Machine Learning Techniques for High Performance Engine Calibration

Ever since the advent of electronic fuel injection, auto manufacturers have been able to increase fuel efficiency and power production, and to meet stricter emission standards. Most of these systems use engine sensors (Speed, Throttle Position, etc.) in concert with look-up tables to determine the correct amount of fuel to inject. While these systems work well, it is time and labor intensive to fine tune the parameters for these look-up tables. In general, automobile manufacturers are able to absorb the cost of this calibration since the variation between engines in a new model line is often small enough as to be inconsequential for a specific calibration. However, a growing number of drivers are interested in modifying their vehicles with the intent of improving performance. While some aftermarket performance upgrades can be accounted for by the original manufacturer equipped (OEM) electronic control unit (ECU), other more significant changes, such as adding a turbocharger or installing larger fuel injectors, require more drastic accommodations. These modifications often require an entirely new ECU calibration or an aftermarket ECU to properly control the upgraded engine. The problem is now that the driver becomes responsible for the calibration of the ECU for this "new" engine. However, most drivers are unable to devote the resources required to achieve a calibration of the same quality as the original manufacturers. At best, this results in reduced fuel economy and performance, and at worst, unsafe and possibly destructive operation of the engine. The purpose of this thesis is to design and develop--using machine learning techniques--an approximate predictive model from current engine data logs, which can be used to rapidly and incrementally improve the calibration of the engine. While there has been research into novel control methods for engine air-fuel ratio control, these methods are inaccessible to the majority of end users, either due to cost or the required expertise with engine calibration. This study shows that there is a great deal of promise in applying machine learning techniques to engine calibration and that the process of engine calibration can be expedited by the application of these techniques

Use of Mandibular Distraction Osteogenesis to Correct Micrognathia and Airway Obstruction in Newborn Female with Pierre Robin Sequence and Neonatal Abstinence Syndrome in Rural Appalachia

We present a case of Pierre Robin sequence and Neonatal Abstinence Syndrome (NAS) in a newborn female patient to highlight the surgical technique of mandibular distraction osteogenesis to correct airway obstruction due to micrognathia. The patient presented as a transport after delivery due to respiratory distress. She was noted to have a cleft palate and micrognathia. The absence of other dysmorphic features diagnosed her with non-syndromic Pierre Robin sequence. To solve her upper airway obstruction, mandibular distraction osteogenesis was performed. This procedure allowed the patient to be weaned from all respiratory support and nasogastric tube feeds by the end of her hospitalization. She was able to be discharged home weeks before her internal hardware was surgically removed. Mandibular distraction osteogenesis was previously unavailable in rural Appalachia, making this case novel to the area. The patient also developed NAS during her hospitalization, highlighting the ongoing substance abuse epidemic in Appalachia

Neonatal Abstinence Syndrome and Infant Hearing Assessment: A Kids’ Inpatient Database Review

Objective: Neonatal abstinence syndrome (NAS) has become an epidemic. This study assesses documented rates of failed newborn hearing screening (NBHS) or hearing loss diagnosis (HL) in NAS infants, and sociodemographic factors associated with abnormal inpatient hearing results. Methods: The 2016 HCUP/KID national database was used to identify a weighted sample of infants with failed NBHS/HL during birth hospitalization. Independent variables included diagnoses of NAS/in-utero opioid exposure, HL risk factor presence and sociodemographic data. Univariate analyses and multivariate logistic regression were used to determine associations between NAS and abnormal hearing assessment. Results:NAS infants had lower odds ratio (OR) of documented failed NBHS (OR=0.76, p Conclusion: NAS children have lower rates of inpatient documented failed NBHS and higher rates of HL diagnosis. The complex medical care of these infants could complicate NBHS, documentation, and subsequent follow-up. Certain sociodemographic factors result in a higher risk of hearing loss

Fuel-Supply-Limited Stellar Relaxation Oscillations: Application to Multiple Rings around AGB Stars and Planetary Nebulae

We describe a new mechanism for pulsations in evolved stars: relaxation oscillations driven by a coupling between the luminosity-dependent mass-loss rate and the H fuel abundance in a nuclear-burning shell. When mass loss is included, the outward flow of matter can modulate the flow of fuel into the shell when the stellar luminosity is close to the Eddington luminosity $L_{\rm Edd}$. When the luminosity drops below $L_{\rm Edd}$, the mass outflow declines and the shell is re-supplied with fuel. This process can be repetitive. We demonstrate the existence of such oscillations and discuss the dependence of the results on the stellar parameters. In particular, we show that the oscillation period scales specifically with the mass of the H-burning relaxation shell (HBRS), defined as the part of the H-burning shell above the minimum radius at which the luminosity from below first exceeds the Eddington threshold at the onset of the mass loss phase. For a stellar mass M_*\sim 0.7\Msun, luminosity L_*\sim 10^4\Lsun, and mass loss rate |\dot M|\sim 10^{-5}\Msun yr$^{-1}$, the oscillations have a recurrence time $\sim 1400$ years $\sim 57\tau_{\rm fsm}$, where $\tau_{\rm fsm}$ is the timescale for modulation of the fuel supply in the HBRS by the varying mass-loss rate. This period agrees with the $\sim$ 1400-year period inferred for the spacings between the shells surrounding some planetary nebulae, and the the predictied shell thickness, of order 0.4 times the spacing, also agrees reasonably well.Comment: 15 pages TeX, 1 ps figure submitted to Ap

The iNaturalist Species Classification and Detection Dataset

Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories. In contrast, the natural world is heavily imbalanced, as some species are more abundant and easier to photograph than others. To encourage further progress in challenging real world conditions we present the iNaturalist species classification and detection dataset, consisting of 859,000 images from over 5,000 different species of plants and animals. It features visually similar species, captured in a wide variety of situations, from all over the world. Images were collected with different camera types, have varying image quality, feature a large class imbalance, and have been verified by multiple citizen scientists. We discuss the collection of the dataset and present extensive baseline experiments using state-of-the-art computer vision classification and detection models. Results show that current non-ensemble based methods achieve only 67% top one classification accuracy, illustrating the difficulty of the dataset. Specifically, we observe poor results for classes with small numbers of training examples suggesting more attention is needed in low-shot learning.Comment: CVPR 201

Viscosity and Rotation in Core-Collapse Supernovae

We construct models of core-collapse supernovae in one spatial dimension, including rotation, angular momentum transport, and viscous dissipation employing an alpha-prescription. We compare the evolution of a fiducial 11 M_sun non-rotating progenitor with its evolution including a wide range of imposed initial rotation profiles (1.25<P_0<8 s, where P_0 is the initial, approximately solid-body, rotation period of the iron core). This range of P_0 covers the region of parameter space from where rotation begins to modify the dynamics (P_0~8 s) to where angular velocities at collapse approach Keplerian (P_0~1 s). Assuming strict angular momentum conservation, all models in this range leave behind neutron stars with spin periods <10 ms, shorter than those of most radio pulsars, but similar to those expected theoretically for magnetars at birth. A fraction of the gravitational binding energy of collapse is stored in the free energy of differential rotation. This energy source may be tapped by viscous processes, providing a mechanism for energy deposition that is not strongly coupled to the mass accretion rate through the stalled supernova shock. This effect yields qualitatively new dynamics in models of supernovae. We explore several potential mechanisms for viscosity in the core-collapse environment: neutrino viscosity, turbulent viscosity caused by the magnetorotational instability (MRI), and turbulent viscosity by entropy- and composition-gradient-driven convection. We argue that the MRI is the most effective. We find that for rotation periods in the range P_0<~5 s, and a range of viscous stresses, that the post-bounce dynamics is significantly effected by the inclusion of this extra energy deposition mechanism; in several cases we obtain strong supernova explosions.Comment: accepted to ApJ, references added, discussion tightened, 26 pages, 11 figures, emulateap

Factors Controlling Soil Microbial Biomass and Bacterial Diversity and Community Composition in a Cold Desert Ecosystem: Role of Geographic Scale

Understanding controls over the distribution of soil bacteria is a fundamental Understanding controls over the distribution of soil bacteria is a fundamental step toward describing soil ecosystems, understanding their functional capabilities, and predicting their responses to environmental change. This study investigated the controls on the biomass, species richness, and community structure and composition of soil bacterial communities in the McMurdo Dry Valleys, Antarctica, at local and regional scales. The goals of the study were to describe the relationships between abiotic characteristics and soil bacteria in this unique, microbially dominated environment, and to test the scale dependence of these relationships in a low complexity ecosystem. Samples were collected from dry mineral soils associated with snow patches, which are a significant source of water in this desert environment, at six sites located in the major basins of the Taylor and Wright Valleys. Samples were analyzed for a suite of characteristics including soil moisture, pH, electrical conductivity, soil organic matter, major nutrients and ions, microbial biomass, 16 S rRNA gene richness, and bacterial community structure and composition. Snow patches created local biogeochemical gradients while inter-basin comparisons encompassed landscape scale gradients enabling comparisons of microbial controls at two distinct spatial scales. At the organic carbon rich, mesic, low elevation sites Acidobacteria and Actinobacteria were prevalent, while Firmicutes and Proteobacteria were dominant at the high elevation, low moisture and biomass sites. Microbial parameters were significantly related with soil water content and edaphic characteristics including soil pH, organic matter, and sulfate. However, the magnitude and even the direction of these relationships varied across basins and the application of mixed effects models revealed evidence of significant contextual effects at local and regional scales. The results highlight the importance of the geographic scale of sampling when determining the controls on soil microbial community characteristics. toward describing soil ecosystems, understanding their functional capabilities, and predicting their responses to environmental change. This study investigated the controls on the biomass, species richness, and community structure and composition of soil bacterial communities in the McMurdo Dry Valleys, Antarctica, at local and regional scales. The goals of the study were to describe the relationships between abiotic characteristics and soil bacteria in this unique, microbially dominated environment, and to test the scale dependence of these relationships in a low complexity ecosystem. Samples were collected from dry mineral soils associated with snow patches, which are a significant source of water in this desert environment, at six sites located in the major basins of the Taylor and Wright Valleys. Samples were analyzed for a suite of characteristics including soil moisture, pH, electrical conductivity, soil organic matter, major nutrients and ions, microbial biomass, 16 S rRNA gene richness, and bacterial community structure and composition. Snow patches created local biogeochemical gradients while inter-basin comparisons encompassed landscape scale gradients enabling comparisons of microbial controls at two distinct spatial scales. At the organic carbon rich, mesic, low elevation sites Acidobacteria and Actinobacteria were prevalent, while Firmicutes and Proteobacteria were dominant at the high elevation, low moisture and biomass sites. Microbial parameters were significantly related with soil water content and edaphic characteristics including soil pH, organic matter, and sulfate. However, the magnitude and even the direction of these relationships varied across basins and the application of mixed effects models revealed evidence of significant contextual effects at local and regional scales. The results highlight the importance of the geographic scale of sampling when determining the controls on soil microbial community characteristics

Antidiabetic Activities of Malaysian Agarwood (Aquilaria SPP) Leaves Extract

Aquilaria SPP or agarwood were reported to have pharmacological activities. There was a report of one diabetic patient who was drank infusion of agarwood leaf was found to have blood sugar return to normal

Kinematics of fault-related folding derived from a sandbox experiment

We analyze the kinematics of fault tip folding at the front of a fold-and-thrust wedge using a sandbox experiment. The analog model consists of sand layers intercalated with low-friction glass bead layers, deposited in a glass-sided experimental device and with a total thickness h = 4.8 cm. A computerized mobile backstop induces progressive horizontal shortening of the sand layers and therefore thrust fault propagation. Active deformation at the tip of the forward propagating basal décollement is monitored along the cross section with a high-resolution CCD camera, and the displacement field between pairs of images is measured from the optical flow technique. In the early stage, when cumulative shortening is less than about h/10, slip along the décollement tapers gradually to zero and the displacement gradient is absorbed by distributed deformation of the overlying medium. In this stage of detachment tip folding, horizontal displacements decrease linearly with distance toward the foreland. Vertical displacements reflect a nearly symmetrical mode of folding, with displacements varying linearly between relatively well defined axial surfaces. When the cumulative slip on the décollement exceeds about h/10, deformation tends to localize on a few discrete shear bands at the front of the system, until shortening exceeds h/8 and deformation gets fully localized on a single emergent frontal ramp. The fault geometry subsequently evolves to a sigmoid shape and the hanging wall deforms by simple shear as it overthrusts the flat ramp system. As long as strain localization is not fully established, the sand layers experience a combination of thickening and horizontal shortening, which induces gradual limb rotation. The observed kinematics can be reduced to simple analytical expressions that can be used to restore fault tip folds, relate finite deformation to incremental folding, and derive shortening rates from deformed geomorphic markers or growth strata