An Algorithmic Framework for Coverability in Well-Structured Systems

2012 ◽  
Author(s):  
Tim Strazny ◽  
Roland Meyer
Keyword(s):  
Structured Systems ◽  
Algorithmic Framework

Probing the Structural Features and the Micro-heterogeneity of Various Deep Eutectic Solvents and their Water Dilutions by the Photophysical Behaviour of Two Fluorophores

2020 ◽  
Author(s):  
Matteo Tiecco ◽  
Irene Di Guida ◽  
Pier Luigi Gentili ◽  
Raimondo Germani ◽  
Carmela Bonaccorso ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Transient Absorption ◽  
Photophysical Properties ◽  
Deep Eutectic Solvents ◽  
Structural Features ◽  
Solvation Dynamics ◽  
Structured Systems ◽  
Reduced Viscosity ◽  
Bond Network ◽  
Fluorescent Molecules

<div><div><div><p>The structural features of a series of diverse Deep Eutectic Solvents (DESs) have been investigated and characterized by means of two fluorescent probes. The spectral and photophysical properties of the latter are strictly dependent on the experienced environment, so that they can provide insights into the polarity, viscosity, hydrogen-bond network, and micro-heterogeneity of the various DESs.</p><p>In fact, the investigated DESs exhibit a variety of properties with regards to their hydrophilicity, acidity, and hydrogen-bond ability, and these details were deeply probed by the two fluorescent molecules. The effect of the addition of water, which is a key strategy for tuning the properties of these structured systems, was also tested. In particular, the excited state dynamics of the probes, measured by femtosecond-resolved transient absorption, proved instrumental in understanding the changes in the structural properties of the DESs, namely reduced viscosity and enhanced heterogeneity, as the water percentage increases. Differences between the various DESs in terms of both local microheterogeneity and bulk viscosity also emerged from the peculiar multi-exponential solvation dynamics undergone by the excited states of the probes.</p></div></div></div>

Probing the Structural Features and the Micro-heterogeneity of Various Deep Eutectic Solvents and their Water Dilutions by the Photophysical Behaviour of Two Fluorophores

2020 ◽  
Author(s):  
Matteo Tiecco ◽  
Irene Di Guida ◽  
Pier Luigi Gentili ◽  
Raimondo Germani ◽  
Carmela Bonaccorso ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Transient Absorption ◽  
Photophysical Properties ◽  
Deep Eutectic Solvents ◽  
Structural Features ◽  
Solvation Dynamics ◽  
Structured Systems ◽  
Reduced Viscosity ◽  
Bond Network ◽  
Fluorescent Molecules

<div><div><div><p>The structural features of a series of diverse Deep Eutectic Solvents (DESs) have been investigated and characterized by means of two fluorescent probes. The spectral and photophysical properties of the latter are strictly dependent on the experienced environment, so that they can provide insights into the polarity, viscosity, hydrogen-bond network, and micro-heterogeneity of the various DESs.</p><p>In fact, the investigated DESs exhibit a variety of properties with regards to their hydrophilicity, acidity, and hydrogen-bond ability, and these details were deeply probed by the two fluorescent molecules. The effect of the addition of water, which is a key strategy for tuning the properties of these structured systems, was also tested. In particular, the excited state dynamics of the probes, measured by femtosecond-resolved transient absorption, proved instrumental in understanding the changes in the structural properties of the DESs, namely reduced viscosity and enhanced heterogeneity, as the water percentage increases. Differences between the various DESs in terms of both local microheterogeneity and bulk viscosity also emerged from the peculiar multi-exponential solvation dynamics undergone by the excited states of the probes.</p></div></div></div>

An Algorithmic Framework for Performing Collaborative Filtering

2017 ◽  
Vol 51 (2) ◽  
pp. 227-234 ◽  
Author(s):  
Jonathan L. Herlocker ◽  
Joseph A. Konstan ◽  
Al Borchers ◽  
John Riedl
Keyword(s):  
Collaborative Filtering ◽  
Algorithmic Framework

scholarly journals Prioritising surgical cases deferred by the COVID-19 pandemic: an ethics-inspired algorithmic framework for health leaders

BMJ Leader ◽  
2021 ◽  
pp. leader-2020-000343
Author(s):  
Amit Jain ◽  
Tinglong Dai ◽  
Christopher G Myers ◽  
Punya Jain ◽  
Shruti Aggarwal
Keyword(s):  
Decision Analysis ◽  
Health System ◽  
System Capacity ◽  
Surgical Case ◽  
The World ◽  
The Face ◽  
Algorithmic Framework ◽  
Limited Health ◽  
Healthcare Leaders

Elective surgical suspension during the COVID-19 pandemic resulted in a sizeable surgical case backlog throughout the world. As we ramp back up, how do we decide which cases take priority? Potential future waves (or a future pandemic) may lead to additional surgical shutdown and subsequent reopening. Deciding which cases to prioritise in the face of limited health system capacity has emerged as a new challenge for healthcare leaders. Here we present an ethically grounded and operationally efficient surgical prioritisation framework for healthcare leaders and practitioners, drawing insights from decision analysis and organisational sciences.

scholarly journals Two-step machine learning enables optimized nanoparticle synthesis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Flore Mekki-Berrada ◽  
Zekun Ren ◽  
Tan Huang ◽  
Wai Kuan Wong ◽  
Fang Zheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Properties ◽  
Materials Science ◽  
Nanoparticle Synthesis ◽  
Bayesian Optimization ◽  
Absorbance Spectrum ◽  
Nanomaterial Synthesis ◽  
Target Spectrum ◽  
Colour Palette ◽  
Algorithmic Framework

AbstractIn materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum. Combining a Gaussian process-based Bayesian optimization (BO) with a deep neural network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable by humans, the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum.

scholarly journals A reconfiguration strategy for modular robots using origami folding

2018 ◽  
Vol 38 (1) ◽  
pp. 73-89 ◽  
Author(s):  
Meibao Yao ◽  
Christoph H. Belke ◽  
Hutao Cui ◽  
Jamie Paik
Keyword(s):  
Heuristic Algorithm ◽  
Minimum Energy ◽  
Computational Time ◽  
Modular Robots ◽  
Automatic Modeling ◽  
Minimum Energy Consumption ◽  
Modular Units ◽  
Optimal Reconfiguration ◽  
Algorithmic Framework ◽  
Modeling Algorithm

Reconfigurability in versatile systems of modular robots is achieved by changing the morphology of the overall structure as well as by connecting and disconnecting modules. Recurrent connectivity changes can cause misalignment that leads to mechanical failure of the system. This paper presents a new approach to reconfiguration, inspired by the art of origami, that eliminates connectivity changes during transformation. Our method consists of an energy-optimal reconfiguration planner that generates an initial 2D assembly pattern and an actuation sequence of the modular units, both resulting in minimum energy consumption. The algorithmic framework includes two approaches, an automatic modeling algorithm as well as a heuristic algorithm. We further demonstrate the effectiveness of our method by applying the algorithms to Mori, a modular origami robot, in simulation. Our results show that the heuristic algorithm yields reconfiguration schemes with high quality, compared with the automatic modeling algorithm, simultaneously saving a considerable amount of computational time and effort.

scholarly journals PEDLA: predicting enhancers with a deep learning-based algorithmic framework

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Feng Liu ◽  
Hao Li ◽  
Chao Ren ◽  
Xiaochen Bo ◽  
Wenjie Shu
Keyword(s):  
Deep Learning ◽  
Algorithmic Framework
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