A least square method based model for identifying protein complexes in protein-protein interaction network

Protein complex formed by a group of physical interacting proteins plays a crucial role in cell activities. Great effort has been made to computationally identify protein complexes from protein-protein interaction (PPI) network. However, the accuracy of the prediction is still far from being satisfactory, because the topological structures of protein complexes in the PPI network are too complicated. This paper proposes a novel optimization framework to detect complexes from PPI network, named PLSMC. The method is on the basis of the fact that if two proteins are in a common complex, they are likely to be interacting. PLSMC employs this relation to determine complexes by a penalized least squares method. PLSMC is applied to several public yeast PPI networks, and compared with several state-of-the-art methods. The results indicate that PLSMC outperforms other methods. In particular, complexes predicted by PLSMC can match known complexes with a higher accuracy than other methods. Furthermore, the predicted complexes have high functional homogeneity

The influence of H2O or/and O2 introduction during the low-temperature gas-phase sulfation of organic COS + CS2 on the conversion and deposition of sulfur-containing species in the sulfated CeO2-OS catalyst for NH3-SCR

Herein, the typical components of blast furnace gas, including H O and O , were introduced to improve the NH -SCR activity of the sulfated CeO -OS catalyst during the gas-phase sulfation of organic COS + CS at 50 °C. The characterization results demonstrate that the introduction of O or H O during gas-phase sulfation enhances the conversion of organic COS + CS on a cubic fluorite CeO surface and reduces the formation of sulfur and sulfates in the catalyst, but decreases the BET surface area and pore volume of the sulfated CeO -OS catalyst. However, the introduction of O or H O during the gas-phase sulfation increases the molar ratios of Ce /(Ce + Ce ) and O /(O + O + O ) on the sulfated CeO -OS catalyst surface, thus promoting the formation of surface oxygen vacancies and chemisorbed oxygen, and these properties of the catalyst are further enhanced by the co-existence of O and H O. Furthermore, the reduction of sulfates formed under the action of O or H O decreases the weak acid sites of the sulfated CeO -OS catalyst, but the few and highly dispersive sulfates present stronger reducibility, and the proportion of medium-strong acid sites of the catalyst increases. These factors help to improve the NH -SCR activity of the sulfated CeO -OS catalyst. Thus, there exists a synergistic effect of H O and O introduction during gas-phase sulfation on the physical-chemical properties and catalytic performance of the sulfated CeO -OS catalyst by organic COS + CS at 50 °C

Exoplanets in the Antarctic Sky I. The first data release of AST3-II (CHESPA) and new found variables within the southern CVZ of TESS

Located at Dome A, the highest point of the Antarctic plateau, the Chinese Kunlun station is considered to be one of the best ground-based photometric sites because of its extremely cold, dry, and stable atmosphere. A target can be monitored from there for over 40 days without diurnal interruption during a polar winter. This makes Kunlun station a perfect site to search for short-period transiting exoplanets. Since 2008, an observatory has existed at Kunlun station, and three telescopes are working there. Using these telescopes, the AST3 project has been carried out over the last 6 yr with a search for transiting exoplanets as one of its key programs (CHESPA). In the austral winters of 2016 and 2017, a set of target fields in the southern continuous viewing zone (CVZ) of TESS were monitored by the AST3-II telescope. In this paper, we introduce the CHESPA and present the first data release containing photometry of 26,578 bright stars (m(i) <= 15). The best photometric precision at the optimum magnitude for the survey is around 2 mmag. To demonstrate the data quality, we also present a catalog of 221 variables with a brightness variation greater than 5 mmag from the 2016 data. Among these variables, 179 are newly identified periodic variables not listed in the AAVSO database (https://www.aavso.org/), and 67 are listed in the Candidate Target List. These variables will require careful attention to avoid false-positive signals when searching for transiting exoplanets. Dozens of new transiting exoplanet candidates will be released in a subsequent paper

Exoplanets in the Antarctic Sky. II. 116 Transiting Exoplanet Candidates Found by AST3-II (CHESPA) within the Southern CVZ of TESS

We report first results from the CHinese Exoplanet Searching Program from Antarctica (CHESPA)-a wide-field high-resolution photometric survey for transiting exoplanets carried out using telescopes of the AST3 (Antarctic Survey Telescopes times 3) project. There are now three telescopes (AST3-I, AST3-II, and CSTAR-II) operating at Dome A-the highest point on the Antarctic Plateau-in a fully automatic and remote mode to exploit the superb observing conditions of the site, and its long and uninterrupted polar nights. The search for transiting exoplanets is one of the key projects for AST3. During the austral winters of 2016 and 2017 we used the AST3-II telescope to survey a set of target fields near the southern ecliptic pole, falling within the continuous viewing zone of the TESS mission. The first data release of the 2016 data, including images, catalogs, and light curves of 26,578 bright stars (7.5 <= m(i) <= 15), was presented in Zhang et al. The best precision, as measured by the rms of the light curves at the optimum magnitude of the survey (m(i) = 10), is around 2 mmag. We detect 222 objects with plausible transit signals from these data, 116 of which are plausible transiting exoplanet candidates according to their stellar properties as given by the TESS Input Catalog, Gaia DR2, and TESS-HERMES spectroscopy. With the first data release from TESS expected in late 2018, this candidate list will be timely for improving the rejection of potential false-positives

A Fast Circle Detector with Efficient Arc Extraction

Circle detection is a crucial problem in computer vision and pattern recognition. Improving the accuracy and efficiency of circle detectors has important scientific significance and excellent application value. In this paper, we propose a circle detection method with efficient arc extraction. In order to reduce edge redundancy and eliminate crossing points, we present an edge refinement algorithm to refine the edges into single-pixel-wide branchless contour curves. To address the contour curve segmentation difficulty, we improved the CTAR (Chord to Triangular Arms Ratio) corner detection method to enhance corner point detection and segment the contour curves based on corner points. Then, we used the relative position constraint of arcs to improve the circle detection accuracy further. Finally, we verified the feasibility and reliability of the proposed method by comparing our approach with five other methods using three datasets. The experimental results showed that the presented method had the advantages of anti-obscuration, anti-defect, and real-time performance over other methods

A Semi-Supervised Semantic Segmentation Method for Blast-Hole Detection

The goal of blast-hole detection is to help place charge explosives into blast-holes. This process is full of challenges, because it requires the ability to extract sample features in complex environments, and to detect a wide variety of blast-holes. Detection techniques based on deep learning with RGB-D semantic segmentation have emerged in recent years of research and achieved good results. However, implementing semantic segmentation based on deep learning usually requires a large amount of labeled data, which creates a large burden on the production of the dataset. To address the dilemma that there is very little training data available for explosive charging equipment to detect blast-holes, this paper extends the core idea of semi-supervised learning to RGB-D semantic segmentation, and devises an ERF-AC-PSPNet model based on a symmetric encoder–decoder structure. The model adds a residual connection layer and a dilated convolution layer for down-sampling, followed by an attention complementary module to acquire the feature maps, and uses a pyramid scene parsing network to achieve hole segmentation during decoding. A new semi-supervised learning method, based on pseudo-labeling and self-training, is proposed, to train the model for intelligent detection of blast-holes. The designed pseudo-labeling is based on the HOG algorithm and depth data, and proved to have good results in experiments. To verify the validity of the method, we carried out experiments on the images of blast-holes collected at a mine site. Compared to the previous segmentation methods, our method is less dependent on the labeled data and achieved IoU of 0.810, 0.867, 0.923, and 0.945, at labeling ratios of 1/8, 1/4, 1/2, and 1

Flow and Heat Transfer Characteristics of S-CO₂ in a Vertically Rising Y-Tube

The supercritical carbon dioxide Brayton cycle has gradually become a research focus, but we also see a deficiency in research related to the flow and heat transfer characteristics of S-CO2 boiler staves with high parameters. In this paper, the flow and heat transfer of supercritical carbon dioxide is investigated in a 1000 MW supercritical boiler cooled wall tube in the parameters of a pressure of 30.42 MPa, a mass flux of 1592~2207 kg/(m2·s), and a heat flux of 39.8~71.2 kw/m2; a three-dimensional model of supercritical CO2 fluid in the cooling wall tube is established with the RNG k-epsilon turbulence model. Numerical simulations are carried out according to the following boundary conditions: an adiabatic half side, a heated half side, and a Y-type three-way two-to-one. The effects of the mass flux, inlet temperature, and heat flux on the flow and heat transfer characteristics in the Y-tube are analyzed, which exerts great influence on the research of S-CO2 boiler stave thermodynamics

Threshold Segmentation and Length Measurement Algorithms for Irregular Curves in Complex Backgrounds

It is an urgent problem to know how to quickly and accurately measure the length of irregular curves in complex background images. To solve the problem, we first proposed a quasi-bimodal threshold segmentation (QBTS) algorithm, which transforms the multimodal histogram into a quasi-bimodal histogram to achieve a faster and more accurate segmentation of the target curve. Then, we proposed a single-pixel skeleton length measurement (SPSLM) algorithm based on the 8-neighborhood model, which used the 8-neighborhood feature to measure the length for the first time, and achieved a more accurate measurement of the curve length. Finally, the two algorithms were tested and analyzed in terms of accuracy and speed on the two original datasets of this paper. The experimental results show that the algorithms proposed in this paper can quickly and accurately segment the target curve from the neon design rendering with complex background interference and measure its length

Synthesis of Cost-Optimal Heat Exchanger Networks Using a Novel Stochastic Algorithm and a Modified Stage-Wised Superstructure

Facing the current energy structure urgently needs to be transformed, heat exchanger network (HEN) can implement heat recovery and cost reduction by the arrangement for heat exchanges between cold and hot streams. The plenty of integer and continuous variables involved in HEN synthesis cause the results to be easily trapped in local optima. To avoid this situation, the mechanism of accepting imperfect solutions is added in a novel algorithm called Random Walk Algorithm with Compulsive Evolution. However, several potential solutions maybe abandoned by accepting imperfect solutions. To maintain the global searching ability, and at the same time, protecting the potential solutions during the optimization process, the limitations of accepting imperfect solutions are investigated in this work, then a back substitution strategy and elite optimization strategy based on algorithm are proposed. The former is to identify and adjust the inferior individuals in long-term stagnation while the latter is to keep and perform a fine search for the better solutions. Furthermore, a modified stage-wised superstructure is also developed to implement the flexible placement of utilities, which efficiently enlarges the solution domain. The validation of strategies and model is implemented by three cases, the results are lower, with 2219 $/year, 1280$/year, and 2M $/year than the best published result, revealing the strong abilities of the proposed method in designing more economical HENs

Fluid Flow and Heat Transfer in Microchannel Heat Sinks: Modelling review and recent progress

© 2022 Elsevier Ltd. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.tsep.2022.101203Nowadays, microchannel has been widely utilized in various multidisciplinary fields, and as a consequence, some new and different requirements for microchannels in the process of practical application are required, such as structure, working fluid, and operating conditions, etc. This article reviews the current research achievement of microchannels, as well as the thermodynamic research on microchannels with different structures in the past five years, but mainly focuses on the numerical methods. The purpose of this review article aims to summarize a comprehensive overview of the latest developments of numerical methods in microchannel heat sinks, as well as to provide a useful benchmark for future research. The present article reviews straightforward on the most commonly used numerical methods for solving governing equations and optimizing data, including conventional computational fluid dynamics (CFD) simulation methods, molecular dynamics simulation (MDS), Lattice Boltzmann methods (LBM), direct simulation Monte Carlo (DSMC), and other techniques such as machine learning (ML) approach, artificial neural network (ANN) method, genetic algorithm (GA), Taguchi algorithm (TA), as well as optimisation methods. This review will not only help to understand the physical mechanism of microchannels in different application fields but also help to fill in the gaps in related research and provide research methods for future numerical studies.Peer reviewe