scholarly journals Predicting the long-term stability of compact multiplanet systems

2020 ◽  
Vol 117 (31) ◽  
pp. 18194-18205 ◽  
Author(s):  
Daniel Tamayo ◽  
Miles Cranmer ◽  
Samuel Hadden ◽  
Hanno Rein ◽  
Peter Battaglia ◽  
...  
Keyword(s):  
Machine Learning Techniques ◽  
Training Dataset ◽  
Complementary Method ◽  
Long Term Stability ◽  
Learning Techniques ◽  
Resonant Dynamics ◽  
The Stability ◽  
Long Timescales

We combine analytical understanding of resonant dynamics in two-planet systems with machine-learning techniques to train a model capable of robustly classifying stability in compact multiplanet systems over long timescales of109orbits. Our Stability of Planetary Orbital Configurations Klassifier (SPOCK) predicts stability using physically motivated summary statistics measured in integrations of the first104orbits, thus achieving speed-ups of up to105over full simulations. This computationally opens up the stability-constrained characterization of multiplanet systems. Our model, trained on ∼100,000 three-planet systems sampled at discrete resonances, generalizes both to a sample spanning a continuous period-ratio range, as well as to a large five-planet sample with qualitatively different configurations to our training dataset. Our approach significantly outperforms previous methods based on systems’ angular momentum deficit, chaos indicators, and parametrized fits to numerical integrations. We use SPOCK to constrain the free eccentricities between the inner and outer pairs of planets in the Kepler-431 system of three approximately Earth-sized planets to both be below 0.05. Our stability analysis provides significantly stronger eccentricity constraints than currently achievable through either radial velocity or transit-duration measurements for small planets and within a factor of a few of systems that exhibit transit-timing variations (TTVs). Given that current exoplanet-detection strategies now rarely allow for strong TTV constraints [S. Hadden, T. Barclay, M. J. Payne, M. J. Holman,Astrophys. J.158, 146 (2019)], SPOCK enables a powerful complementary method for precisely characterizing compact multiplanet systems. We publicly release SPOCK for community use.

scholarly journals Fabrication and Characterization of Micro-Nano Electrodes for Implantable BCI

Micromachines ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 242 ◽  
Author(s):  
Xi ◽  
Ji ◽  
Guo ◽  
Li ◽  
Liu
Keyword(s):  
Electrochemical Impedance ◽  
Impedance Spectrum ◽  
Electrochemical Impedance Spectrum ◽  
Cyclic Voltammogram ◽  
Long Term Stability ◽  
Computer Interfaces ◽  
Ag Electrodes ◽  
The Stability

Signal recording and stimulation with high spatial and temporal resolution are of increasing interest with the development of implantable brain-computer interfaces (BCIs). However, implantable BCI technology still faces challenges in the biocompatibility and long-term stability of devices after implantation. Due to the cone structure, needle electrodes have advantages in the biocompatibility and stability as nerve recording electrodes. This paper develops the fabrication of Ag needle micro/nano electrodes with a laser-assisted pulling method and modifies the electrode surface by electrochemical oxidation. A significant impedance reduction of the modified Ag/AgCl electrodes compared to the Ag electrodes is demonstrated by the electrochemical impedance spectrum (EIS). Furthermore, the stability of modified Ag/AgCl electrodes is confirmed by cyclic voltammogram (CV) scanning. These findings suggest that these micro/nano electrodes have a great application prospect in neural interfaces.

Determining the extent and drivers of attrition losses from wind using long-term datasets and machine learning techniques

2019 ◽  
Vol 92 (4) ◽  
pp. 425-435 ◽  
Author(s):  
John Moore ◽  
Yue Lin
Keyword(s):  
Machine Learning ◽  
Basal Area ◽  
Wind Damage ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Validation Dataset ◽  
Gradient Boosting ◽  
Factors Associated ◽  
Learning Techniques

Abstract In addition to causing large-scale catastrophic damage to forests, wind can also cause damage to individual trees or small groups of trees. Over time, the cumulative effect of this wind-induced attrition can result in a significant reduction in yield in managed forests. Better understanding of the extent of these losses and the factors associated with them can aid better forest management. Information on wind damage attrition is often captured in long-term growth monitoring plots but analysing these large datasets to identify factors associated with the damage can be problematic. Machine learning techniques offer the potential to overcome some of the challenges with analysing these datasets. In this study, we applied two commonly-available machine learning algorithms (Random Forests and Gradient Boosting Trees) to a large, long-term dataset of tree growth for radiata pine (Pinus radiata D. Don) in New Zealand containing more than 157 000 observations. Both algorithms identified stand density and height-to-diameter ratio as being the two most important variables associated with the proportion of basal area lost to wind. The algorithms differed in their ease of parameterization and processing time as well as their overall ability to predict wind damage loss. The Random Forest model was able to predict ~43 per cent of the variation in the proportion of basal area lost to wind damage in the training dataset (a random sample of 80 per cent of the original data) and 45 per cent of the validation dataset (the remaining 20 per cent of the data). Conversely, the Gradient Boosting Tree model was able to predict more than 99 per cent of the variation in wind damage loss in the training dataset, but only ~49 per cent of the variation in the validation dataset, which highlights the potential for overfitting models to specific datasets. When applying these techniques to long-term datasets, it is also important to be aware of potential issues with the underlying data such as missing observations resulting from plots being abandoned without measurement when damage levels have been very high.

The Stability of the WHO Reference Thromboplastin NIBS & C 67/40

1979 ◽  
Vol 42 (04) ◽  
pp. 1135-1140 ◽  
Author(s):  
G I C Ingram
Keyword(s):  
Human Brain ◽  
Collaborative Study ◽  
Calibration Constant ◽  
Long Term Stability ◽  
Freeze Dried ◽  
Show Evidence ◽  
Human Material ◽  
Reference Preparation ◽  
The Stability

SummaryThe International Reference Preparation of human brain thromboplastin coded 67/40 has been thought to show evidence of instability. The evidence is discussed and is not thought to be strong; but it is suggested that it would be wise to replace 67/40 with a new preparation of human brain, both for this reason and because 67/40 is in a form (like Thrombotest) in which few workers seem to use human brain. A �plain� preparation would be more appropriate; and a freeze-dried sample of BCT is recommended as the successor preparation. The opportunity should be taken also to replace the corresponding ox and rabbit preparations. In the collaborative study which would be required it would then be desirable to test in parallel the three old and the three new preparations. The relative sensitivities of the old preparations could be compared with those found in earlier studies to obtain further evidence on the stability of 67/40; if stability were confirmed, the new preparations should be calibrated against it, but if not, the new human material should receive a calibration constant of 1.0 and the new ox and rabbit materials calibrated against that.The types of evidence available for monitoring the long-term stability of a thromboplastin are discussed.

Enhanced stability of conductive polyacetylene in ladder-like surface-grafted brushes

2016 ◽  
Vol 7 (36) ◽  
pp. 5664-5670 ◽  
Author(s):  
Michał Szuwarzyński ◽  
Karol Wolski ◽  
Szczepan Zapotoczny
Keyword(s):  
Term Stability ◽  
Long Term Stability ◽  
Enhanced Stability

Formation and characterization of polyacetylene-based brushes that exhibit exceptional long term stability in air is presented here.

Projecting conflict risk following the Shared Socioeconomic pathways: what role for water stress and climate?

2021 ◽  
Author(s):  
Sophie de Bruin ◽  
Jannis Hoch ◽  
Nina von Uexkull ◽  
Halvard Buhaug ◽  
Nico Wanders
Keyword(s):  
Climate Change ◽  
At Risk ◽  
Machine Learning Techniques ◽  
Mitigation Measures ◽  
Related Factors ◽  
Socioeconomic Impacts ◽  
Learning Techniques ◽  
The Future ◽  
Conflict Risk

<p>The socioeconomic impacts of changes in climate-related and hydrology-related factors are increasingly acknowledged to affect the on-set of violent conflict. Full consensus upon the general mechanisms linking these factors with conflict is, however, still limited. The absence of full understanding of the non-linearities between all components and the lack of sufficient data make it therefore hard to address violent conflict risk on the long-term. </p><p>Although it is neither desirable nor feasible to make exact predictions, projections are a viable means to provide insights into potential future conflict risks and uncertainties thereof. Hence, making different projections is a legitimate way to deal with and understand these uncertainties, since the construction of diverse scenarios delivers insights into possible realizations of the future.  </p><p>Through machine learning techniques, we (re)assess the major drivers of conflict for the current situation in Africa, which are then applied to project the regions-at-risk following different scenarios. The model shows to accurately reproduce observed historic patterns leading to a high ROC score of 0.91. We show that socio-economic factors are most dominant when projecting conflicts over the African continent. The projections show that there is an overall reduction in conflict risk as a result of increased economic welfare that offsets the adverse impacts of climate change and hydrologic variables. It must be noted, however, that these projections are based on current relations. In case the relations of drivers and conflict change in the future, the resulting regions-at-risk may change too.   By identifying the most prominent drivers, conflict risk mitigation measures can be tuned more accurately to reduce the direct and indirect consequences of climate change on the population in Africa. As new and improved data becomes available, the model can be updated for more robust projections of conflict risk in Africa under climate change.</p>

Long-term Cholesterol Risk Prediction using Machine Learning Techniques in ELSA Database

2021 ◽  
Author(s):  
Nikos Fazakis ◽  
Elias Dritsas ◽  
Otilia Kocsis ◽  
Nikos Fakotakis ◽  
Konstantinos Moustakas
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Machine Learning Techniques ◽  
Learning Techniques

Machine Learning for Neurodegenerative Disorder Diagnosis — Survey of Practices and Launch of Benchmark Dataset

2018 ◽  
Vol 27 (03) ◽  
pp. 1850011 ◽  
Author(s):  
Athanasios Tagaris ◽  
Dimitrios Kollias ◽  
Andreas Stafylopatis ◽  
Georgios Tagaris ◽  
Stefanos Kollias
Keyword(s):  
Machine Learning ◽  
Neurodegenerative Disorder ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Network Architectures ◽  
Imaging Data ◽  
Learning Techniques ◽  
Medical Examinations

Neurodegenerative disorders, such as Alzheimer’s and Parkinson’s, constitute a major factor in long-term disability and are becoming more and more a serious concern in developed countries. As there are, at present, no effective therapies, early diagnosis along with avoidance of misdiagnosis seem to be critical in ensuring a good quality of life for patients. In this sense, the adoption of computer-aided-diagnosis tools can offer significant assistance to clinicians. In the present paper, we provide in the first place a comprehensive recording of medical examinations relevant to those disorders. Then, a review is conducted concerning the use of Machine Learning techniques in supporting diagnosis of neurodegenerative diseases, with reference to at times used medical datasets. Special attention has been given to the field of Deep Learning. In addition to that, we communicate the launch of a newly created dataset for Parkinson’s disease, containing epidemiological, clinical and imaging data, which will be publicly available to researchers for benchmarking purposes. To assess the potential of the new dataset, an experimental study in Parkinson’s diagnosis is carried out, based on state-of-the-art Deep Neural Network architectures and yielding very promising accuracy results.

scholarly journals Limitations and Challenges in the Stability of Cysteamine Eye Drop Compounded Formulations

2021 ◽  
Vol 15 (1) ◽  
pp. 2
Author(s):  
Cristina Martín-Sabroso ◽  
Mario Alonso-González ◽  
Ana Fernández-Carballido ◽  
Juan Aparicio-Blanco ◽  
Damián Córdoba-Díaz ◽  
...  
Keyword(s):  
Shelf Life ◽  
Stability Study ◽  
Eye Drops ◽  
Microbiological Analysis ◽  
Nitrogen Gas ◽  
Long Term Stability ◽  
Main Degradation Product ◽  
The Stability ◽  
Unstable Molecule

Accumulation of cystine crystals in the cornea of patients suffering from cystinosis is considered pathognomonic and can lead to severe ocular complications. Cysteamine eye drop compounded formulations, commonly prepared by hospital pharmacy services, are meant to diminish the build-up of corneal cystine crystals. The objective of this work was to analyze whether the shelf life proposed for six formulations prepared following different protocols used in hospital pharmacies is adequate to guarantee the quality and efficacy of cysteamine eye drops. The long-term and in-use stabilities of these preparations were studied using different parameters: content of cysteamine and its main degradation product cystamine; appearance, color and odor; pH and viscosity; and microbiological analysis. The results obtained show that degradation of cysteamine was between 20% and 50% after one month of storage in the long-term stability study and between 35% and 60% in the in-use study. These data confirm that cysteamine is a very unstable molecule in aqueous solution, the presence of oxygen being the main degradation factor. Saturation with nitrogen gas of the solutions offers a means of reducing cysteamine degradation. Overall, all the formulae studied presented high instability at the end of their shelf life, suggesting that their clinical efficacy might be dramatically compromised.

LONG-TERM STABILITY ANALYSIS OF ACOUSTIC ABSORBING BOUNDARY CONDITIONS

2013 ◽  
Vol 23 (11) ◽  
pp. 2129-2154 ◽  
Author(s):  
HÉLÈNE BARUCQ ◽  
JULIEN DIAZ ◽  
VÉRONIQUE DUPRAT
Keyword(s):  
Stability Analysis ◽  
Boundary Conditions ◽  
Absorbing Boundary Conditions ◽  
Computational Domain ◽  
Acoustic Wave Equation ◽  
Absorbing Boundary ◽  
Term Stability ◽  
Long Term Stability ◽  
The Stability

This work deals with the stability analysis of a one-parameter family of Absorbing Boundary Conditions (ABC) that have been derived for the acoustic wave equation. We tackle the problem of long-term stability of the wave field both at the continuous and the numerical levels. We first define a function of energy and show that it is decreasing in time. Its discrete form is also decreasing under a Courant–Friedrichs–Lewy (CFL) condition that does not depend on the ABC. Moreover, the decay rate of the continuous energy can be determined: it is exponential if the computational domain is star-shaped and this property can be illustrated numerically.

A Review of Preparation and Tribological Applications of Pickering Emulsion

2021 ◽  
pp. 1-27
Author(s):  
Yichen Bao ◽  
Kai Liu ◽  
Quan Zheng ◽  
Lulu Yao ◽  
Yufu Xu
Keyword(s):  
Pickering Emulsion ◽  
Solid Particles ◽  
Preparation Methods ◽  
Factors Affecting ◽  
Long Term Stability ◽  
New Strategy ◽  
New Type ◽  
The Stability ◽  
Near Future

Abstract Pickering emulsion is a new type of stable emulsion made by ultra-fine solid particles instead of traditional surfactants as stabilizers, which has received widespread attention in recent years. The preparation methods of stator-rotor homogenization, high-pressure homogenization, and ultrasonic emulsification were compared with others in this work. The main factors affecting the stability of Pickering emulsion are the surface humidity of the solid particles, the polarity of the oil phase, and the oil-water ratio. These factors could affect the nature of the solid particles, the preparation process of Pickering emulsion and the external environment. Consequently, the long-term stability of Pickering emulsion is still a challenge. The tribological investigations of Pickering emulsion were summarized, and the multifunctional Pickering emulsion shows superior prospects for tribological applications. Moreover, the latest development of Pickering emulsion offers a new strategy for smart lubrication in the near future.

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