. In polyriboinosinic-polyribocytidilic acid rats, dopamine neurons showed reduced spontaneously firing rate and population activity. Conclusions: These results confirm that maternal immune activation severely impairs dopamine system and that the polyriboinosinic-polyribocytidilic acid model can be considered a proper animal model of a psychiatric condition that fulfills a multidimensional set of validity criteria predictive of a human patholog

Analysis of tick-borne encephalitis virus-induced host responses in human cells of neuronal origin and interferon-mediated protection

Tick-borne encephalitis virus (TBEV) is a member of the genus Flavivirus. It can cause serious infections in humans that may result in encephalitis/meningoencephalitis. Although several studies have described the involvement of specific genes in the host response to TBEV infection in the central nervous system (CNS), the overall network remains poorly characterized. Therefore, we investigated the response of DAOY cells (human medulloblastoma cells derived from cerebellar neurons) to TBEV (Neudoerfl strain, Western subtype) infection to characterize differentially expressed genes by transcriptome analysis. Our results revealed a wide panel of interferon-stimulated genes (ISGs) and pro-inflammatory cytokines, including type III but not type I (or II) interferons (IFNs), which are activated upon TBEV infection, as well as a number of non-coding RNAs, including long non-coding RNAs. To obtain a broader view of the pathways responsible for eliciting an antiviral state in DAOY cells we examined the effect of type I and III IFNs and found that only type I IFN pre-treatment inhibited TBEV production. The cellular response to TBEV showed only partial overlap with gene expression changes induced by IFN-β treatment – suggesting a virus-specific signature – and we identified a group of ISGs that were highly up-regulated following IFN-β treatment. Moreover, a high rate of down-regulation was observed for a wide panel of pro-inflammatory cytokines upon IFN-β treatment. These data can serve as the basis for further studies of host–TBEV interactions and the identification of ISGs and/or lncRNAs with potent antiviral effects in cases of TBEV infection in human neuronal cells

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested