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Super resolution microscopy was developed to overcome the Abbe diffraction limit, which effects conventional optical microscopy, in order to study the smaller components of biological systems. In recent years nanomaterials have been explored as luminescent probes for super resolution microscopy, as many have advantages over traditional fluorescent dye molecules. This review will summarize several different types of nanomaterial probes, covering quantum dots, carbon dots, and dye doped nanoparticles. For the purposes of this review the term “nanoparticle” will be limited to polymer-based, protein-based, and silica-based nanoparticles, including core–shell structured nanoparticles. Luminescent nanomaterials have shown promise as super-resolution probes, and continued research in this area will yield new advances in both materials science and biochemical microscopy at the nanometer scale. 

Distributed manipulators - consisting of a set of actuators or robots working cooperatively to achieve a manipulation task - are robust and flexible tools for performing a range of planar manipulation skills. One novel example is the delta array, a distributed manipulator composed of a grid of delta robots, capable of performing dexterous manipulation tasks using strategies incorporating both dynamic and static contact. Hand-designing effective distributed control policies for such a manipulator can be complex and time consuming, given the high-dimensional action space and unfamiliar system dynamics. In this paper, we examine the principles guiding development and control of such a delta array for a planar translation task. We explore policy learning as a robust cooperative control approach, allowing for smooth manipulation of a range of objects, showing improved accuracy and efficiency over baseline human-designed policies. 

Abstract In drylands, runoff during storms redistributes water and nutrients from bare soil areas to vegetated patches, subsidizing vegetation with additional resources. The extent of this redistribution depends on the interplay between surface roughness and permeability; greater permeability in vegetated patches promotes run‐on to vegetation, but greater surface roughness diverts runoff, producing tortuous flow paths that bypass vegetation. Here, this interplay is examined in virtual experiments using the 2D Saint Venant Equations to measure runoff connectivity. Flowpaths are delineated using tracers advected by the flow. Distances between tracer sources and sinks along flowpaths measure hydrologic connectivity at two lengthscales: connectivity to the hillslope outlet and within‐slope source‐sink connectivity. Differences between these connectivity lengthscales indicate how flow may “by‐pass” vegetated patches within hillslopes. At the hillslope scale, a derived power‐law relation between the runoff coefficient and outlet connectivity describes hillslope water losses, providing a foundation for identifying landscapes likely to shed water. 

ABSTRACT The variability induced by precipitable water vapour (PWV) can heavily affect the accuracy of time-series photometric measurements gathered from the ground, especially in the near-infrared. We present here a novel method of modelling and mitigating this variability, as well as open-sourcing the developed tool – Umbrella. In this study, we evaluate the extent to which the photometry in three common bandpasses (r′, i′, z′), and SPECULOOS’ primary bandpass (I + z′), are photometrically affected by PWV variability. In this selection of bandpasses, the I + z′ bandpass was found to be most sensitive to PWV variability, followed by z′, i′, and r′. The correction was evaluated on global light curves of nearby late M- and L-type stars observed by SPECULOOS’ Southern Observatory (SSO) with the I + z′ bandpass, using PWV measurements from the LHATPRO and local temperature/humidity sensors. A median reduction in RMS of 1.1 per cent was observed for variability shorter than the expected transit duration for SSO’s targets. On timescales longer than the expected transit duration, where long-term variability may be induced, a median reduction in RMS of 53.8 per cent was observed for the same method of correction. 

Dye-doped nanoparticles have been investigated as bright, luminescent labels for super-resolution microscopy via localization methods. One key factor in super-resolution is the size of the luminescent label, which in some cases results in a frame shift between the label target and the label itself. Ag@SiO 2 core–shell nanoparticles, doped with organic fluorophores, have shown promise as super-resolution labels. One key aspect of these nanoparticles is that they blink under certain conditions, allowing super-resolution localization with a single excitation source in aqueous solution. In this work, we investigated the effects of both the Ag core and the silica (SiO 2 ) shell on the self-blinking properties of these nanoparticles. Both core size and shell thickness were manipulated by altering the reaction time to determine core and shell effects on photoblinking. Size and shell thickness were investigated individually under both dry and hydrated conditions and were then doped with a 1 mM solution of Rhodamine 110 for analysis. We observed that the cores themselves are weakly luminescent and are responsible for the blinking observed in the fully-synthesized metal-enhanced fluorescence nanoparticles. There was no statistically significant difference in photoblinking behavior—both intensity and duty cycle—with decreasing core size. This observation was used to synthesize smaller nanoparticles ranging from approximately 93 nm to 110 nm as measured using dynamic light scattering. The blinking particles were localized via super-resolution microscopy and show single particle self-blinking behavior. As the core size did not impact blinking performance or intensity, the nanoparticles can instead be tuned for optimal size without sacrificing luminescence properties. 

Abstract Environmental decisions with substantial social and environmental implications are regularly informed by model predictions, incurring inevitable uncertainty. The selection of a set of model predictions to inform a decision is usually based on model performance, measured by goodness‐of‐fit metrics. Yet goodness‐of‐fit metrics have a questionable relationship to a model's value to end users, particularly when validation data are themselves uncertain. For example, decisions based on flow frequency models are not necessarily improved by adopting models with the best overall goodness of fit. We propose an alternative model evaluation approach based on the conditional value of sample information, first defined in 1961, which has found extensive use in sampling design optimization but which has not previously been used for model evaluation. The metric uses observations from a validation set to estimate the expected monetary costs associated with model prediction uncertainties. A model is only considered superior to alternatives if (i) its predictions reduce these costs and (ii) sufficient validation data are available to distinguish its performance from alternative models. By describing prediction uncertainties in monetary terms, the metric facilitates the communication of prediction uncertainty by end users, supporting the inclusion of uncertainty analysis in decision making.