Efficient Ship:A Hybrid Deep Learning Framework for Ship Detection in the River

Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters.Current deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of detection.To solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this paper.The core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship objects.We also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model overfitting.We compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called Seaships.Experiments show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.

Action Recognition with Temporal Scale-Invariant Deep Learning Framework

Recognizing actions according to video features is an important problem in a wide scope of applications. In this paper, we propose a temporal scale.invariant deep learning framework for action recognition, which is robust to the change of action speed. Specifically, a video is firstly split into several sub.action clips and a keyframe is selected from each sub.action clip. The spatial and motion features of the keyframe are extracted separately by two Convolutional Neural Networks(CNN) and combined in the convolutional fusion layer for learning the relationship between the features. Then, Long Short Term Memory(LSTM) networks are applied to the fused features to formulate long.term temporal clues. Finally, the action prediction scores of the LSTM network are combined by linear weighted summation. Extensive experiments are conducted on two popular and challenging benchmarks, namely, the UCF.101 and the HMDB51 Human Actions. On both benchmarks, our framework achieves superior results over the state.of.the.art methods by 93.7% on UCF.101 and 69.5% on HMDB51, respectively.

A Method for Service-Oriented Personalized Requirements Analysis

The development of Web service has changed the process of software production, and requirements engineering becomes the key issue of service-oriented software engineering. Meantime, it reduces the degree of difficulty of software production, which facilitates end-users to customize software according to their personalized requirements. The paper proposes a method for service-oriented personalized requirements analysis, which is based on domain goal model and process model. The method can inform users of potential errors in requirements by detecting the correctness of requirements, which is driven by users’ personalized operations on goal models, and customize personalized processes to satisfy users’ requirements by reusing domain processes. The personalized processes are the basis for Web service discovery and composition.

An Approach to Generation of Process-Oriented Requirements Specification

In service-oriented computing, process model may serve as a link to connect users’ requirements with Web Services. In this paper, we propose an approach and related key techniques to generate process-oriented requirements specification from user’s goal. For this purpose, a requirements description language named SORL will be provided to capture users’ requirements. Then, a unified requirements meta-modeling frame RPGS will be used to construct reusable domain assets, which is the basis of generating requirements specifications. Finally, a set of rules are defined to extract process control structures from users’ requirements described with SORL, so that we can convert requirements description into process-oriented requirements specification smoothly.

Positional Isomeric Thiophene-Based π-Conjugated Chromophores:Synthesis,Structure,and Optical Properties

A series of positional isomeric chromophores o-TC,m-TC,and p-TC,in which electron-rich thiophene moieties were connected byπ-conjugated bridges,were divergently synthesized and characterized.Single-crystal X-ray diffraction analysis revealed an intriguing zipper-like packing mode which was adopted by m-TC in the solid state.Subsequently,UV−vis absorption spectra and fluorescence spectra in a series of solvents were investigated.The nearly coplanar para isomer p-TC was found to have the most intense UV−vis absorption,fluorescence emission,and the highest photoluminescence quantum yield.The molecule structure,electronic nature,and origination of the absorption of p-TC were revealed through density functional theory calculations.Interestingly,all three positional isomers exhibited strong and stable electrochemiluminescence emission,which enriched the existing knowledge on the optical properties of thiophene-based oligomers.

Detection of Variants of B.1.617 Lineage from Five Returning Chinese Nationals at a Guangxi-Vietnam Border Port——Guangxi Zhuang Autonomous Region,China,April 2021

A 47-year-old male(Patient A)passenger from Vietnam whose nucleic acid results for coronavirus disease 2019(COVID-19),also known as severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),were positive at Pingxiang Customs on April 29,2021,and subsequently confirmed by Guangxi CDC laboratories on April 30.

The functional analysis of transiently upregulated miR-101 suggests a “braking” regulatory mechanism during myogenesis

Skeletal muscle differentiation is a highly coordinated process that involves many cellular signaling pathways and microRNAs(miRNAs).A group of muscle-specific miRNAs has been reported to promote myogenesis by suppressing key signaling pathways for cell growth.However,the functional role and regulatory mechanism of most non-muscle-specific miRNAs with stage-specific changes during differentiation are largely unclear.Here,we describe the functional characterization of miR-101a/b,a pair of non-muscle-specific miRNAs that show the largest change among a group of transiently upregulated miRNAs during myogenesis in C2C12 cells.The overexpression of miR-101a/b inhibits myoblast differentiation by suppressing the p38/MAPK,Interferon Gamma,and Wnt pathways and enhancing the C/EBP pathway.Mef2a,a key protein in the p38/MAPK pathway,was identified as a direct target of miR-101a/b.Interestingly,we found that the long non-coding RNA(lncRNA)Malat1,which promotes muscle differentiation,interacts with miR-101a/b,and this interaction competes with Mef2a mRNA to relieve the inhibition of the p38/MAPK pathway during myogenesis.These results uncovered a“braking”role in differentiation of transiently upregulated miRNAs and provided new insights into the competing endogenous RNA(ceRNA)regulatory mechanism in myoblast differentiation and myogenesis.

Simultaneous Dehalogenation and Hydrogenation in Sonogashira Coupling

We herein report a simultaneous dehalogenation and hydrogenation(DHH)reaction in Sonogashira coupling involving hexahalogenobenzene C6X6(X=I,Br),in which a halogen on the aryl polyhalide substrate was substituted by a hydrogen atom.First,a steric bulky terminal alkyne 5 was designed and synthesized to study the influence of the reaction conditions(e.g.,catalyst,solvent,temperature)and the halogens on the substrates C6X6 on the DHH reaction.Moreover,based on the optimized conditions,a terminal alkyne 6 with less steric hindrance was further synthesized to investigate the influence of alkyne on the DHH reaction.As a result,two aromatic polyynes 7 and 8 were were further studied and compared.The influence produced by the alkynes and aryl polyhalides substrate provide insights into Sonogashira coupling involving terminal alkyne with huge steric hindrance and polyhalogenated aromatic hydrocarbons.