Miracle-dz/UniFAR-FTUs huggingface dataset:
Shear strengthening of reinforced concrete members with CFRP sheets

In light of their excellent mechanical properties and other advantages over conventional systems for repairing and rehabilitating structures, fibre-reinforced polymers (FRPs) are commonly used to enhance the shear strength of RC members. Because of the relatively recent application of the technique in civil engineering, however, it is addressed in very few recommendations or guides. While the design guides in place contain a single, well defined and generally accepted procedure for calculating bending moments, they collectively propose a wide variety of numerical models for shear, none of which is universally acknowledged. The present article focuses on the calculation of ultimate shear strength and describes an experimental study in that context conducted on reinforced concrete beams retrofitted with unidirectional carbon fibre reinforced polymer (CFRP) fabric.
Metabolomic analysis of a synthetic metabolic switch in Streptomyces coelicolor A3(2)

The global analysis of metabolism by liquid chromatography coupled to mass spectrometry is often hampered by a large amount of biological and technical variability. Here, we introduce an experimental and analytical strategy that can produce robust metabolome profiles in the face of this challenge. By applying a new computational approach based on concordance analysis to an extremely large number of analytical replicates, we are able to show that the overexpression of an antisense non‐coding RNA targeting glutamine synthetase I results in a major reorganization of the metabolism of Streptomyces coelicolor, the model species of antibiotic‐producing bacteria. We identified 97 metabolites with statistically significant reproducible dynamic behavior across the time series. The observed metabolic changes are very rapid, specific and widespread across metabolism, but focus on the nitrogen assimilation pathways. Our results demonstrate the power of highly replicated experimental designs for the robust characterization of metabolite dynamics. The identified global rearrangement of metabolism suggests the usefulness of RNA interference as an efficient strategy to manipulate the physiology of bacteria with wider biotechnological applicability in microorganisms.
Strathprints Institutional Repository Fast Spectral Solution of the Generalized Enskog Equation for Dense Gases

(2015) Fast spectral solution of the generalised Enskog equation for dense gases. Strathprints is designed to allow users to access the research output of the University of Strathclyde. Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (http://strathprints.strath.ac.uk/) and the content of this paper for research or study, educational, or not-for-profit purposes without prior permission or charge. a r t i c l e i n f o a b s t r a c t We propose a fast spectral method for solving the generalized Enskog equation for dense gases. For elastic collisions, the method solves the Enskog collision operator with a computational cost of O (M d−1 N d log N), where d is the dimension of the velocity space, and M d−1 and N d are the number of solid angle and velocity space discretizations, respectively. For inelastic collisions, the cost is N times higher. The accuracy of this fast spectral method is assessed by comparing our numerical results with analytical solutions of the spatially-homogeneous relaxation of heated granular gases. We also compare our results for force-driven Poiseuille flow and Fourier flow with those from molecular dynamics and Monte Carlo simulations. Although it is phenomenological, the generalized Enskog equation is capable of capturing the flow dynamics of dense granular gases, and the fast spectral method is accurate and efficient. As example applications, Fourier and Couette flows of a dense granular gas are investigated. In addition to the temperature profile, both the density and the high-energy tails in the velocity distribution functions are found to be strongly influenced by the restitution coefficient.
Impact of Self-Disclosure on Newcomers in Online Communities

User-generated online communities such as online discussion boards have become an important element of life in information society. However, their success is accompanied with challenges. Particularly, online communities’ high reliance on user-generate content makes ensuring a continuous flow of newcomers vital to their existence. Newcomers face various difficulties due to their lack of commitment and familiarity with the community and many online communities experience high quitting rate of newcomers. In the current work, we aim to study how self-disclosure can be used as a mechanism to encourage more effort and commitment from newcomers. We present our results from exploring the relationship between selfdisclosure and newcomers’ effort and commitment in an online community. Our results show that selfdisclosure has significant positive associations with newcomers’ subsequent effort in the community.
Tracking of non-brownian particles using the Viterbi algorithm

We present a global tracking algorithm for tracking particles with dynamic motion models. The tracking algorithm augments a existing global track linking algorithm based on the Viterbi algorithm with a Gaussian Mixture Probability Hypothesis Density filter. This allows the tracking algorithm to use the target velocities to link tracks. The algorithm can handle clutter, missed detections, and random appearance and disappearance of particles in the field of view. The algorithm can also handle targets that switch between different motion models according to a Markov process. The algorithm is evaluated on the synthetic datasets used in the ISBI 2012 Particle Tracking Challenge, which simulate vesicles, receptors, microtubules, and viruses at different particle densities and signal to noise ratios. The evaluation shows that our algorithm performs well across a wide range of particle tracking problems in both 2D and 3D.
Are Delta-Aminolevulinate Dehydratase Inhibition and Metal Concentrations Additional Factors for the Age-Related Cognitive Decline?

Aging is often accompanied by cognitive impairments and influenced by oxidative status and chemical imbalances. Thus, this study was conducted to examine whether age-related cognitive deficit is associated with oxidative damage, especially with inhibition of the enzyme delta-aminolevulinate dehydratase (ALA-D), as well as to verify the influence of some metals in the enzyme activity and cognitive performance. Blood ALA-D activity, essential (Fe, Zn, Cu, Se) and non-essential metals (Pb, Cd, Hg, As, Cr, Ni, V) were measured in 50 elderly and 20 healthy young subjects. Cognitive function was assessed by tests from Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) battery and other. The elderly group presented decreased ALA-D activity compared to the young group. The index of ALA-D reactivation was similar to both study groups, but negatively associated with metals. The mean levels of essential metals were within the reference values, while the most toxic metals were above them in both groups. Cognitive function impairments were observed in elderly group and were associated with decreased ALA-D activity, with lower levels of Se and higher levels of toxic metals (Hg and V). Results suggest that the reduced ALA-D activity in elderly can be an additional factor involved in cognitive decline, since its inhibition throughout life could lead to accumulation of the neurotoxic compound ALA. Toxic metals were found to contribute to cognitive decline and also to influence ALA-D reactivation.
Word Vectors and Two Kinds of Similarity

This paper examines what kind of similarity between words can be represented by what kind of word vectors in the vector space model. Through two experiments, three methods for constructing word vectors, i.e., LSA-based, cooccurrence-based and dictionary-based methods, were compared in terms of the ability to represent two kinds of similarity, i.e., taxonomic similarity and associative similarity. The result of the comparison was that the dictionary-based word vectors better reflect taxonomic similarity, while the LSA-based and the cooccurrence-based word vectors better reflect associative similarity.
Performance of Rubberwood Fiber–Thermoplastic Natural Rubber Composites

Rubberwood fiber–thermoplastic natural rubber (RWF–TPNR) composites were prepared and evaluated in this study. The RWFs were supplied in the form of thermomechanical pulp (TMP) from the Hume Fiberbord Company in Malaysia. The fibers were screened to 0.5–1 mm sizes and blended with TPNR and maleic anhydride–grafted polypropylene (MAPP) as a compatibilizer in the internal mixer of Brabender Plasticorder machine. The mechanical, thermal properties and morphology of the composites were investigated by tensile, impact, flexural, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and scanning electron microscope (SEM). The results showed that the tensile strength and Young's modulus increased, while tensile strain and impact strength decreased with increasing of fiber loading. Thermogravimetric analysis showed an increasing degradation temperature of the samples with RWF. On the other hand, SEM micrographs showed good adherence between fibers and matrix with the presence of MAPP.
Adaptive inverse control of linear motor based on ANFIS

The plant studied in this paper is a servo platform vertically driven by a permanent magnet synchronous linear motor (PMSLM). Due to the passive influence of the nonlinear factors such as imbalance torque, friction, static resistance torque and inertia change, an inverse control method based on Adaptive Neuro-Fuzzy Inference System (ANFIS) is applied to improve the system performance. The ANFIS is adopted to design the nonlinear controller, which has the inverse transfer function of the plant. Normally, there is no direct feedback in the control loop, except that in the adjustment period, the plant output is monitored and utilized by an adaptive algorithm to adjust the parameters of the controller. Simulation and experimental results verify that the method can overcome the adverse effects of nonlinear factors.
Drug–drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths

Motivation Adverse events resulting from drug‐drug interactions (DDI) pose a serious health issue. The ability to automatically extract DDIs described in the biomedical literature could further efforts for ongoing pharmacovigilance. Most of neural networks‐based methods typically focus on sentence sequence to identify these DDIs, however the shortest dependency path (SDP) between the two entities contains valuable syntactic and semantic information. Effectively exploiting such information may improve DDI extraction. Results In this article, we present a hierarchical recurrent neural networks (RNNs)‐based method to integrate the SDP and sentence sequence for DDI extraction task. Firstly, the sentence sequence is divided into three subsequences. Then, the bottom RNNs model is employed to learn the feature representation of the subsequences and SDP, and the top RNNs model is employed to learn the feature representation of both sentence sequence and SDP. Furthermore, we introduce the embedding attention mechanism to identify and enhance keywords for the DDI extraction task. We evaluate our approach using the DDI extraction 2013 corpus. Our method is competitive or superior in performance as compared with other state‐of‐the‐art methods. Experimental results show that the sentence sequence and SDP are complementary to each other. Integrating the sentence sequence with SDP can effectively improve the DDI extraction performance. Availability and implementation The experimental data is available at https://github.com/zhangyijia1979/hierarchical‐RNNs‐model‐for‐DDI‐extraction. Contact zhyj@dlut.edu. cn or michel.dumontier@maastrichtuniversity.nl. Supplementary information Supplementary data are available at Bioinformatics online.
Modeling and Representing Negation in Data-driven Machine Learning-based Sentiment Analysis

We propose a scheme for explicitly modeling and representing negation of word n-grams in an augmented word n-gram feature space. For the purpose of negation scope detection, we compare 2 methods: the simpler regular expression-based NegEx, and the more sophisticated Conditional Random Field-based LingScope. Additionally, we capture negation implicitly via word biand trigrams. We analyze the impact of explicit and implicit negation modeling as well as their combination on several data-driven machine learning-based sentiment analysis subtasks, i.e. document-level polarity classification, both inand cross-domain, and sentence-level polarity classification. In all subtasks, explicitly modeling negation yields statistically significant better results than not modeling negation or modeling it only implicitly.
Investigation of Route-Independent Aggressive and Safe Driving Features Obtained from Accelerometer Signals

System that can identify safe and aggressive driving behavior is useful in nowadays transportation service sector: both in private and public. In this paper, we propose a systematic approach (step-by-step) for driving style classification and investigate features that best describe differences between aggressive and safe driving styles when driving information is collected from 3-axis accelerometer (G-sensor) alone which is ubiquitous and inexpensive inertial measurement sensor. Data were collected from light vehicle when driving in aggressive and save driving style. Features investigated do not depend on same route assumption and can be used as universal. Using driving features a classification task is performed by applying Random Forest algorithm. Results show that high classification accuracy (95.5%) using experimental data can be obtained when applying proposed input feature set.
Transactional pointcuts: designation reification and advice of interrelated join points

Aspect-oriented mechanisms are characterized by their join point models. A join point model has three components: join points, which are elements of language semantics; "a means of identifying join points"; and "a means of affecting the behaviour at those join points. " A pointcut-advice model is a dynamic join point model in which join points are points in program execution. Pointcuts select a set of join points, and advice affects the behaviour of the selected join points. In this model, join points are typically selected and advised independently of each other. That is, the relationships between join points are not taken into account in join point selection and advice. In practice, join points are often not independent. Instead, they form part of a higher-level operation that implements the intent of the developer (e.g. managing a resource). There are natural situations in which join points should be selected only if they play a specific role in that operation. We propose a new join point model that takes join point interrelationships into account and allows the designation of more complex computations as join points. Based on the new model, we have designed an aspect-oriented construct called a transactional pointcut (transcut). Transcuts select sets of interrelated join points and reify them into higher-level join points that can be advised. They share much of the machinery and intuition of pointcuts, and can be viewed as their natural extension. We have implemented a transcuts prototype as an extension to the AspectJ language and integrated it into the abc compiler. We present an example where a transcut is applied to implement recommended resource handling practices in the presence of exceptions within method boundaries.
Comparison of combitube, easy tube and tracheal tube for general anesthesia

Background & Aims: The Combitube® and EasyTube™ enable effective ventilation whether placed in the trachea or esophagus and can be used in prehospital settings, as well as in “Cannot Ventilate Cannot Intubate” situations in the operating room. Whether they can be continued to provide general anesthesia, if required, is not established. Thus the efficacy of Combitube and EasyTube was evaluated and compared with the tracheal tube for general anesthesia using controlled ventilation. Materials and Methods: Combitube, EasyTube and tracheal tubes were used in 30 patients each to secure the airway in a randomized controlled manner. Ventilatory parameters were measured along with hemodynamic variables, and characteristics related to device placement. Results: There was no significant difference in the various ventilatory parameters including minute ventilation requirement to maintain eucapnia amongst the three groups at any time point. There was no hypoxia or hypercarbia in any patient at any time. Placement of EasyTube was more difficult (P = 0.01) as compared with both Combitube and tracheal tube. EasyTube and Combitube resulted in higher incidence of minor trauma than with a tracheal tube (P = 0.00). Conclusion: Combitube and EasyTube may be continued for general anesthesia in patients undergoing elective nonlaparoscopic surgeries of moderate duration, if placed for airway maintenance. Given the secondary observations regarding placement characteristics of the airway devices, it, however cannot be concluded that the devices are a substitute for endotracheal tube for airway maintenance per se, unless specifically indicated
Comparison of viable plate count, turbidity measurement and real‐time PCR for quantification of Porphyromonas gingivalis

The viable plate count (VPC) is considered as the reference method for bacterial enumeration in periodontal microbiology but shows some important limitations for anaerobic bacteria. As anaerobes such as Porphyromonas gingivalis are difficult to culture, VPC becomes time‐consuming and less sensitive. Hence, efficient normalization of experimental data to bacterial cell count requires alternative rapid and reliable quantification methods. This study compared the performance of VPC with that of turbidity measurement and real‐time PCR (qPCR) in an experimental context using highly concentrated bacterial suspensions. Our TaqMan‐based qPCR assay for P. gingivalis 16S rRNA proved to be sensitive and specific. Turbidity measurements offer a fast method to assess P. gingivalis growth, but suffer from high variability and a limited dynamic range. VPC was very time‐consuming and less repeatable than qPCR. Our study concludes that qPCR provides the most rapid and precise approach for P. gingivalis quantification. Although our data were gathered in a specific research context, we believe that our conclusions on the inferior performance of VPC and turbidity measurements in comparison to qPCR can be extended to other research and clinical settings and even to other difficult‐to‐culture micro‐organisms.
Incremental Task-Level Reasoning in a Competitive Factory Automation Scenario

Facing the fourth industrial revolution, autonomous mobile robots are expected to play an important role in the production processes of the future. The new Logistics League Sponsored by Festo (LLSF) under the RoboCup umbrella focuses on this aspect of robotics to provide a benchmark testbed on a common robot platform. We describe certain aspects of the integrated robot system of our Carologistics RoboCup team, in particular our reasoning system for the supply chain problem of the LLSF. We approach the problem by deploying the CLIPS rules engine for product planning and dealing with the incomplete knowledge that exists in the domain and show that it is suitable for computationally limited platforms.
Identification of CD4+ T Cell Epitopes in C. burnetii Antigens Targeted by Antibody Responses

Coxiella burnetii is an obligate intracellular Gram-negative bacterium that causes acute Q fever and chronic infections in humans. A killed, whole cell vaccine is efficacious, but vaccination can result in severe local or systemic adverse reactions. Although T cell responses are considered pivotal for vaccine derived protective immunity, the epitope targets of CD4+ T cell responses in C. burnetii vaccination have not been elucidated. Since mapping CD4+ epitopes in a genome with over 2,000 ORFs is resource intensive, we focused on 7 antigens that were known to be targeted by antibody responses. 117 candidate peptides were selected from these antigens based on bioinformatics predictions of binding to the murine MHC class II molecule H-2 IAb. We screened these peptides for recognition by IFN-γ producing CD4+ T cell in phase I C. burnetii whole cell vaccine (PI-WCV) vaccinated C57BL/6 mice and identified 8 distinct epitopes from four different proteins. The identified epitope targets account for 8% of the total vaccination induced IFN-γ producing CD4+ T cells. Given that less than 0.4% of the antigens contained in C. burnetii were screened, this suggests that prioritizing antigens targeted by antibody responses is an efficient strategy to identify at least a subset of CD4+ targets in large pathogens. Finally, we examined the nature of linkage between CD4+ T cell and antibody responses in PI-WCV vaccinated mice. We found a surprisingly non-uniform pattern in the help provided by epitope specific CD4+ T cells for antibody production, which can be specific for the epitope source antigen as well as non-specific. This suggests that a complete map of CD4+ response targets in PI-WCV vaccinated mice will likely include antigens against which no antibody responses are made.
Genetic Analysis of Group A Streptococcus Isolates Recovered during Acute Glomerulonephritis Outbreaks in Guizhou Province of China

In this study, 68 group A streptococcus (GAS) isolates associated with two outbreaks of acute glomerulonephritis (AGN) in China were analyzed by emm typing. A total of 11 different emm types were identified. Analysis of emm type distribution suggested that AGN outbreaks in two counties were caused by emm60.1- and emm63.0-type GAS. These two types were further characterized by pulsed-field gel electrophoresis, multilocus sequence typing, sof sequence typing, and PCR-based identification of streptococcal pyrogenic exotoxin A, B, and C (speA, speB, and speC) genes. In antimicrobial susceptibility tests, all outbreak strains were resistant to erythromycin and tetracycline, and the rates of resistance of nonoutbreak strains to the two antibiotics were 63.6% and 90.9%. This study is also the first to report a nephritogenic M63 GAS strain.
Minimum multicuts and Steiner forests for Okamura-Seymour graphs

We study the problem of nding minimum multicuts for an undirected planar graph, where all the terminal vertices are on the boundary of the outer face. This is known as an Okamura-Seymour instance. We show that for such an instance, the minimum multicut problem can be reduced to the minimum-cost Steiner forest problem on a suitably dened dual graph. The minimum-cost Steiner forest problem has a 2-approximation algorithm. Hence, the minimum multicut problem has a 2-approximation algorithm for an Okamura-Seymour instance.