Miracle-dz/UniFAR-FTUs huggingface dataset:
Coupling of Analytical and Higher Order Finite Element Approaches for an Efficient Simulation of Ultrasonic Guided Waves

In recent years the interest in online monitoring of lightweight structures with ultrasonic guided waves is steadily growing. Thus, the demand for powerful and efficient numerical simulation tools to predict the behavior of waves in thin-walled designs is equally increasing. Since automotive and aeronautic applications are a driving force in developing such Structural Health Monitoring (SHM) systems, Lamb wave based flaw detection devices are a suitable choice. Considering the aforementioned areas of application conventional linear and quadratic pure displacement finite elements quickly reach their limits. The required mesh density, to obtain good quality solutions, results in enormous computational costs when the wave propagation problem is to be solved within the time domain. To resolve this problem different possibilities are available. Analytical methods or higher order finite element method approaches (p-FEM) are among them. Although analytical approaches offer fast and accurate results they are limited to rather simple geometries. On the other hand, higher order finite element schemes are still computationally demanding. The drawbacks of both methods can be circumvented if regions of complex geometry are modeled using a p-FEM approach, while the response of the remaining structure is computed utilizing an analytical approach. The objective of the paper is to present an efficient method to couple different p-FEM schemes with an analytical description of an undisturbed region. Using this approach the numerical effort is reduced drastically. The functionality of the proposed scheme is demonstrated by studying the propagation of ultrasonic guided waves, so called Lamb waves, excited by a piezoelectric patch actuator. The actuator is modeled utilizing higher order coupled field finite elements, whereas the homogenous, isotropic plate is described analytically. The results of this "semi-analytical" approach show promising properties and highlight the opportunities to reduce the numerical effort if closed-form solutions are available.
Optimized IIR Filter Applied to the Limiting and Filtering Technique for Peak-to-Average Power Ratio Reduction

To improve the efficiency of wireless transmitters, the peak-to-average power ratio (PAPR) of complex-valued envelope signals can be reduced through a limiting and filtering technique. A constrained nonlinear optimization tool is reported in literature for the identification of the clipping factor and a finite impulse response (FIR) filter. In this work, such tool is extended to handle the identification of an infinite impulse response (IIR) filter. The extension is not straightforward because IIR filters can exhibit instability and the nonlinear optimizer becomes more susceptible to be trapped into local minima. An additional constraint is incorporated here to guarantee stability and coefficients from a Butterworth low-pass filter are chosen as an initial guess for the identification procedure. Matlab simulation results from a case study that employs a WCDMA envelope signal show that an extra PAPR reduction of 1.5 dB is achieved by adopting the optimized IIR filter instead of an optimized FIR filter, where this number is equivalent to 15.5% of the initial PAPR value. Keywords—Constrained nonlinear optimization, finite impulse response filter, infinite impulse response filter, power amplifier, radio frequency, wireless communication systems.
A novel method for minimizing the average waiting time of visitors during an airport pickup service

Abstract Traditional research has seldom discussed the importance of a successful pickup service in tourism management. This study presents a first-meeting procedure for reducing the average waiting time of visitors and receptionists, when they first meet each other during a pickup service. The main idea behind the procedure is to send a pickup service mobile application to each client for later automatic identification. The simulation results indicate that the automatic services indeed make their first meeting easier and friendlier, yielding better customer satisfaction. A time-efficient display scheduling algorithm for meeting scheduling is proposed in the procedure, which aims to minimize the average waiting time of all visitors. The proposed algorithm makes the first meeting more time-efficient and less labor intensive. We suggest that the findings can be extended to similar problems for better customer satisfaction; i.e. less waiting time and less execution time.
Designing evacuation routes with GeoGebra

We expose in this paper how the command ShortestDistance can be used in GeoGebra to design evacuation routes in buildings in a dynamic and interactive way. For those cases in which a legal regulation or procedure compels specific requirements in the design, we also indicate how to make use of JavaScript in order to implement a more general version of Dijkstra’s algorithm that makes possible to deal with such specifications.
The Evidence of Clade 7.1 Avian Influenza Virus (H5N1) in Qinghai Lake

The highly pathogenic influenza A virus subtype H5N1 spread throughout Asia since 2003, reached to Europe in 2005, and the Middle East, as well as Africa and caused a global concern for a potential pandemic threat last decade. A Clade 2.3. 2 H5N1 virus became dominate in the Qinghai Lake region in 2009 with sporadic mammal cases of infection and transferred to Russia and Europe through wild migratory birds. Currently, HPAI H5N1 of clades 2.3. 4, 2.3. 2, and 7 are the dominant co-circulating H5N1 viruses in poultry in Asia. 2.3. 2 Clade is dominant in wild birds through the world whereas there is no evident data about Clade 7 circulation in wild birds. We detected HPAI H5N1 virus of Clade 7.1 in Qinghai Lake, that closely related to Shanxi-like and Vietnam viruses co-circulating in poultry. This is the first report of Clade 7.1 H5N1 in wild birds. Based on phylogenetic analyses, the virus can be originated from Clade 7.1 virus gene pool that spread in Vietnam and Chinese poultry and could spread with migratory birds to Qinghai Lake. The Qinghai Lake continues to be significant hotspot for H5N1 surveillance since the regular outbreaks occurred there in wild birds and mammals. Based on these facts and findings, the related researchers should pay more attention to the Qinghai Lake basin as significant hotspot for H5N1 avian influenza surveillance since the regular H5N1 outbreaks occurred there in wild birds with sporadic mammal cases of infection.
ESTIMATION OF SCALING PARAMETERS AND ENERGY FLUXES OVER THE GULF OF MEXICO USING NEW OBSERVATIONS AND USING THE COARE PROGRAM

There is a need to calculate the transport and dispersion of pollutants emitted from offshore oil and gas exploration and production activities in the Gulf of Mexico to estimate and understand their impact on onshore areas. However, much uncertainty exists concerning the atmospheric boundary layer in the region due to space and time variations caused by variable underlying water temperatures and the effects of mesoscale atmospheric structures. The current study, supported by the U.S. Department of the Interior Minerals Management Service, has instrumented six oil platforms to obtain boundary layer observation measurements, including 915-MHz radar wind profilers, 2-KHz Radio Acoustic Sounding Systems (RASS), and near-surface routine meteorology instruments. Two of these meteorological stations operated from May 1998 through September 2001 and four operated from October 2000 through September 2001. The profilers measure winds and RASS measure virtual temperatures between heights of about 100 m and a few kilometers. The near-surface observations at the oil platforms include sea-skin temperature as well as wind speed, wind direction, air temperature, and mixing ratio at a reference elevation, zr, of about 25 m. These new data, in addition to the traditional data collected by buoys and available from the National Climatic Data Center (NCDC), are being analyzed to investigate the overwater surface energy balance and boundary layer structure for both steady-state horizontally homogeneous conditions and for conditions variable in time and space. These three-dimensional, timedependent fields will be used for analysis of transport and dispersion from overwater sources. Figure 1 shows a map of the Gulf of Mexico region and indicates the locations of the various observation sites.
Feasibility and effectiveness assessment of a mobile application for subhealth management

Abstract Introduction: Mibyeongbogam (MBBG) is a mobile application developed for subhealth status self-management in the Republic of Korea. It aims to assess a user's subhealth status, and then to recommend relevant traditional Korean medicine (TKM)-based health-promoting strategies. The purpose of this study is to evaluate the feasibility and effectiveness of MBBG's employment for the subhealth management of general healthy adults. Methods: This is a prospective, open-label, parallel group, randomized controlled trial that will seek to enroll 150 healthy adults, aged 30 to 49 years old, from 2 hospitals in the Republic of Korea. The eligible participants will then be randomly allocated to either the MBBG or control group, at a 1:1 allocation ratio. The MBBG group will use the application for 12 weeks, while the control group will undergo no intervention. The awareness of subhealth status will be primarily assessed. Health promoting behaviors, quality of life, TKM-based health questionnaires, and physical examination results will be assessed as secondary outcomes. Discussion: The primary endpoint will be tested with a 2-sample t test, or a Wilcoxon rank sum test. Any other continuous variables will be tested via an analysis of covariance, while categorical variables will be tested by a Chi-squared or Fisher exact test. Repeated measure analysis of variance will be performed to explore any in-group differences. The results will be addressed with a 95% confidence interval. We expect that MBBG will be the 1st TKM-based mobile application to be feasible for primary care in subhealth management. Trial registration: CRIS (Clinical Research Information Service), KCT0003488, February 11, 2019
7D3 PERFORMANCE OF THE EMANUEL CUMULUS PARAMETERIZATION SCHEME IN NOGAPS: IMPACT OF RECENT MODIFICATIONS

Recent implementation of the Emanuel cumulus parameterization scheme (Emanuel 1991, Emanuel and Zivkovic-Rothman 1999) in the U. S. Navy Operational Global Atmospheric Prediction System (NOGAPS) in place of the Relaxed Arakawa-Schubert scheme (Moorthi and Suarez 1992) has resulted in an overall improvement in the performance of NOGAPS, especially for tropical cyclone predictions. The Emanuel scheme, however, shows some weaknesses. Among them, the most significant characteristics are a warm bias at upper levels, a weak wind bias at all levels, and underprediction of heavy-precipitation events.
DYNAMICS OF SURFACTANT-INFLUENCED GAS / LIQUID INTERFACES

The presence of surfactants, ubiquitous at most gas/liquid interfaces and particularly at the air/water interface, has a pronounced effect on the stress balance at the interface, and this in turn is nonlinearly coupled to the bulk flow. Forces acting on the interface include surface tension gradients and the viscous resistance to shear and dilation. These viscoelastic properties vary with the surfactant concentration at the interface. The surface tension and surface shear viscosity were measured directly over a range of surface concentration of hemicyanine (an insoluble surfactant). Here, we present a fundamental description of the interface and its coupling to the bulk flow, and develop a Navier-Stokes numerical model. Numerical studies of a canonical flow are presented, comparing contaminated interface cases with that of a clean interface and a rigid no-slip surface, providing added insight into the altered dynamics that result from the presence of a small amount of surfactants.
Cluster Analysis-Based Approach Features Selection on Machine Learning for Detecting Intrusion

: Various machine learning technology approaches have been applied to intrusion detection system (IDS). To get optimal results, it needs to take several stages for processing the traffics. Among them is the feature selection method, where irrelevant and redundant features are removed. In the previous research, the system is developed based on feature grouping that used a clustering approach as the evaluation criteria. In this research, we propose a method for improving the performance of machine learning with the feature selection approach based on feature clustering. We propose cluster based feature selection derived from the value of mutual information and Pearson correlation. The cluster hierarchy is used in forming filters that are used to create selected and reduced clusters. In developing the cluster hierarchy, single, complete, and average linkage method are used to determine the formation of the best feature clusters. The classification method with Support Vector Machine (SVM), Naïve Bayes, and J48 decision tree are applied to observe the performance of the proposed feature selection. Based on the experimental results, we find that the highest accuracy (i
Learning Functors using Gradient Descent

Neural networks are a general framework for differentiable optimization which includes many other machine learning approaches as special cases. In this paper we build a category-theoretic formalism around a neural network system called CycleGAN. CycleGAN is a general approach to unpaired image-to-image translation that has been getting attention in the recent years. Inspired by categorical database systems, we show that CycleGAN is a "schema", i.e. a specific category presented by generators and relations, whose specific parameter instantiations are just set-valued functors on this schema. We show that enforcing cycle-consistencies amounts to enforcing composition invariants in this category. We generalize the learning procedure to arbitrary such categories and show a special class of functors, rather than functions, can be learned using gradient descent. Using this framework we design a novel neural network system capable of learning to insert and delete objects from images without paired data. We qualitatively evaluate the system on the CelebA dataset and obtain promising results.
Proteomics Tools – An Update

Recent advances in high-throughput genome sequencing, analytical instrumentation, bioinformatics and computational biology has permanently changed the way biology is practiced. With the evolution and progress in system biology, scientists can now study the systems comprehensively and in very fine molecular details rather than just focusing on various components of living system individually. Proteomics, commonly described as expressed form of genome, has emerged as one of the vital constituent of system biology and is being studied extensively across the globe. Unlike genome, proteome is a very dynamic and complex entity involving not only the structures, sequences, and functions of proteins, but also their abundance, localization, modifications, and interactions. Hence, proteomics can provide plenty of information for better understanding of the diversity and intricacies of the biological system. However, the analysis of these various properties of the proteome requires an equally diverse range of technologies, like two-dimensional gel electrophoresis, mass spectrometry, and protein microarrays. In this review, we provide an introduction to the field of proteomics with an aim to describe numerous tools and techniques to study proteomics. Sachin Kumar1*, Vikar Gaur, Sartaj Khurana2, Sudeep Bose3, Manjula Kiran4, Manjula Kiran5 and Surender K Sharawat6 1Department of Medical Oncology, Dr. B. R. Ambedkar Institute Rotary Cancer Hospital, India 2Department of Medical Oncology, Dr. B. R. Ambedkar Institute Rotary Cancer Hospital, India 3Department of Biotechnology, Amity University, Noida, Uttar Pradesh -201303, India 4Department of Biotechnology, Amity University, Noida, Uttar Pradesh -201303, India 5Deprtment of Biologicals, Amity University, India 6Department of Medical Oncology, Dr. B. R. Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, India Sachin Kumar, et al., Clinics in Oncology Lung Cancer Remedy Publications LLC. , | http://clinicsinoncology.com/ 2017 | Volume 2 | Article 1358 2 protein interactions. In contrast, through expression proteomics, novel, disease-specific or differentially expressed proteins can be identified by comparing the expression levels of proteins in normal healthy tissues and diseased tissues and such proteins can represent a therapeutic target or can serve as diagnostic biomarkers. Determination of protein-protein interactions by interaction proteomics helps to determine protein functions and can also show how proteins assemble in larger complexes.
Automatic Error Correction on Japanese Functional Expressions Using Character-based Neural Machine Translation

Correcting spelling and grammatical errors of Japanese functional expressions shows practical usefulness for Japanese Second Language (JSL) learners. However, the collection of these types of error data is difficult because it relies on detecting Japanese functional expressions first. In this paper, we propose a framework to correct the spelling and grammatical errors of Japanese functional expressions as well as the error data collection problem. Firstly, we apply a bidirectional Long Short-Term Memory with a Conditional Random Field (BiLSTM-CRF) model to detect Japanese functional expressions. Secondly, we extract phrases which include Japanese functional expressions as well as their neighboring words from native Japanese and learners’ corpora. Then we generate a large scale of artificial error data via substitu-tion, injection and deletion operations. Finally, we utilize the generated artificial error data to train a sequence-to-sequence neural machine translation model for correcting Japanese functional expression errors. We also compare the character-based method with the word-based method. The experimental results indicate that the char-acter-based method outperforms the word-based method both on artificial error data and real error data.
Ways Into The Logic Of Alexander Of Aphrodisias

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Accounting Conservatism in Europe and the Impact of Mandatory IFRS Adoption: Do country, institutional and legal differences survive?

It is argued that IFRS put more emphasis on neutrality than conservatism. We examine the impact of the mandatory change to IFRS in 2005 by European firms on the level of accounting conservatism and check whether well documented differences in conservatism across countries and varied institutional and legal settings survive the change. We document that accounting conservatism has decreased after the adoption of IFRS overall and more specifically across French and German law origin countries and countries with important debt markets and with less developed equity markets. We also find a reduction in conservatism in countries where the tax book conformity was high. More importantly, we document that differences across countries, institutional and legal settings disappear after mandatory IFRS adoption. We only find weak evidence that insider economies with weak enforcement (Leuz et al. 2003’s cluster 3 which includes Greece, Italy, Portugal, and Spain) continue to have a lower level of conservatism. Finally, the decrease in conservatism is most significant in countries which had the greatest difference with the new IFRS standards. Overall, we conclude that accounting standards do matter!
GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

High-performance implementations of graph algorithms are challenging to implement on new parallel hardware such as GPUs because of three challenges: (1) the difficulty of coming up with graph building blocks, (2) load imbalance on parallel hardware, and (3) graph problems having low arithmetic intensity. To address some of these challenges, GraphBLAS is an innovative, on-going effort by the graph analytics community to propose building blocks based on sparse linear algebra, which allow graph algorithms to be expressed in a performant, succinct, composable, and portable manner. In this paper, we examine the performance challenges of a linear-algebra-based approach to building graph frameworks and describe new design principles for overcoming these bottlenecks. Among the new design principles is exploiting input sparsity, which allows users to write graph algorithms without specifying push and pull direction. Exploiting output sparsity allows users to tell the backend which values of the output in a single vectorized computation they do not want computed. Load-balancing is an important feature for balancing work amongst parallel workers. We describe the important load-balancing features for handling graphs with different characteristics. The design principles described in this paper have been implemented in “GraphBLAST”, the first high-performance linear algebra-based graph framework on NVIDIA GPUs that is open-source. The results show that on a single GPU, GraphBLAST has on average at least an order of magnitude speedup over previous GraphBLAS implementations SuiteSparse and GBTL, comparable performance to the fastest GPU hardwired primitives and shared-memory graph frameworks Ligra and Gunrock, and better performance than any other GPU graph framework, while offering a simpler and more concise programming model.
Real-Time Physics-Based Removal of Shadows and Shading From Road Surfaces

We present a real-time physics-based system for generating an illumination free representation of road surfaces that maintains the distinction between asphalt and painted road markings. Cast shadows on road surfaces can create false features and modify the color of road markings, potentially masking important information for vehicle vision systems. We demonstrate a novel method for identifying the relative spectral properties of the direct and ambient illumination conditions and for using that to create an illumination-free 2D chromaticity space in log RGB. We then show how that representation can be used to generate an illumination-free greyscale representation that distinguishes road, white paint, and yellow paint, making it suitable for further analysis and classification. The entire process runs faster than 30Hz on current automotive-grade embedded processing systems. We evaluate the system on a paint detection task, comparing two types of learned classifiers, random forests and convolutional neural networks. For each type, one classifier is trained on the original images, and the other is trained on the illumination-free greyscale output. The classifiers are of identical complexity and trained on the same size data set. For both types, the classifier trained on the illumination-free outputs performs better, even on images with no cast shadows. The gap in performance is indicative of the cost of forcing a classifier to learn a task in the presence of the confounding illumination signal.