Over the past decade, interest about metal halide perovskites has rapidly increased, as they can find wide application in optoelectronic devices. Nevertheless, although thermal evaporation is crucial for the development and engineering of such devices based on multilayer structures, the optical properties of thermally deposited perovskite layers (spontaneous and amplified spontaneous emission) have been poorly investigated. This paper is a study from a nano- to micro- and macro-scale about the role of light-emitting species (namely free carriers and excitons) and trap states in the spontaneous emission of thermally evaporated thin layers of CH3NH3PbBr3 perovskite after wet air UV light trap passivation. The map of light emission from grains, carried out by SNOM at the nanoscale and by micro-PL techniques, clearly indicates that free and localized excitons (EXs) are the dominant light-emitting species, the localized excitons being the dominant ones in the presence of crystallites. These species also have a key role in the amplified spontaneous emission (ASE) process: for higher excitation densities, the relative contribution of localized EXs basically remains constant, while a clear competition between ASE and free EXs spontaneous emission is present, which suggests that ASE is due to stimulated emission from the free EXs.
As machine learning is used to make strides in med- ical diagnostics, few methods provide heuristics from which human doctors can learn directly. This work introduces a method for leveraging human observable structures, such as macro scale vascular formations, for producing assessments of medical conditions with rela- tively few training cases, and uncovering patterns that are potential diagnostic aids. The approach draws on shape grammars, a rule-based technique, pioneered in design and architecture, and accelerated through a re- cursive sub-graph mining algorithm. The distribution of rule instances in the data from which they are in- duced is then used as an intermediary representation en- abling common classification and anomaly detection ap- proaches to identify indicative rules with relatively small data sets. The method is applied to 7 Tesla time-of- flight (TOF) angiography MRI (n = 54) of human brain vasculature. The data were segmented and induced to generate representative grammar rules. Ensembles of rules were isolated to implicate vascular conditions reli- ably. This application demonstrates the power of auto- mated structured intermediary representations for as- sessing nuanced biological form relationships, and the strength of shape grammars, in particular for identify- ing indicative patterns in complex vascular networks.
Maintaining a balance between excitatory and inhibitory activity is an essential feature of neural networks of the neocortex. In the face of perturbations in the levels of excitation to cortical neurons, synapses adjust to maintain excitatory-inhibitory (EI) balance. In this review, we summarize research on this EI homeostasis in the neocortex, using stroke as our case study, and in particular the loss of excitation to distant cortical regions after focal lesions. Widespread changes following a localized lesion, a phenomenon known as diaschisis, are not only related to excitability, but also observed with respect to functional connectivity. Here, we highlight the main findings regarding the evolution of excitability and functional cortical networks during the process of post-stroke recovery, and how both are related to functional recovery. We show that cortical reorganization at a global scale can be explained from the perspective of EI homeostasis. Indeed, recovery of functional networks is paralleled by increases in excitability across the cortex. These adaptive changes likely result from plasticity mechanisms such as synaptic scaling and are linked to EI homeostasis, providing a possible target for future therapeutic strategies in the process of rehabilitation. In addition, we address the difficulty of simultaneously studying these multiscale processes by presenting recent advances in large-scale modeling of the human cortex in the contexts of stroke and EI homeostasis, suggesting computational modeling as a powerful tool to tie the meso- and macro-scale processes of recovery in stroke patients.
AbstractIsotopic techniques have been used to study phenomena in the geological, environmental, and ecological sciences. For example, isotopic values of multiple elements elucidate the pathways energy and nutrients take in the environment. Isoscapes interpolate isotopic values across a geographical surface and are used to study environmental processes in space and time. Thus, isoscapes can reveal ecological shifts at local scales, and show distribution thresholds in the wider environment at the macro-scale. This study demonstrates a further application of isoscapes, using soil isoscapes of 13C/12C and 15N/14N as an environmental baseline, to understand variation in trophic ecology across a population of Eurasian badgers (Meles meles) at a regional scale. The use of soil isoscapes reduced error, and elevated the statistical signal, where aggregated badger hairs were used, and where individuals were identified using genetic microarray analysis. Stable isotope values were affected by land-use type, elevation, and meteorology. Badgers in lowland habitats had diets richer in protein and were adversely affected by poor weather conditions in all land classes. It is concluded that soil isoscapes are an effective way of reducing confounding biases in macroscale, isotopic studies. The method elucidated variation in the trophic and spatial ecology of economically important taxa at a landscape level. These results have implications for the management of badgers and other carnivores with omnivorous tendencies in heterogeneous landscapes.
The evaluation of community livability quantifies the demands of human settlement at the micro scale, supporting urban governance decision-making at the macro scale. Big data generated by the urban management of government agencies can provide an accurate, real-time, and rich data set for livability evaluation. However, these data are intertwined by overlapping geographical management boundaries of different government agencies. It causes the difficulty of data integration and utilization when evaluating community livability. To address this problem, this paper proposes a scheme of partitioning basic geographical space into grids by optimally integrating various geographical management boundaries relevant to enterprise-level big data. Furthermore, the system of indexes on community livability is created, and the evaluation model of community livability is constructed. Taking Wuhan as an example, the effectiveness of the model is verified. After the evaluation, the experimental results show that the livability evaluation with reference to our basic geographic grids can effectively make use of governmental big data to spatially identify the multi-dimensional characteristics of a community, including management, environment, facility services, safety, and health. Our technical solution to evaluate community livability using gridded basic urban geographical data is of large potential in producing thematic data of community, constructing a 15-min community living circle of Wuhan, and enhancing the ability of the community to resist risks.
AbstractColonization of terrestrial environments by filamentous fungi relies on their ability to form networks that can forage for and connect resource patches. Despite the importance of these networks, ecologists rarely consider network features as functional traits because their measurement and interpretation are conceptually and methodologically difficult. To address these challenges, we have developed a pipeline to translate images of fungal mycelia, from both micro- and macro-scales, to weighted network graphs that capture ecologically relevant fungal behaviour. We focus on four properties that we hypothesize determine how fungi forage for resources, specifically: connectivity; relative construction cost; transport efficiency; and robustness against attack by fungivores. Constrained ordination and Pareto front analysis of these traits revealed that foraging strategies can be distinguished predominantly along a gradient of connectivity for micro- and macro-scale mycelial networks that is reminiscent of the qualitative ‘phalanx’ and ‘guerilla’ descriptors previously proposed in the literature. At one extreme are species with many inter-connections that increase the paths for multidirectional transport and robustness to damage, but with a high construction cost; at the other extreme are species with an opposite phenotype. Thus, we propose this approach represents a significant advance in quantifying ecological strategies for fungi using network information.
The calcium carbonate whisker (CW) and basalt fiber are gaining popularity due to its enhanced mechanical properties in composites. Also, the short and long fibers provide bridging role and resistance against cracking from micro- to macro-scale, respectively. The usage of long and short hybrid basalt fiber along with addition of CW in cement-based composites is still a research gap. In this work, experimental behavior of CW basalt hybrid fiber reinforced mortar is considered with various content and length (3 mm, 6 mm, 12 mm, and 20 mm) of hybrid basalt fibers. In addition to this, synergy performance index is determined to quantitatively evaluate the positive interaction of hybrid basalt fiber in cementitious materials. The strengthening effect of whiskers and basalt fibers are also studied using scanning electron microscopy. The CW with various basalt fiber contents having different length of hybrid basalt fiber is used. It was found that the four various length of hybrid basalt fiber together with CW in cement mortar exhibited enhanced compressive, flexural, and split tensile strength than that of pure mortar and single length basalt fiber reinforced cementitious mortar. The results of synergy performance index showed similar trend with the experimental results. The strengthening effect caused by step by step crack arresting mechanism was also observed in cementitious material.
Building polymers implemented into building panels and exterior façades have been determined as the major contributor to severe fire incidents, including the 2017 Grenfell Tower fire incident. To gain a deeper understanding of the pyrolysis process of these polymer composites, this work proposes a multi-scale modelling framework comprising of applying the kinetics parameters and detailed pyrolysis gas volatiles (parent combustion fuel and key precursor species) extracted from Molecular Dynamics models to a macro-scale Computational Fluid Dynamics fire model. The modelling framework was tested for pure and flame-retardant polyethylene systems. Based on the modelling results, the chemical distribution of the fully decomposed chemical compounds was realised for the selected polymers. Subsequently, the identified gas volatiles from solid to gas phases were applied as the parent fuel in the detailed chemical kinetics combustion model for enhanced predictions of toxic gas, charring, and smoke particulate predictions. The results demonstrate the potential application of the developed model in the simulation of different polymer materials without substantial prior knowledge of the thermal degradation properties from costly experiments.