One-Shot M-Array Pattern Based on Coded Structured Light for Three-Dimensional Object Reconstruction

Pattern encoding and decoding are two challenging problems in a three-dimensional (3D) reconstruction system using coded structured light (CSL). In this paper, a one-shot pattern is designed as an M-array with eight embedded geometric shapes, in which each 2 × 2 subwindow appears only once. A robust pattern decoding method for reconstructing objects from a one-shot pattern is then proposed. The decoding approach relies on the robust pattern element tracking algorithm (PETA) and generic features of pattern elements to segment and cluster the projected structured light pattern from a single captured image. A deep convolution neural network (DCNN) and chain sequence features are used to accurately classify pattern elements and key points (KPs), respectively. Meanwhile, a training dataset is established, which contains many pattern elements with various blur levels and distortions. Experimental results show that the proposed approach can be used to reconstruct 3D objects.

Critical Chains and Its Randomness Study

It has been widely accepted in the academic community that the Critical Chain Method (CCM) has significant advantages over the Critical Path Method (CPM) in solving the problem with resource constraints. However, this paper conducted a study on comparing the two methods of Critical Chain Method and Critical Path Method, and found that the only difference between those two methods lies in how to determine the priority of resources allocating, and on the assumption of not setting buffer zone, those two methods have no essential distinctions at all. By establishing the relationship between CCM and CPM, this paper also enriched and improved CCM to some extent, and pointed out that the buffer zone setting in CCM is merely subjective and short of scientificity. In the meantime, for the problem of unclear representation of critical chains, it proposed two ways of representing critical chains and related rules to follow. To verify the conclusion of this paper, further detailed case study of comparing CCM and CPM was performed. Affected by various uncertain factors, the project construction sequence is random, the total construction duration is random, and the critical chain is also random, so it is unable to determine how to direct construction. Aiming at the randomness of the critical chain, this article analyzed various uncertain factors of the critical chain, and on the basis of solving the critical chain sequence time, it proposed the approach to determine the completion probability of the total construction duration and control the construction of the critical chain to direct the construction, in the meantime, the inverse algorithm was adopted to determine of the construction duration under the condition of required completion probability.

Synthesis of Polyethers from Epoxides via Binary Organocatalyst System

The development of “metal-free” catalyst systems with high efficiency for the ring-opening polymerization (ROP) of epoxides, providing polyethers with controlled molecular weights and dispersity as well as desired main-chain sequence...

Stationary Solution of a Fluid Queue Driven by a Queue with Chain Sequence Rates and Controlled Input

A fluid queueing system in which the fluid flow in to the buffer is regulated by the state of the background queueing process is considered. In this model, the arrival and service rates follow chain sequence rates and are controlled by an exponential timer. The buffer content distribution along with averages are found using continued fraction methodology. Numerical results are illustrated to analyze the trend of the average buffer content for the model under consideration. It is interesting to note that the stationary solution of a fluid queue driven by a queue with chain sequence rates does not exist in the absence of exponential timer.

Herbert French (1875–1951) and his differential diagnosis a “work of reference unique in medical literature”

In 1912, the Guy’s Hospital Assistant Physician, Dr Herbert French FRCP, published a magnum opus, An Index of Differential Diagnosis of Main Symptoms by Various Authors. This pioneering work was to formalise the paradigm of a six-chain sequence which underpins best-practice clinical medicine today. That chain comprises: taking a history, examination, compiling a differential diagnosis, tests and investigations, and formulating a diagnosis. Herbert French coined the term “differential diagnosis”; and formalised the earlier developments of Thomas Sydenham (1624 – 1689), Hermann Boerhaave (1668 – 1738) and later(1892), those of Sir William Osler in his The Principles and Practice of Medicine. French placed differential diagnosis formally as the pivot of the sequence of Oslerian medicine which distinguishes modern Western medicine from other healthcare systems. Herbert French was the Goulstonian Lecturer of the Royal College of Physicians (1907), a doctor-soldier in World War I and one of the Royal Physicians to H. M. Household. A prolific writer in the medical press, French updated and personally edited the first six editions of his Differential Diagnosis. The thirteenth edition (1996) was described as a work which “had no parallel” .This work, today in its sixteenth edition, remains “a reference unique in medical literature”.

THE FRACTAL STATISTICAL MODEL OF TRANSREGIONAL AND TRANSNATIONAL E-COMMERCE ENTERPRISES SUPPLY CHAIN SEQUENCE

With the rapid development of world trade exchange, transnational and cross regional e-commerce enterprises have become the heat conductor of trade exchanges among people, organizations and related enterprises of all countries, as well as the important content of high-quality economic development of all countries. Multi-national and transregional e-commerce enterprises have the characteristics of simple circulation structure, simplified transaction cost, high efficiency and rapid evolution in economic and trade activities. However, the traditional transnational and transregional e-commerce enterprises have the disadvantages of slow development and low efficiency in the supply chain. At the same time, there are still many uncertain factors in the corresponding decision sequence. In this paper, the risks faced by cross-border e-commerce supply chain will be comprehensively analyzed and studied. At the same time, the decision-making problem of cross-border e-commerce supply chain sequence will be studied innovatively from two aspects of random uncertainty and fuzzy uncertainty, and a double-layer random expectation model will be established to form a fractal statistical model of supply chain sequence. In this paper, two kinds of sequential strategies are discussed in detail, and a double-layer fuzzy equivalent model is established. Finally, the model is solved by optimization software. The experimental results show that the fractal fractional optimization model proposed in this paper has advantages for the supply chain optimization of multi-national and cross regional e-commerce enterprises.

Geometric potentials from deep learning improve prediction of CDR H3 loop structures

Motivation Antibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining loop (CDR H3) remains a challenge due to its highly diverse set of observed conformations. In recent years, deep neural networks have proven to be effective at capturing the complex patterns of protein structure. This work proposes DeepH3, a deep residual neural network that learns to predict inter-residue distances and orientations from antibody heavy and light chain sequence. The output of DeepH3 is a set of probability distributions over distances and orientation angles between pairs of residues. These distributions are converted to geometric potentials and used to discriminate between decoy structures produced by RosettaAntibody and predict new CDR H3 loop structures de novo. Results When evaluated on the Rosetta antibody benchmark dataset of 49 targets, DeepH3-predicted potentials identified better, same and worse structures [measured by root-mean-squared distance (RMSD) from the experimental CDR H3 loop structure] than the standard Rosetta energy function for 33, 6 and 10 targets, respectively, and improved the average RMSD of predictions by 32.1% (1.4 Å). Analysis of individual geometric potentials revealed that inter-residue orientations were more effective than inter-residue distances for discriminating near-native CDR H3 loops. When applied to de novo prediction of CDR H3 loop structures, DeepH3 achieves an average RMSD of 2.2 ± 1.1 Å on the Rosetta antibody benchmark. Availability and Implementation DeepH3 source code and pre-trained model parameters are freely available at https://github.com/Graylab/deepH3-distances-orientations. Supplementary information Supplementary data are available at Bioinformatics online.

Geometric Potentials from Deep Learning Improve Prediction of CDR H3 Loop Structures

Antibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining loop (CDR H3) remains a challenge due to its highly diverse set of observed conformations. In recent years, deep neural networks have proven to be effective at capturing the complex patterns of protein structure. This work proposes DeepH3, a deep residual neural network that learns to predict inter-residue distances and orientations from antibody heavy and light chain sequence. The output of DeepH3 is a set of probability distributions over distances and orientation angles between pairs of residues. These distributions are converted to geometric potentials and used to discriminate between decoy structures produced by RosettaAntibody. When evaluated on the Rosetta Antibody Benchmark dataset of 49 targets, DeepH3-predicted potentials identified better, same, and worse structures (measured by root-mean-squared distance [RMSD] from the experimental CDR H3 loop structure) than the standard Rosetta energy function for 30, 13, and 6 targets, respectively, and improved the average RMSD of predictions by 21.3% (0.48 Å). Analysis of individual geometric potentials revealed that inter-residue orientations were more effective than inter-residue distances for discriminating near-native CDR H3 loop structures.