bluuebunny/crossref_metadata_2025_split huggingface dataset:
CNN-Based Surface Defect Detection of Smartphone Protective Screen
Authors: Chen Hailang 
Published: August 2020

Abstract
With the advancement of the intelligent era of Industry 4.0, in order to solve the problem of low-efficiency and high miss-detection rate using manual visual inspection for the detection of surface defects on smartphone protective screens, surface defects on smartphone protective screens based on convolutional neural networks are introduced as the detection method. This detection method exhibits excellent feature extraction capabilities and powerful target classification performance during the preprocessing, model design, model training, and detection of smartphone protective screens images. Experimental results show that the detection method can achieve accurate detection of defects such as punctures, bright spots, and scratches on the surface of the smartphone protective screens. The detection verification rate is as high as 98%, and the accuracy is high, which meets the actual needs of enterprises.
Evolutionarily stable strategy analysis of Quantitative investment strategy
Authors: Li Yicun , Xiao Weilin , Teng Yuanyang 
Published: August 2020

Abstract
Based on Chinese Stock Index Future, this study focuses on two types of market price changes, trends and shake. How investors’ strategies evolve to influence the market from trend to shake or from shake to trend to obtain the “evolutionary stable strategy” (ESS) of the market is conducted. The results show that trend strategies are more favourable in most market environments, and find a single strategy cannot survive in the market, and trading frequency should be appropriately reduced to reduce transaction costs, so as to survive in the market for a long time.
Research on System Log Analysis Based on Bidirectional LSTM
Authors: Zhao Jiantao , Wang Ke 
Published: August 2020

Abstract
System logs often record important information in the system and are an important source of information on user behavior, system abnormalities, and system operating conditions. With the stacking of system time and the surge of users, the data generated by the system has also exploded. In order to achieve intelligent system management, this paper proposes a system log analysis method based on bidirectional LSTM. The method uses neural networks to extract keywords and data vectors based on the logs generated by the system, and uses the data sequence corresponding to the keywords as input to make judgments about system abnormalities. The experimental results show that the method can effectively judge the abnormality of the system.
Research on Missile Intelligent Penetration Based on Deep Reinforcement Learning
Authors: Lei Sen , Lei YongLin , Zhu Zhi 
Published: August 2020

Abstract
Penetration capability is one of the most critical measures for assessing the effectiveness of missile systems. In the face of an increasingly sophisticated antimissile system, this article explores the use of artificial intelligence to achieve missile intelligent penetration. The applicability of the deep reinforcement learning method to the missile intelligent penetration problem is elaborated. The Actor-Critic deep reinforcement learning algorithm is studied. The deep reinforcement learning environment model for the missile penetration scenario is developed based on the generic combat effectiveness simulation system, WESS. A multi-sample collaborative training method is designed. On this basis, a missile intelligent penetration training system framework based on WESS and deep reinforcement learning is proposed to guide the missile penetration intelligence learning.
Attention-BLSTM-CRF Based Method for Named Entity Recognition in Judicial Domain
Authors: Wang Chen , Li Bo , Zhang Wenjing 
Published: August 2020

Abstract
Although the texts in the judicial field are relatively standardized, the entity categories are rich and the structure is different, and the entity expression is special in some legal documents, electronic files and guiding cases. In order to improve the effect of named entity recognition in the field of justice, this paper entity type can be divided into four categories, and presents a model of named entity recognition based on attention mechanism, structure improvement of input vector fusion CNN embed mode, BLSTM neural network at the same time we constantly study the characteristics of the context, finally by CRF decoding output sequence. The experimental results show that the method is helpful to the application research in the judicial field, and the experiment on the self-built corpus test set has achieved a good entity identification effect.
Short Text Classification and Clustering based Mobile Application Traffic Identification Method
Authors: Li Yuanhao , Chen Shuhui , Zhao Shuang 
Published: August 2020

Abstract
With the rapid development of mobile network, mobile traffic accounts for a large proportion of network traffic nowadays, and mobile application traffic identification is becoming increasingly important in network security. For mobile application traffic identification, recent works have focused on proposing supervised classifiers that have shown promising performance. However, it is difficult to obtain labeled traffic in practice and most of the traffic is unlabeled. In this paper, we propose a semi-supervised mobile application traffic identification method based on short text classification and clustering, which requires only a small number of labeled samples to classify traffic. First, plain text in mobile traffic is regarded as short text and its features are extracted using a short text classification algorithm. Then K-Means++ is used to cluster the samples and give the predictions. Experimental results show that the proposed method is more effective than the semi-supervised traffic identification methods that use statistical features.
Robustness Analysis of Interdependent Networks under Grey Information Attack
Authors: Wang Zhe , Li Jianhua , Kang Dong , Ran Haodan 
Published: August 2020

Abstract
In modern society, many infrastructures are interdependent owing to functional and logical relations among components in different systems, and they can be characterized as complex interdependent networks. Interdependency makes network so fragile that even a slight initial disturbance may lead to a cascading failure of the entire systems. Previous research on interdependent networks has focused on two types of initial attack: random attack and degree-based targeted attack. In this paper, a grey information attack strategy with the information incompleteness and complexity by adjusting the tunable parameters was designed, and a cascading failure process is studied considering their effects on failure propagation. Moreover, the robustness of two types of interdependent networks (BA - BA, ER - ER) is evaluated under grey attacking strategies. Interesting conclusions could be obtained. The simulation results show that the proposed method can identify the key nodes more effectively, and the robustness of the network decreases with the increase of the grey attack coefficient. In addition, the relation between weight coefficient and robustness under various strategy is quite different. Our results can be very useful for safety design and protection of interdependent networks.
Improving Robustness and Defense with Signal Augmentation for Modulation Identification
Authors: Zou Chuanjin , Yu Chenqiu , Guo Jibin , Guo Ronghai , Pang Junzhong , Zeng Weijun 
Published: August 2020

Abstract
Based on the Convolutional Neural Network, we consider finding an automatic recognition method of signal modulation. Electronic warfare (EW) is one of the most important forms of war, and the outcome of the war depends largely on it. Fast, accurate, secure and continuous communication is increasingly important, and the identification of signal modulation is one of the key links to ensure communication. The traditional recognition method is artificial recognition, which is inefficient and error-prone. In this paper, aiming at several common modulation signals, the automatic recognition system of modulation signals based on Convolu-tional Neural Network is designed by analyzing mathematical models and important parameters, then upgrade and improve the feasibility and effectiveness of the project.
Functionalized PEG-oligo(L-lysine)-PCL micelle system for the delivery of bioactive agents based on pH-sensitive degradation
Authors: Kang Jungkyu , Jeon Seong Ik , Chung In Jae , Ahn Cheol-Hee 
Published: August 2020

Abstract
Methoxy poly(ethylene glycol) amine with a molecular weight of 5 K and ϵ-caprolactone with a molecular weight of 3 K were conjugated to five lysine residues with molecular weight and M
w/M
n of 9.6 K and 1.04, respectively. The shift of peak molecular weight and narrow molecular weight distribution in a gel permeation chromatography (GPC) trace without any noticeable shoulder as well as 1H nuclear magnetic resonance analysis confirmed the successful synthesis of the copolymer. Polymeric micelles, of size around 60 nm, were formed by dialysis and crosslinked micelles (CMs) were prepared by adding a crosslinker, terephthalaldehyde, to generate weak acid-labile benzoic-imine bonds in the interface of the micelle-forming amphiphilic copolymer. The critical micelle concentrations of non-crosslinked micelles and CMs were determined to be 4.26 × 10–2 mg ml−1 and 7.01 × 10–3 mg ml−1, respectively. The hydrolysis rate of the CMs is highly pH-dependent and much more rapid at mild acid than physiological conditions. Doxorubicin was successfully loaded into the CMs and a controlled pH-dependent release behavior was observed. The enhanced micelle stability opens a way for preparing long-circulating delivery systems encapsulating poorly water-soluble drugs.
Material characterization and optimization of CNT-reinforced aluminum (AA7075) functionally graded material processed by ultrasonic cavitation
Authors: Srinivas P N Sudhakar , Babu P Ravindra , Balakrishna B 
Published: August 2020

Abstract
Conventional materials that have to function in rigorous conditions can exhibit extraordinary properties in tough conditions just from reinforcement with carbon nanotubes (CNTs). However, the manufacturing methods available for CNTs are complex and cumbersome, in terms of the process parameters. In our research work, we have intensively concentrated on the synthesis of functionally-graded material reinforced with long carbon fiber nanotubes for cylinder linings in a four-wheeler. The matrix constituent under consideration is AA7075 and the reinforcing constituents are titanium nitride (TiN) and multi-walled carbon nanotubes. These constituents are blended as per defined mixture rules, having matrix mass percentages of 100%, 95%, 90%, 85% and 80%, TiN and CNT mass percentages of 0%, 2.5%, 5%, 7.5%, and 10% are taken in equal proportions. The specimens are synthesized using centrifugal casting and layer-by-layer deposition of the matrix and the reinforcing constituents from the bottom to the top layer, as per the ASTM G33 standard. The uniform dispersion of the CNTs is assured with the aid of an ultrasonic cavitation methodology. The specimens are tested for their mechanical properties, such as tensile and impact strength, and macro- and microhardness. Scanning electron microscopy (SEM) together with energy-dispersive x-ray analysis (EDS) are utilized to verify a continuous phase and uniform dispersion of the carbon nanotubes and the titanium nitride within the matrix constituent. The results show that there is strong interfacial bonding with respect to layers and the interstitial region. Tribological characterization shows that the maximum wear rate, frictional force and coefficient of friction attained values of 100 microns, 15.6 N and 0.15 respectively, at a constant speed of 675 rpm and a wear time of 30 min, over a sliding distance of 9500 m. ANOVA and multiple linear regression are conducted to optimize and standardize the process parameters of the manufacturing process.
Malaria Screening Using Front-Line Loop-Mediated Isothermal Amplification
Authors: McGinnis Eric , Chan Geoffrey , Hudoba Monika , Markin Todd , Yakimec Jim , Roland Kristine 
Published: August 2020

Abstract
Objectives: We implemented front-line loop-mediated isothermal amplification (LAMP)–based malaria screening in our nonendemic multicenter health region to reduce reliance on microscopy without sacrificing diagnostic efficiency. We aimed to evaluate changes in test volumes, positivity rates, turnaround times, and approximate labor time savings resulting from implementation of LAMP-based malaria testing to assess the efficacy of the novel testing algorithm in our regional hub-and-spoke testing model.
Methods: We reviewed data generated from institutional malaria testing between 2016 and 2019, having implemented LAMP in October 2018 as a front-line screening test for all malaria investigations from our hub facility and investigations from satellite facilities with negative rapid diagnostic tests (RDTs) and microscopy.
Results: Blood film microscopy and RDT workloads decreased substantially in the year following LAMP implementation (by 90% and 46%, respectively,) despite similar numbers of patients tested and positivity rates for malaria compared with historical data. LAMP turnaround times (TATs) were comparable to historical TATs for RDTs, and TATs for RDTs and thick films did not increase with the change in workflow.
Conclusions: LAMP was successfully implemented in our multicenter health region malaria diagnostic algorithm, significantly reducing reliance on microscopic evaluations and RDT and providing substantial labor time savings without compromising TATs.
Evaluation of post–flexible cystoscopy urinary tract infection rates
Authors: Cusumano Jaclyn A , Hermenau Matthew , Gaitanis Melissa , Travis Michelle , LaPlante Kerry L , Tran Timothy Y , McConeghy Kevin W 
Published: August 2020

Abstract

Purpose
The risk of urinary tract infection (UTI) development after flexible cystoscopy (FC) is not well described. It remains difficult to assess the role of pre-FC antimicrobial prophylaxis to reduce UTI risk.

Methods
In fall 2017, the urology service at the Providence Veterans Affairs Medical Center implemented routine oral antimicrobial prophylaxis in its outpatient FC clinic. Outpatients were randomly selected for a retrospective chart review to compare patients who received pre-FC antimicrobials (cefuroxime 500 mg tablet or sulfamethoxazole/trimethoprim [800 mg/160 mg] tablet) and those who underwent FC prior to fall 2017 and did not receive prophylaxis. The primary outcome was presence of symptomatic UTI within 30 days post FC. Secondary outcomes included symptomatic UTI that met colony-forming unit (CFU)/mL guideline requirements, and UTI treatment received. Potential risk factors for UTI were also assessed.

Results
A total of 296 patients were included in the final analysis: 139 who did not receive and 157 who received a prophylactic antimicrobial before FC. Rates of symptomatic UTI, symptomatic UTI meeting CFU/mL guideline requirements, and postprocedure treatment for UTI were similar with and without antimicrobial prophylaxis (2.5% vs 2.2% [P &gt; 0.99], 1.9% vs 1.4% [P &gt; 0.99], and 2.5% vs 4.3% [P = 0.53], respectively). The mean number of days from FC to the start of UTI treatment was 7.9 (range, 1-18 days). Age over 65 years was the only risk factor present in all patients with a post-FC UTI, irrespective of antimicrobial prophylaxis.

Conclusion
The rate of post-FC symptomatic UTI was lower than rates previously described in the literature. The role of antimicrobial prophylaxis prior to FC warrants further exploration.
Species-specific flowering phenology responses to experimental warming and drought alter herbaceous plant species overlap in a temperate–boreal forest community
Authors: Rice Karen E , Montgomery Rebecca A , Stefanski Artur , Rich Roy L , Reich Peter B 
Published: August 2020

Abstract

Background and Aims
Warmer temperatures and altered precipitation patterns are expected to continue to occur as the climate changes. How these changes will impact the flowering phenology of herbaceous perennials in northern forests is poorly understood but could have consequences for forest functioning and species interactions. Here, we examine the flowering phenology responses of five herbaceous perennials to experimental warming and reduced summer rainfall over 3 years.

Methods
This study is part of the B4WarmED experiment located at two sites in northern Minnesota, USA. Three levels of warming (ambient, +1.6 °C and +3.1 °C) were crossed with two rainfall manipulations (ambient and 27 % reduced growing season rainfall).

Key Results
We observed species-specific responses to the experimental treatments. Warming alone advanced flowering for four species. Most notably, the two autumn blooming species showed the strongest advance of flowering to warming. Reduced rainfall alone advanced flowering for one autumn blooming species and delayed flowering for the other, with no significant impact on the three early blooming species. Only one species, Solidago spp., showed an interactive response to warming and rainfall manipulation by advancing in +1.6 °C warming (regardless of rainfall manipulation) but not advancing in the warmest, driest treatment. Species-specific responses led to changes in temporal overlap between species. Most notably, the two autumn blooming species diverged significantly in their flowering timing. In ambient conditions, these two species flowered within the same week. In the warmest, driest treatment, flowering occurred over a month apart.

Conclusions
Herbaceous species may differ in how they respond to future climate conditions. Changes to phenology may lead to fewer resources for insects or a mismatch between plants and pollinators.
The Value of Integrating Fluorescent Imaging and Immunohistochemistry for Future Anatomical Studies in Aesthetic Surgery: Lessons From the Cerebrospinal Fluid Circulatory System of Human Nerves and Brain
Authors: Pessa Joel E , Smith Douglas , Sieber David A , Kenkel Jeffrey M , Arbique Gary , Nahai Foad , Heldermon Coy D , Scott Edward W 
Published: August 2020

Abstract

Background
During their work on the cerebrospinal fluid (CSF) circulatory system of human nerves and brain, the authors applied imaging and tissue techniques that complemented basic anatomical dissection.

Objectives
The authors sought to show how integrating fluorescent imaging and basic immunohistochemistry (IHC) with facial anatomy can address current problems in aesthetic surgery.

Methods
The authors developed an algorithm and a set of principles from their work on the CSF circulatory system and applied these to 3 problems in aesthetic surgery: the functional anatomy of the vermilion-cutaneous junction; chemosis; and the functional anatomy of periosteal fixation.

Results
Integrating fluorescent imaging and IHC with anatomical dissection characterizes structural and functional anatomy. Fluorescent imaging helps to identify and locate easily missed structures. IHC defines cell type and function. The vermilion-cutaneous junction is defined by a major lymphatic vessel. Lymphatic flow from the medial limbus to the lateral canthus suggests the etiology of chemosis. Periosteal sites of fixation prevent shear where dural CSF vessels drain directly to subcutaneous lymphatics.

Conclusions
Integrating anatomical dissection with fluorescent imaging and basic IHC characterizes structural and functional anatomy and helps to better understand many problems encountered in aesthetic surgery.
CONCUR: quick and robust calculation of codon usage from ribosome profiling data
Authors: Frye Michaela , Bornelöv Susanne 
Published: August 2020

Abstract

Summary
CONCUR is a standalone tool for codon usage analysis in ribosome profiling experiments. CONCUR uses the aligned reads in BAM format to estimate codon counts at the ribosome E-, P- and A-sites and at flanking positions.

Availability and implementation
CONCUR is written in Perl and is freely available at https://github.com/susbo/concur.

Supplementary information
Supplementary data are available at Bioinformatics online.
Evidence of introgression in endemic frogs from thecampo rupestrecontradicts the reduced hybridization hypothesis
Authors: Magalhães Rafael F , Lemes Priscila , Santos Marcus Thadeu T , Mol Rafael M , Ramos Elisa K S , Oswald Caroline B , Pezzuti Tiago L , Santos Fabrício R , Brandão Reuber A , Garcia Paulo C A 
Published: August 2020

AbstractThe campo rupestre ecosystem is considered an old, climatically buffered, infertile landscape. As a consequence, long-term isolation is thought to have played an important role in the diversification of its biota. Here, we tested for hybridization between two endemic leaf frogs from the campo rupestre. We used sequence markers and coalescent models to verify haplotype sharing between the species, to test the existence and direction of gene flow, and to reconstruct the spatiotemporal dynamics of gene flow. Additionally, ecological niche modelling (ENM) was used to assess for potential co-occurrence by overlapping the climatic niche of these species since the middle Pleistocene. We found haplotype sharing and/or lack of differentiation in four nuclear fragments, one of them associated with introgression. The coalescent models support introgressive hybridization unidirectionally from Pithecopus megacephalus to P. ayeaye, occurring ~300 kya. ENM corroborates this scenario, revealing areas of potential environmental niche overlap for the species at about 787 kya. These results contradict the expectation of reduced hybridization, while ENM suggests climatic fluctuation rather than stability for the two species. The reduced hybridization hypothesis needs to be further investigated because our results suggest that it may have unrealistic premises at least for animals.
On the use of a penalized quasilikelihood information criterion for generalized linear mixed models
Authors: Hui Francis K C 
Published: August 2020

SummaryInformation criteria are commonly used for joint fixed and random effects selection in mixed models. While information criteria are straightforward to implement, a major difficulty in applying them is that they are typically based on maximum likelihood estimates, but calculating such estimates for one candidate mixed model, let alone multiple models, presents a major computational challenge. To overcome this hurdle, we study penalized quasilikelihood estimation and use it as the basis for performing fast joint selection. Under a general framework, we show that penalized quasilikelihood estimation produces consistent estimates of the true parameters. We then propose a new penalized quasilikelihood information criterion whose distinguishing feature is the way it accounts for model complexity in the random effects, since penalized quasilikelihood estimation effectively treats the random effects as fixed. We demonstrate that the criterion asymptotically identifies the true set of important fixed and random effects. Simulations show that the quasilikelihood information criterion performs competitively with and sometimes better than common maximum likelihood information criteria for joint selection, while offering substantial reductions in computation time.