gebhart/patrepeval huggingface dataset:
Edge-on semiconductor x-ray detectors - towards high-rate counting computed tomography (2008)

The main obstacle today for photon-counting x-ray computed tomography (CT) in the clinical domain is the large x-ray flux rate employed in commercial CT systems enabling ultra-low scanning times. State-of-the-art direct-conversion semiconductor detectors with dedicated readout ASICs are able to reach rates of up to 5–10 million recorded events per second and channel. The requirement coming from clinical CT would be around one billion primary events per second and square millimeter active detector surface. Decreasing the (transverse) pixel size of typically 1 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for medical CT down-to about 200 μm×200 μm is effective to reduce the count rates per channel. However, for pixel sizes around and below that level, a number of physical effects like K-escape or charge sharing set in with degrading effects on image quality and spectral information content of the transmission data. A possible way to bridge the gap of about one more order of magnitude in count rate is to apply the sub-structuring of the active sensor volume not only in the directions transverse to the x-ray beam but also along the beam. In this paper we present simulations for a so-called “edge-on” detection concept in which the different sensor layers (in different depths) are read out using independent channels. The main observation reported in this paper is that already a very simple concept of data acquisition or data usage suffices to reconstruct high-quality images with a noise increase restricted to the outermost parts of the object under consideration. The count-rate problem is severe only in the primary beam and in “the first few millimeters of object”, as the event rate is exponentially damped inside the patient, drastically reducing the remaining effective count rate per channel.
Can We Speed up 3D Scanning? A Cognitive and Geometric Analysis (2017)

The paper propose a cognitive inspired change detection method for the detection and localization of shape variations on point clouds. A well defined pipeline is introduced by proposing a coarse to fine approach: i) shape segmentation, ii) fine segment registration using attention blocks. Shape segmentation is obtained using covariance based method and fine segment registration is carried out using gravitational registration algorithm. In particular the introduction of this partition-based approach using visual attention mechanism improves the speed of deformation detection and localization. Some results are shown on synthetic data of house and aircraft models. Experimental results shows that this simple yet effective approach designed with an eye to scalability can detect and localize the deformation in a faster manner. A real world car use case is also presented with some preliminary promising results useful for auditing and insurance claim tasks.
Probabilistic Knowledge-Graph based Workflow Recommender for Network Management Automation (2019)

The move towards more complex and dynamic telecommunication networks renders the need for more automation in the management system stringent. This is especially true in the context of 5G networks, where understanding context for providing the right management recommendations is crucial. Our work focuses on gathering context from the current status of the network and correlating it with useful information from existing documents in the network provider and operator's domain. We propose an architecture that, based on collected and interlinked information, can provide automatic recommendations for addressing existing problems in the network. More specifically, our approach is based on creating a knowledge-graph to direct workflow recommendations for network incidents. We implemented this system using the actual OSS product documentation and real network events and we show that our approach adds new value for automated network management, including improved efficiency and customer experience.
Driver Behavior Monitoring Using Tools of Deep Learning and Fuzzy Inferencing (2018)

Distracted driving is the main cause for car accidents. Driver inattention monitoring systems are promising solutions to mitigate this problem. In this paper, we propose a novel driver inattention monitoring system utilizing deep learning and fuzzy logic theory. A driver head pose estimation module is able to determine whether the driver is focusing on his frontal view, while a deep-learning-based distraction recognition module would detect whether the driver is performing a distraction activity. A danger level inference module based on fuzzy logic combines information from the head pose estimation and the distraction recognition modules to infer the danger level in a real-time manner. In the experimental work, a Convolutional Neural Network model is trained on data of high diversity allowing a more robust driver distraction detection compared to the model trained with only data collected by simulation experiments. In addition, we show that the proposed danger level inference strategy is an effective solution to detect dangerous driving situations by providing timely alerts depending on the vehicle speed.
GEOMETRIC LEAST SQUARES FITTING OF CIRCLE AND ELLIPSE (1999)

The least squares fitting of geometric features to given points minimizes the squares sum of error-of-fit in predefined measures. By the geometric fitting, the error distances are defined with the orthogonal, or shortest, distances from the given points to the geometric feature to be fitted. For the geometric fitting of circle and ellipse, robust algorithms are proposed which are based on the coordinate descriptions of the corresponding point on the circle/ellipse for the given point, where the connecting line of the two points is the shortest path from the given point to the circle/ellipse.
Smooth muscle contraction kinetics at different calcium concentrations (1994)

Actin-myosin interaction kinetics of the intact rat portal vein were studied by analyzing force recovery after cessation of force-inhibiting length vibration. The time constant of postvibration force recovery averaged 0.86 +/- 0.04 s during short-term activation (< 12 s), and increased up to 1.59 +/- 0.02 s (cross-bridge downregulation) during sustained activation of more than 10 min. After the depletion of intracellular calcium stores, the depolarized preparation developed maximum force at an extracellular calcium concentration in excess of 50 mM CaCl2. The time constant of postvibration force recovery rose to 12.31 +/- 1.35 s after an activation period of 30 min. These retarded contraction kinetics may be caused either by low activation of the 20-kDa myosin light chain kinase or by high activity of the 20-kDa myosin light chain phosphatase. Addition of the phosphatase inhibitor okadaic acid (10 microM) during high calcium activation decreases the time constant to 8.04 +/- 0.86 s and appears to prevent the distinct retardation of the contraction kinetics. During submaximum activation (2.5 mM CaCl2), the time constant of postvibration force recovery stabilizes at 1.56 +/- 0.07 s, indicating downregulated cross-bridge kinetics, and is unaffected by phosphatase inhibition. For maximum barium activation, instead of calcium, 19.5 mM BaCl2 is required, which produced time constants of postvibration force recovery of 8.38 +/- 0.32 s. The addition of okadaic did not affect contraction kinetics during barium activation. The pronounced retardation of contraction kinetics that was observed after the maximum calcium activation of the previously calcium depleted, depolarized preparation is probably due to high phosphatase activity.(ABSTRACT TRUNCATED AT 250 WORDS)
CollECT: Collaborative Event deteCtion and Tracking in Wireless Heterogeneous Sensor Networks (2006)

Event detection and tracking are attractive research issues in the wireless sensor network (WSN). The paper proposes a fully distributed protocol, CollECT, to event detection and tracking in a Wireless Heterogeneous Sensor Network (WHSN), composed of many kinds of sensors. In CollECT, three major procedures, vicinity triangulation, event determination, and border sensor selection are used to construct the logical triangle in the vicinity of a sensor, to determine the event, and to select the border sensor to identify the event boundary, respectively. The procedures perform repeatedly to both detect and track events. Simulation results demonstrate that CollECT is promising for event detection and tracking due to satisfactory event accuracy and reasonable fitness of border sensors.
The case for ecosocialism (2020)

If there is one thing the recent unprecedented bushfire emergency has proved to us in Australia - and to millions in countries who watched on in horror - is that the climate emergency is not just something to worry about in the future: this is the climate emergency and it is already catastrophic
Thermophotovoltaic Converter Performance for Radioisotope Power Systems (2005)

The development of lightweight, efficient power for emerging NASA missions and recent advances in thermophotovoltaic (TPV) conversion technology have renewed interest in combining radioisotope heat sources with photovoltaic energy conversion for Radioisotope Power Systems (RPS) for spacecraft. TPV power conversion uses advanced materials able to utilize a broader, spectrally tuned range of wavelengths for more efficient power conversion than Si solar cells. Spectral control, through choices of selective radiant emitters, TPV modules, and filters, is key to high‐efficiency operation. This paper describes performance tests of an array of TPV cells with boundary conditions prototypical of an RPS. TPV performance tests were conducted at prototypical array size (≅100 cm2), emitter temperature (1350 K), and heat rejection temperature (300 K). Test hardware included InGaAs TPV cells at 0.60 eV band‐gap, with tandem plasma/interference filters for spectral control. At the target emitter temperature of 1350 K, a conversion efficiency of 19% has been demonstrated for the TPV module. Results are consistent with measured cell efficiency (28%), calculated spectral control efficiency (80%), and calculated thermal efficiency in the optical cavity (90%).
Stability analysis of the uniform motion of electrodynamic bodies (2020)

Abstract We show that when an electrodynamic body is perturbed from a state of uniform motion, it starts to perform fast oscillations, irrespective of the frequency of the perturbation. It has been demonstrated in previous works that the state of uniform motion can be Lyapunov unstable for particles having a prolate geometry with respect to the direction of motion. Here we show that the limit cycle oscillations are destabilized through a Hopf bifurcation as the geometry of the electrodynamic body gradually switches from prolate to oblate. The resulting symmetry breaking of the Lorentz group implies that the principle of inertia only holds on average, suggesting that the default state of matter is not uniform motion, but self-oscillation as well. We propose that the excitability of electrodynamic bodies under external perturbations is at the basis of the wave-particle duality and its related quantum effects.
Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery (2020)

ABSTRACT Development of accurate disease models and discovery of immune-modulating drugs is challenged by the immune system’s highly interconnected and context-dependent nature. Here we apply deep-learning-driven analysis of cellular morphology to develop a scalable “phenomics” platform and demonstrate its ability to identify dose-dependent, high-dimensional relationships among and between immunomodulators, toxins, pathogens, genetic perturbations, and small and large molecules at scale. High-throughput screening on this platform demonstrates rapid identification and triage of hits for TGF-β- and TNF-α-driven phenotypes. We deploy the platform to develop phenotypic models of active SARS-CoV-2 infection and of COVID-19-associated cytokine storm, surfacing compounds with demonstrated clinical benefit and identifying several new candidates for drug repurposing. The presented library of images, deep learning features, and compound screening data from immune profiling and COVID-19 screens serves as a deep resource for immune biology and cellular-model drug discovery with immediate impact on the COVID-19 pandemic.
Isolation and Impact (2013)

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Developments and trends in electrodeposition (2023)

Electrodeposition is a very important technology. Covering inexpensive and widely available base materials with a thin (typically 0.1 to 10 ..mu..m) coating with specific properties extends their use to applications which otherwise would have been prohibitively expensive. Furthermore, the ability to provide property control in coatings as thick as 10 mm adds further value to this important technique of surface modification. In recent years a number of advances in areas such as composite plating, pulse plating and high speed plating have added further credence to the use of electrodeposition as a viable coating technology. This paper will review these branches of the industry and discuss future trends. 29 refs., 1 fig., 4 tabs.
Production of High‐Quality Diesel from Biomass Waste Products (2011)

High-quality liquid fuels are obtained from non-edible carbohydrates by energy-efficient processes. 2-Methylfuran, produced by hydrogenation of furfural, is converted into 6-alkyl undecanes in a catalytic solvent-free process (see scheme with 6-butylundecane). A diesel fuel is produced with an excellent motor cetane number (71) and pour point (−90 °C) and with global process conversions and selectivities close to 90 %. Detailed facts of importance to specialist readers are published as ”Supporting Information”. Such documents are peer-reviewed, but not copy-edited or typeset. They are made available as submitted by the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Determination of load and generation composition in distribution grids (2009)

Due to the permanently increasing portion of distributed energy generation, energy management systems become more important to allow a stable operation of the grid at its limits. Distribution grids are often sparsely monitored and an upgrade of measuring technology is associated with high costs. However, information about load behaviour at the nodes of a grid is essential for grid state identification and load forecasting. In this paper an algorithm is developed which determines load and generation composition on each node of a distribution grid using standardized load and generation profiles. The results of this method allow the creation of time series of nodal power. Thus, it is possible to predict the utilization and the state of a grid more precisely by avoiding investments in additional measurement equipment.
Three dimensionally-ordered 2D MoS<sub>2</sub>vertical layers integrated on flexible substrates with stretch-tunable functionality and improved sensing capability (2018)

The intrinsically anisotropic crystallinity of two-dimensional (2D) transition metal dichalcogenide (2D TMD) layers enables a variety of intriguing material properties which strongly depend on the physical orientation of constituent 2D layers. For instance, 2D TMDs with vertically-aligned layers exhibit numerous dangling bonds on their 2D layer edge sites predominantly exposed on the surface, projecting significantly improved physical and/or chemical adsorption capability compared to their horizontally-oriented 2D layer counterparts. Such property advantages can be further promoted as far as the material can be integrated onto unconventional substrates of tailored geometry/functionality, offering vast opportunities for a wide range of applications which demand enhanced surface area/reactivity and mechanical flexibility. Herein, we report a new form of 2D TMDs, i.e., three-dimensionally ordered 2D molybdenum disulfide (2D MoS2) with vertically-aligned layers integrated on elastomeric substrates and explore their tunable multi-functionalities and technological promise. We grew large-scale (>2 cm2) vertically-aligned 2D MoS2 layers using a three-dimensionally patterned silicon dioxide (SiO2) template and directly transferred/integrated them onto flexible polydimethylsiloxane (PDMS) substrates by taking advantage of the distinguishable water-wettability of 2D MoS2vs. SiO2. The excellent structural integrity of the integrated vertical 2D MoS2 layers was confirmed by extensive spectroscopy/microscopy characterization. In addition, the stretch-driven unique tunability of their optical and surface properties was also examined. Moreover, we applied this material for flexible humidity sensing and identified significantly improved (>10 times) sensitivity over conventionally-designed horizontal 2D MoS2 layers, further confirming their high potential for unconventional flexible technologies.
Unconventional Gas Stimulation by Creating Synthetic Sweetspot (2013)

Abstract Unconventional resources are considered to be the game changer of near-by future energy. The huge resources of unconventional gas worldwide along with the increasing oil demand make the contribution of unconventional gas to be critical to the world economy. However, major challenges that operators face to produce from unconventional resources are due to the need for identifying a sweetspot, commercial stimulation technique that creates sufficient stimulated reservoir volume, very low permeability and different damage mechanisms such as water blocking, filtrate invasion during drilling, completion, stimulation or production. This paper describes a new stimulation technique that creates synthetic sweetspot by thermal and mechanical energies. An energized fluid was used to generate localized heat and pressure in tight core samples and therefore create microfracturing. Created microfractures significantly increased permeability, porosity, and stimulated reservoir volume. A synthetic sweetspot nearby a wellbore or fracture area can be created by the new treatment mechanism. Permeability of tested cores was measured by Coreflood system and showed significant increase with new treatment technique. Communication between micro and macro pores was also found as a result of the treatment. The treatment doesn't utilize any hydrocarbons and have no lift overs or potential damage. The generated high temperature and pressure can remove any damaging material or condensate banking near wellbore or fracture area. The new treatment technique was evaluated using Coreflood system with actual tight core samples collected from tight reservoirs in Saudi Arabia. NMR and CT-Scan analysis were used to show creation of microfracturing and permeability porosity changes.
Microencapsulation: concepts, mechanisms, methods and some applications in food technology (2014)

Microencapsulation is a process in which active substances are coated by extremely small capsules. It is a new technology that has been used in the cosmetics industry as well as in the pharmaceutical, agrochemical and food industries, being used in flavors, acids, oils, vitamins, microorganisms, among others. The success of this technology is due to the correct choice of the wall material, the core release form and the encapsulation method. Therefore, in this review, some relevant microencapsulation aspects, such as the capsule, wall material, core release forms, encapsulation methods and their use in food technology will be briefly discussed.
Mechanism of dorsal root ganglion stimulation for pain relief in painful diabetic polyneuropathy is not dependent on GABA release in the dorsal horn of the spinal cord (2019)

Dorsal root ganglion stimulation does not induce GABA release from the spinal dorsal horn cells, suggesting that the mechanisms underlying DRGS in pain relief are different from those of conventional SCS. The modulation of a GABA-mediated "Gate Control" in the DRG itself, functioning as a prime Gate of nociception, is suggested and discussed.
Protective Effects of a Novel Quinone Derivative, E3330, on Mouse Hepatitis Virus(MHV)-Induced Chronic Hepatitis in Athymic Nude Mice. (1997)

In this experiment, we examined the protective effects of a novel quinone derivative, E3330, on MHV-2cc-induced chronic hepatitis in athymic nude mice for up to 3 weeks after virus infection. The daily dose of 25 mg/kg b.w. suppressed the viral replication in the liver and the progression of hepatic lesions. The expansion of small focal lesions at 1 week after viral inoculation (WAI) was suppressed at 2 WAI, and the lesions were still small at 3 WAI in E3330-administered group, whereas small focal lesions at 1 WAI were expanded at 2 WAI to fuse with each other at 3 WAI in the control group. E3330 therefore showed protective effects on MHV-2cc-induced chronic hepatitis in athymic nude mice, but further studies are needed to analyze the mechanism.
Introduction (2020)

This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book deals with the question whether social exclusion by classmates lead to behaviour problems and learning difficulties of students with special educational needs (SEN). It aims to investigate whether the ordinary class attitude toward peers with a disability are related to the social participation of students with disabilities. Attitudes toward peers with a disability, as well as aspects of social participation like peer acceptance, friendships and peer interactions were assessed using questionnaires. The findings showed that on the one hand students with SEN are less accepted by their peers, have fewer friends, as well as less social interactions than their peers without SEN. The findings revealed an influence across the aspects of social participation for both, students with autism spectrum disorder and sensory and/or motor limitations compared to regular students.
Double-Stranded Ribonucleic Acid (RNA) Induces  -Cell Fas Messenger RNA Expression and Increases Cytokine-Induced  -Cell Apoptosis (2001)

Type 1 diabetes mellitus (T1DM) is an autoimmune disease caused by progressive destruction of insulin-producing pancreatic -cells.Both viral infections and the cytokines interleukin-1 (IL-1) and interferon- (IFN-) have been suggested as potential mediators of -cell death in early T1DM.We presently investigated whether the viral replicative intermediate double stranded RNA [here used as synthetic polyinosinic-polycytidylic acid (PIC)] modifies the effects of IL-1 and IFN- on gene expression and viability of rat pancreatic -cells.For this purpose, fluorescence-activated cell sorting-purified rat -cells were exposed for 6 -16 h (study of gene expression by RT-PCR) or 6 -9 days (study of viability by nuclear dyes) to PIC and/or IL-1 and IFN-.PIC increased the expression of Fas and Mn superoxide dismutase messenger RNAs by 5-to 10-fold.IL-1 and a combination of PIC and IFN- (but not PIC or IFN- alone) induced expression of inducible nitric oxide (NO) synthase (iNOS) and consequent NO production.Induction of iNOS expression by PIC and Materials and Methods -Cell isolation and cultureIslets were isolated from male Wistar rats by collagenase digestion and dissociated into single cells in a calcium-free medium containing