6,294 research outputs found
Machine learning in solar physics
The application of machine learning in solar physics has the potential to
greatly enhance our understanding of the complex processes that take place in
the atmosphere of the Sun. By using techniques such as deep learning, we are
now in the position to analyze large amounts of data from solar observations
and identify patterns and trends that may not have been apparent using
traditional methods. This can help us improve our understanding of explosive
events like solar flares, which can have a strong effect on the Earth
environment. Predicting hazardous events on Earth becomes crucial for our
technological society. Machine learning can also improve our understanding of
the inner workings of the sun itself by allowing us to go deeper into the data
and to propose more complex models to explain them. Additionally, the use of
machine learning can help to automate the analysis of solar data, reducing the
need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a
Living Review in Solar Physics (LRSP
TeamSTEPPS and Organizational Culture
Patient safety issues remain despite several strategies developed for their deterrence. While many safety initiatives bring about improvement, they are repeatedly unsustainable and short-lived. The index hospital’s goal was to build an organizational culture within a groundwork that improves teamwork and continuing healthcare team engagement. Teamwork influences the efficiency of patient care, patient safety, and clinical outcomes, as it has been identified as an approach for enhancing collaboration, decreasing medical errors, and building a culture of safety in healthcare. The facility implemented Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS), an evidence-based framework which was used for team training to produce valuable and needed changes, facilitating modification of organizational culture, increasing patient safety compliance, or solving particular issues. This study aimed to identify the correlation between TeamSTEPPS enactment and improved organizational culture in the ambulatory care nursing department of a New York City public hospital
Ultra High Strength Steels for Roll Formed Automotive Body in White
One of the more recent steel developments is the quenching and partitioning process, first proposed by Speer et al. in 2003 on developing 3rd generation advanced high-strength steel (AHSS). The quenching and partitioning (Q&P) process set a new way of producing martensitic steels with enhanced austenite levels, realised through controlled thermal treatments. The main objective of the so-called 3rd generation steels was to realise comparable properties to the 2nd generation but without high alloying additions. Generally, Q&P steels have remained within lab-scale environments, with only a small number of Q&P steels produced industrially. Q&P steels are produced either by a one-step or two-step process, and the re-heating mechanism for the two-step adds additional complexities when heat treating the material industrially. The Q&P steels developed and tested throughout this thesis have been designed to achieve the desired microstructural evolution whilst fitting in with Tata’s continuous annealing processing line (CAPL) capabilities. The CALPHAD approach using a combination of thermodynamics, kinetics, and phase transformation theory with software packages ThermoCalc and JMatPro has been successfully deployed to find novel Q&P steels. The research undertaken throughout this thesis has led to two novel Q&P steels, which can be produced on CAPL without making any infrastructure changes to the line. The two novel Q&P steels show an apparent reduction in hardness mismatch, illustrated visually and numerically after nano-indentation experiments. The properties realised after Q&P heat treatments on the C-Mn-Si alloy with 0.2 Wt.% C and the C-Mn-Si alloy with the small Cr addition is superior to the commercially available QP980/1180 steels by BaoSteel. Both novel alloys had comparable levels of elongation and hole expansion ratio to QP1180 but are substantially stronger with a > 320MPa increase in tensile stress. The heat treatment is also less complex as there is no requirement to heat the steel back up after quenching due to one-step quenching and partitioning being employed on the novel alloys
The Effects of Salt Precipitation During CO2 Injection into Deep Saline Aquifer and Remediation Techniques
The by-products of combustion from the utilisation of fossil fuels for energy generation are a source of greenhouse gas emissions, mainly Carbon dioxide (CO2). This has been attributed to climate change because of global warming. Carbon capture and storage (CCS) technology has the potential to reduce anthropogenic greenhouse gas emissions by capturing CO2 from emissions sources and stored in underground formations such as depleted oil and gas reservoirs or deep saline formations. Deep saline aquifers for disposal of greenhouse gases are attracting much attention as a result of their large storage capacity. The problem encountered during CO2 trapping in the saline aquifer is the vaporisation of water along with the dissolution of CO2. This vaporisation cause salt precipitation which eventually reduces porosity and impairs the permeability of the reservoir thereby impeding the storage capacity and efficiency of the technology. Salt precipitation during CO2 storage in deep saline aquifers can have severe consequences during carbon capture and storage operations in terms of CO2 injectivity.This work investigates and assesses, experimentally, the effects of the presence of salt precipitation on the CO2 injectivity, the factors that influence them on selected core samples by core flooding experiments, and remediation of salt precipitation during CO2 injection. The investigation also covered the determination of optimum range of deep saline aquifers for CO2 storage, and the effects of different brine-saturated sandstones during CO2 sequestration in deep saline aquifers. In this investigation, three (3) different sandstone core samples (Bentheimer, Salt Wash North, and Grey Berea) with different petrophysical properties were used for the study. This is carried out in three different phases for a good presentation.• Phase I of this study involved brine preparation and measurement of brine properties such as brine salinity, viscosity, and density. The brine solutions were prepared from different salts (NaCl, CaCl2, KCl, MgCl2), which represent the salt composition of a typical deep saline aquifer. The core samples were saturated with different brine salinities (5, 10, 15, 20, 25, wt.% Salt) and testing was conducted using the three selected core samples.• Phase II entailed the cleaning and characterisation of the core samples by experimental core analyses to determine the petrophysical properties: porosity and permeability. Helium Porosimetry and saturation methods were used for porosity determination. Core flooding was used to determine the permeability of the core samples. The core flooding process was conducted at a simulated reservoir pressure of 1500 psig, the temperature of 45 °C, with injection rates of 3.0 ml/min respectively. Interfacial tension (IFT) measurements between the CO2 and various brine salinities as used in the core flooding were also conducted in this phase. Remediation scenarios of opening the pore spaces of the core samples were carried out using the same core flooding rig and the precipitated core samples were flooded with remediation fluids (low salinity brine and seawater) under the same reservoir conditions. The petrophysical properties (Porosity, Permeability) of the core samples were measured before core flooding, after core flooding and remediation test respectively.• In phase III of the study, SEM Image analyses were conducted on the core samples before core flooding, after core flooding, and remediation test respectively. This was achieved by using the FEI Quanta FEG 250 FEG high-resolution Scanning Electron Microscope (SEM) interfaced to EDAX Energy Dispersive X-ray Analysis (EDX).xivResults from Bentheimer, Salt Wash North, and Grey Berea core samples indicated a reduction in porosity, permeability impairment, as well as salt precipitation. It was also found that, at 10 to 20 wt.% brine concentrations in both monovalent and divalent brine, a substantial volume of CO2 is sequestered, which indicates the optimum concentration ranges for storage purposes. The salting-out effect was greater in divalent salt, MgCl2 and CaCl2 as compared to monovalent salt (NaCl and KCl). Porosity decreased by 0.5% to 7% while permeability was decreased by up to 50% in all the tested scenarios. CO2 solubility was evaluated in a pressure decay test, which in turn affects injectivity. Hence, the magnitude of CO2 injectivity impairment depends on both the concentration and type of salt species. The findings from this study are directly relevant to CO2 sequestration in deep saline aquifers as well as screening criteria for carbon storage with enhanced gas and oil recovery processes. Injection of remediation fluids during remediation tests effectively opened the pore spaces and pore throats of the core samples and thereby increasing the core sample's porosity in the range of 14.0% to 28.5% and 2.2% to 12.9% after using low salinity brine and seawater remediation fluids respectively. Permeability also increases in the range of 40.6% to 68.4% and 7.4% to 17.2% after using low salinity brine and seawater remediation fluids respectively. These findings provide remediation strategies useful in dissolving precipitated salt as well as decreasing the salinity of the near-well brine which causes precipitation.The SEM images of the core samples after the flooding showed that salt precipitation not only plugged the pore spaces of the core matrix but also showed significant precipitation around the rock grains thereby showing an aggregation of the salts. This clearly proved that the reduction in the capacity of the rock is associated with salt precipitation in the pore spaces as well as the pore throats. Thus, insight gained in this study could be useful in designing a better mitigation technique, CO2 injectivity scenarios, as well as an operating condition for CO2 sequestration in deep saline aquifers
On Transforming Reinforcement Learning by Transformer: The Development Trajectory
Transformer, originally devised for natural language processing, has also
attested significant success in computer vision. Thanks to its super expressive
power, researchers are investigating ways to deploy transformers to
reinforcement learning (RL) and the transformer-based models have manifested
their potential in representative RL benchmarks. In this paper, we collect and
dissect recent advances on transforming RL by transformer (transformer-based RL
or TRL), in order to explore its development trajectory and future trend. We
group existing developments in two categories: architecture enhancement and
trajectory optimization, and examine the main applications of TRL in robotic
manipulation, text-based games, navigation and autonomous driving. For
architecture enhancement, these methods consider how to apply the powerful
transformer structure to RL problems under the traditional RL framework, which
model agents and environments much more precisely than deep RL methods, but
they are still limited by the inherent defects of traditional RL algorithms,
such as bootstrapping and "deadly triad". For trajectory optimization, these
methods treat RL problems as sequence modeling and train a joint state-action
model over entire trajectories under the behavior cloning framework, which are
able to extract policies from static datasets and fully use the long-sequence
modeling capability of the transformer. Given these advancements, extensions
and challenges in TRL are reviewed and proposals about future direction are
discussed. We hope that this survey can provide a detailed introduction to TRL
and motivate future research in this rapidly developing field.Comment: 26 page
Antimicrobial Peptides Aka Host Defense Peptides – From Basic Research to Therapy
This Special Issue reprint will address the most current and innovative developments in the field of HDP research across a range of topics, such as structure and function analysis, modes of action, anti-microbial effects, cell and animal model systems, the discovery of novel host-defense peptides, and drug development
Modelling, Monitoring, Control and Optimization for Complex Industrial Processes
This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors
Affinity-Based Reinforcement Learning : A New Paradigm for Agent Interpretability
The steady increase in complexity of reinforcement learning (RL) algorithms is accompanied by a corresponding increase in opacity that obfuscates insights into their devised strategies. Methods in explainable artificial intelligence seek to mitigate this opacity by either creating transparent algorithms or extracting explanations post hoc. A third category exists that allows the developer to affect what agents learn: constrained RL has been used in safety-critical applications and prohibits agents from visiting certain states; preference-based RL agents have been used in robotics applications and learn state-action preferences instead of traditional reward functions. We propose a new affinity-based RL paradigm in which agents learn strategies that are partially decoupled from reward functions. Unlike entropy regularisation, we regularise the objective function with a distinct action distribution that represents a desired behaviour; we encourage the agent to act according to a prior while learning to maximise rewards. The result is an inherently interpretable agent that solves problems with an intrinsic affinity for certain actions. We demonstrate the utility of our method in a financial application: we learn continuous time-variant compositions of prototypical policies, each interpretable by its action affinities, that are globally interpretable according to customers’ financial personalities.
Our method combines advantages from both constrained RL and preferencebased RL: it retains the reward function but generalises the policy to match a defined behaviour, thus avoiding problems such as reward shaping and hacking. Unlike Boolean task composition, our method is a fuzzy superposition of different prototypical strategies to arrive at a more complex, yet interpretable, strategy.publishedVersio
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