Multilayered Complexity Analysis in Architectural Design: Two Measurement Methods Evaluating Self-Similarity and Complexity

This article contributes to clarifying the questions of whether and how fractal geometry, i.e., some of its main properties, are suitable to characterize architectural designs. This is done in reference to complexity-related aesthetic qualities in architecture, taking advantage of the measurability of one of them; the fractal dimension. Research in this area so far, has focused on 2-dimensional elevation plans. The authors present several methods to be used on a variety of source formats, among them a recent method to analyze pictures taken from buildings, i.e., 2.5-dimensional representations, to discuss the potential that lies within their combination. Color analysis methods will provide further information on the significance of a multilayered production and observation of results in this realm. In this publication results from the box-counting method are combined with a coordinate-based method for analyzing redundancy of proportions and their interrelations as well as the potential to include further layers of comparison are discussed. It presents a new area of box-counting implementation, a methodologically redesigned gradient analysis and its new algorithm as well as the combination of both. This research shows that in future systems it will be crucial to integrate several strategies to measure balanced aesthetic complexity in architecture.

Syotti: Scalable Bait Design for DNA Enrichment

Bait-enriched sequencing is a relatively new sequencing protocol that is becoming increasingly ubiquitous as it has been shown to successfully amplify regions of interest in metagenomic samples. In this method, a set of synthetic probes ("baits") are designed, manufactured, and applied to fragmented metagenomic DNA. The probes bind to the fragmented DNA and any unbound DNA is rinsed away, leaving the bound fragments to be amplified for sequencing. This effectively enriches the DNA for which the probes were designed. Most recently, Metsky et al. (Nature Biotech 2019) demonstrated that bait-enrichment is capable of detecting a large number of human viral pathogens within metagenomic samples. In this work, we formalize the problem of designing baits by defining the Minimum Bait Cover problem, which aims to find the smallest possible set of bait sequences that cover every position of a set of reference sequences under an approximate matching model. We show that the problem is NP-hard, and that it remains NP-hard under very restrictive assumptions. This indicates that no polynomial-time exact algorithm exists for the problem, and that the problem is intractable even for small and deceptively simple inputs. In light of this, we design an efficient heuristic that takes advantage of succinct data structures. We refer to our method as Syotti. The running time of Syotti shows linear scaling in practice, running at least an order of magnitude faster than state-of-the-art methods, including the recent method of Metsky et al. At the same time, our method produces bait sets that are smaller than the ones produced by the competing methods, while also leaving fewer positions uncovered. Lastly, we show that Syotti requires only 25 minutes to design baits for a dataset comprised of 3 billion nucleotides from 1000 related bacterial substrains, whereas the method of Metsky et al. shows clearly super-linear running time and fails to process even a subset of 8% of the data in 24 hours. Our implementation is publicly available at https://github.com/jnalanko/syotti.

Quantification of the suitable rooftop area for solar panel installation from overhead imagery using Convolutional Neural Networks

The integration of solar technology in the built environment is realized mainly through rooftop-installed panels. In this paper, we leverage state-of-the-art Machine Learning and computer vision techniques applied on overhead images to provide a geo-localization of the available rooftop surfaces for solar panel installation. We further exploit a 3D building database to associate them to the corresponding roof geometries by means of a geospatial post-processing approach. The stand-alone Convolutional Neural Network used to segment suitable rooftop areas reaches an intersection over union of 64% and an accuracy of 93%, while a post-processing step using building database improves the rejection of false positives. The model is applied to a case study area in the canton of Geneva and the results are compared with another recent method used in the literature to derive the realistic available area.

An Academic Performance Indicator Using Flexible Multi-Criteria Methods

Composite indicators are a very useful tool for conveying summary information on the overall performance of institutions and facilitating decision-making. Increasingly, there is a demand for indicators that allow performance to be assessed after the implementation of a strategy. This has several difficulties, and in this paper, we address three of them: how to evaluate at different points in time, how to estimate the weighting of the criteria and how to normalize the data. Our proposal is based on multicriteria techniques, using a recent method, uwTOPSIS, and is applied to data collected from 2975 students enrolled in the first year of science and engineering at the Industrial University of Santander (Colombia) from the first semester of 2016 to the first semester of 2019. In the paper, we show that our proposal makes it possible to measure and evaluate the academic performance of students at two points in time, and this allows the University to know whether its student support policy has been successful and to what degree it has been effective. Due to the large amount of data handled, data management has been done using R programming language, and model implementation has been done with Python.

Identifying Extreme COVID-19 Mortality Risks in English Small Areas: A Disease Cluster Approach

The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important.

Scale of oceanic eddy killing by wind from global satellite observations

Wind is the primary driver of the oceanic general circulation, yet the length scales at which this energy transfer occurs are unknown. Using satellite data and a recent method to disentangle multiscale processes, we find that wind deposits kinetic energy into the geostrophic ocean flow only at scales larger than 260 km, on a global average. We show that wind removes energy from scales smaller than 260 km at an average rate of −50 GW, a process known as eddy killing. To our knowledge, this is the first objective determination of the global eddy killing scale. We find that eddy killing is taking place at almost all times but with seasonal variability, peaking in winter, and it removes a substantial fraction (up to 90%) of the wind power input in western boundary currents. This process, often overlooked in analyses and models, is a major dissipation pathway for mesoscales, the ocean’s most energetic scales.

mtDNA Heteroplasmy: Origin, Detection, Significance, and Evolutionary Consequences

Mitochondrial DNA (mtDNA) is predominately uniparentally transmitted. This results in organisms with a single type of mtDNA (homoplasmy), but two or more mtDNA haplotypes have been observed in low frequency in several species (heteroplasmy). In this review, we aim to highlight several aspects of heteroplasmy regarding its origin and its significance on mtDNA function and evolution, which has been progressively recognized in the last several years. Heteroplasmic organisms commonly occur through somatic mutations during an individual’s lifetime. They also occur due to leakage of paternal mtDNA, which rarely happens during fertilization. Alternatively, heteroplasmy can be potentially inherited maternally if an egg is already heteroplasmic. Recent advances in sequencing techniques have increased the ability to detect and quantify heteroplasmy and have revealed that mitochondrial DNA copies in the nucleus (NUMTs) can imitate true heteroplasmy. Heteroplasmy can have significant evolutionary consequences on the survival of mtDNA from the accumulation of deleterious mutations and for its coevolution with the nuclear genome. Particularly in humans, heteroplasmy plays an important role in the emergence of mitochondrial diseases and determines the success of the mitochondrial replacement therapy, a recent method that has been developed to cure mitochondrial diseases.

Equivalence of Approaches to Relational Quantum Dynamics in Relativistic Settings

We have previously shown that three approaches to relational quantum dynamics—relational Dirac observables, the Page-Wootters formalism and quantum deparametrizations—are equivalent. Here we show that this “trinity” of relational quantum dynamics holds in relativistic settings per frequency superselection sector. Time according to a clock subsystem is defined via a positive operator-valued measure (POVM) that is covariant with respect to the group generated by its (quadratic) Hamiltonian. This differs from the usual choice of a self-adjoint clock observable conjugate to the clock momentum. It also resolves Kuchař's criticism that the Page-Wootters formalism yields incorrect localization probabilities for the relativistic particle when conditioning on a Minkowski time operator. We show that conditioning instead on the covariant clock POVM results in a Newton-Wigner type localization probability commonly used in relativistic quantum mechanics. By establishing the equivalence mentioned above, we also assign a consistent conditional-probability interpretation to relational observables and deparametrizations. Finally, we expand a recent method of changing temporal reference frames, and show how to transform states and observables frequency-sector-wise. We use this method to discuss an indirect clock self-reference effect and explore the state and temporal frame-dependence of the task of comparing and synchronizing different quantum clocks.

Last mile deliveries with lockers: formulations and algorithms

In this paper we consider the use of lockers in parcel delivery, a recent method used in the last mile logistics. Lockers are pick up points made of several cells that are located in several points of a city where customers can collect their parcels as an alternative to home delivery. We study routing problems in which one or multiple vehicles are used to deliver parcels directly to customers or to lockers. We also study the influence of the introduction of lockers when these problems include time windows. We propose a set of novel formulations for these problems, some valid inequalities, and a branch-and-cut algorithm. Moreover, we investigate the difference between the routing problems with lockers and the classical routing problems.

Bulk Hyperpolarization of Inorganic Materials

Solid-state NMR spectroscopy is a well-established method to obtain atomic-level information about the structure of inorganic materials, but its use is often limited by low sensitivity. We review how solvent generated dynamic nuclear polarization can be used to increase sensitivity in solid-state NMR of inorganic materials, with emphasis on our recent method for hyperpolarization of proton-free bulk. We use selected examples to show how overall gains in sensitivity can be observed in both the surface and bulk spectra of inorganic compounds such as lithium titanate. The hyperpolarization methods reviewed here can be used to improve NMR sensitivity for a range of inorganic materials.