Lighting design method of museum exhibition hall based on Internet of Things and deep learning

In order to improve the lighting effect of the museum exhibition hall, clearly express the exhibition content of the museum exhibition hall, a lighting design method of museum exhibition hall based on Internet of Things and deep learning is proposed. According to the characteristics and functions of light sources and lamps, select appropriate light sources and lamps, and establish a convolutional neural network to evaluate the performance of lighting characteristic network model through computing accuracy, precision, recall and F1 score. Because the illumination of museum exhibition hall cannot be too high, the light projection method is designed to realize the lighting design of museum exhibition hall from two aspects: lighting mode and lighting characteristics, environmental lighting and light source form. The experimental results show that the lighting design method of the museum exhibition hall based on the Internet of Things and deep learning can achieve more than 70%, which has a good lighting effect and can clearly express the display content of the museum exhibition hall.

Scalable Species Tree Inference with External Constraints

Species tree inference under the multi-species coalescent (MSC) model is a basic step in biological discovery. Despite the developments in recent years of methods that are proven statistically consistent and that have high accuracy, large datasets create computational challenges. Although there is gener- ally some information available about the species trees that could be used to speed up the estimation, only one method, ASTRAL-J, a recent development in the ASTRAL family of methods, is able to use this information. Here we describe two new methods, NJst-J and FASTRAL-J, that can estimate the species tree given partial knowledge of the species tree in the form of a non-binary unrooted constraint tree.. We show that both NJst-J and FASTRAL-J are much faster than ASTRAL-J and we prove that all three methods are statistically consistent under the multi-species coalescent model subject to this constraint. Our extensive simulation study shows that both FASTRAL-J and NJst-J provide advantages over ASTRAL-J: both are faster (and NJst-J is particularly fast), and FASTRAL-J is generally at least as accurate as ASTRAL-J. An analysis of the Avian Phylogenomics project dataset with 48 species and 14,446 genes presents additional evidence of the value of FASTRAL-J over ASTRAL-J (and both over ASTRAL), with dramatic reductions in running time (20 hours for default ASTRAL, and minutes or seconds for ASTRAL-J and FASTRAL-J, respectively). Availability: FASTRAL-J and NJst-J are available in open source form at https://github.com/ RuneBlaze/FASTRAL-constrained and https://github.com/RuneBlaze/NJst-constrained. Locations of the datasets used in this study and detailed commands needed to reproduce the study are provided in the supplementary materials at http://tandy.cs.illinois.edu/baqiao-suppl.pdf.

Recursive MAGUS: Scalable and accurate multiple sequence alignment

Multiple sequence alignment tools struggle to keep pace with rapidly growing sequence data, as few methods can handle large datasets while maintaining alignment accuracy. We recently introduced MAGUS, a new state-of-the-art method for aligning large numbers of sequences. In this paper, we present a comprehensive set of enhancements that allow MAGUS to align vastly larger datasets with greater speed. We compare MAGUS to other leading alignment methods on datasets of up to one million sequences. Our results demonstrate the advantages of MAGUS over other alignment software in both accuracy and speed. MAGUS is freely available in open-source form at https://github.com/vlasmirnov/MAGUS.

Place and role of legal doctrine in the system of forms (sources) of law: general-theoretical aspect

The article deals with the problem of legal nature of the legal doctrine as a source (form) of law. The article substantiates the idea that the legal doctrine has a twofold meaning, since it has an independent meaning in the system of forms of law of various legal systems, as well as is fully a source of law that forms the foundation, methodological basis for the creation, interpretation and application of legal norms in other legal systems, in particular the Russian state. The author draws attention to the characteristic features inherent in the legal doctrine, analyzes its role in various legal systems, where it acts as a form of law. The author compares the positions of various points of view of Russian scientists on the legal nature of legal doctrine as a form of law. The author identifies and describes the characteristic features of legal doctrine as a source of law in the activity of the mechanism of the Russian state in the sphere of legislative and executive implementation.

Using Robinson-Foulds supertrees in divide-and-conquer phylogeny estimation

One of the Grand Challenges in Science is the construction of the Tree of Life, an evolutionary tree containing several million species, spanning all life on earth. However, the construction of the Tree of Life is enormously computationally challenging, as all the current most accurate methods are either heuristics for NP-hard optimization problems or Bayesian MCMC methods that sample from tree space. One of the most promising approaches for improving scalability and accuracy for phylogeny estimation uses divide-and-conquer: a set of species is divided into overlapping subsets, trees are constructed on the subsets, and then merged together using a “supertree method”. Here, we present Exact-RFS-2, the first polynomial-time algorithm to find an optimal supertree of two trees, using the Robinson-Foulds Supertree (RFS) criterion (a major approach in supertree estimation that is related to maximum likelihood supertrees), and we prove that finding the RFS of three input trees is NP-hard. Exact-RFS-2 is available in open source form on Github at https://github.com/yuxilin51/GreedyRFS.

Recursive MAGUS: scalable and accurate multiple sequence alignment

Multiple sequence alignment tools struggle to keep pace with rapidly growing sequence data, as few methods can handle large datasets while maintaining alignment accuracy. We recently introduced MAGUS, a new state-of-the-art method for aligning large numbers of sequences. In this paper, we present a comprehensive set of enhancements that allow MAGUS to align vastly larger datasets with greater speed. We compare MAGUS to other leading alignment methods on datasets of up to one million sequences. Our results demonstrate the advantages of MAGUS over other alignment software in both accuracy and speed. MAGUS is freely available in open-source form at https://github.com/vlasmirnov/MAGUS.

Bactrian χϸονο ‘(calendar) year, (regnal) year’

Since H. Humbach's Baktrische Sprachdenkmäler (Wiesbaden, 1966) the main etymological proposal for Bactrian χϸονο ‘(calendar) year, (regnal) year’ has been A. Thierfelder's suggestion of a loanword from Hellenistic Greek χρόνος ‘time’. In this article the plausibility of this etymology is re-examined, and it is further argued that it should be rejected on the grounds that the formal phonological differences between the potential Hellenistic Greek source form and its presumable loan-adaptation form in Bactrian are inconsistent with what is known of Bactrian diachronic phonology.

Using Robinson-Foulds Supertrees in Divide-and-Conquer Phylogeny Estimation

One of the Grand Challenges in Science is the construction of the Tree of Life , an evolutionary tree containing several million species, spanning all life on earth. However, the construction of the Tree of Life is enormously computationally challenging, as all the current most accurate methods are either heuristics for NP -hard optimization problems or Bayesian MCMC methods that sample from tree space. One of the most promising approaches for improving scalability and accuracy for phylogeny estimation uses divide-and-conquer: a set of species is divided into overlapping subsets, trees are constructed on the subsets, and then merged together using a ``supertree method". Here, we present Exact-RFS-2, the first polynomial-time algorithm to find an optimal supertree of two trees, using the Robinson-Foulds Supertree (RFS) criterion (a major approach in supertree estimation that is related to maximum likelihood supertrees), and we prove that finding the RFS of three input trees is NP -hard. We also present GreedyRFS (a greedy heuristic that operates by repeatedly using Exact-RFS-2 on pairs of trees, until all the trees are merged into a single supertree). We evaluate Exact-RFS-2 and GreedyRFS, and show that they have better accuracy than the current leading heuristic for RFS. Exact-RFS-2 and GreedyRFS are available in open source form on Github at github.com/yuxilin51/GreedyRFS

Simulation and Analysis of Building Energy Consumption in Port passenger Stations

Port passenger station buildings (PPSD) are an important part of transportation buildings in China, which is characterized by large human flow, long operating time, high load of equipment and lighting. The characteristics and functions of PPSD lead to the high energy consumption. However, the energy consumption analysis of PPSD was deficient. In this paper, the characteristics of energy consumption of port passenger stations in cold regions and hot summer and warm winter regions in China were analyzed. Based on eQUEST, the building models of port passenger stations are established. The influencing factors of the building energy consumption were analyzed through orthogonal experiment with SPSS. Results show that the factors such as summer indoor design temperature, heat source form, air conditioning form, window to wall ratio and lighting control mode are the key factors affecting the energy consumption of port passenger station.

wQFM: Statistically Consistent Genome-scale Species Tree Estimation from Weighted Quartets

MotivationSpecies tree estimation from genes sampled from throughout the whole genome is complicated due to the gene tree-species tree discordance. Incomplete lineage sorting (ILS) is one of the most frequent causes for this discordance, where alleles can coexist in populations for periods that may span several speciation events. Quartet-based summary methods for estimating species trees from a collection of gene trees are becoming popular due to their high accuracy and statistical guarantee under ILS. Generating quartets with appropriate weights, where weights correspond to the relative importance of quartets, and subsequently amalgamating the weighted quartets to infer a single coherent species tree allows for a statistically consistent way of estimating species trees. However, handling weighted quartets is challenging.ResultsWe propose wQFM, a highly accurate method for species tree estimation from multi-locus data, by extending the quartet FM (QFM) algorithm to a weighted setting. wQFM was assessed on a collection of simulated and real biological datasets, including the avian phylogenomic dataset which is one of the largest phylogenomic datasets to date. We compared wQFM with wQMC, which is the best alternate method for weighted quartet amalgamation, and with ASTRAL, which is one of the most accurate and widely used coalescent-based species tree estimation methods. Our results suggest that wQFM matches or improves upon the accuracy of wQMC and ASTRAL.AvailabilitywQFM is available in open source form at https://github.com/Mahim1997/wQFM-2020.