Hydromagnetic nanofluid flow past a stretching cylinder embedded in non-Darcian Forchheimer porous media

The present article presents the hydromagnetic nanofluid flow past a stretching cylinder embedded in non-Darcian Forchheimer porous media by using Buongiorno’s mathematical model (Buongiorno in J Heat Transf 128:240–250, 2006; Nadeem et al. in J Taiwan Inst Chem Eng 45:121, 2014, Nadeem et al. Appl Nanosci 4:625–631, 2014). Thermal radiation via Roseland’s approximation (Akbar et al. in Chin J Aeronaut 26:1389–1397, 2013; Nadeem and Haq in J Aerosp Eng 28:04014061, 2012), Brownian motion, thermophoresis and Joule heating effects are also considered. To explore thermal characteristics, prescribed heat flux and prescribed mass flux boundary conditions are deployed. Governing flow problem consists of PDEs in the cylindrical form, which are converted into system of nonlinear ODEs by applying applicable similarity transforms. ODEs are tackled by RK–Fehlberg fourth–fifth-order numerical integration scheme with shooting algorithm. Impact of numerous involving physical parameters on flow features like temperature distribution, velocity distribution, Sherwood number, local Nusselt number and skin friction coefficient is shown through graphs and tables.

     

A note on “Regular bipolar fuzzy graphs” Neural Computing and Applications 21(1) (2012) 197–205

In this note, we show by examples that Definitions 3.3, 3.5, Propositions 3.9, 3.10 and Theorem 3.17 in the paper by Akram and Dudek (Neural Comput Appl 21(1):197–205, 2012) contain some flaws, and then, we presented the updated results. Hence, we introduce generalized regular bipolar fuzzy graphs.

     

A complete ranking of trapezoidal fuzzy numbers and its applications to multi-criteria decision making

The problem (or scenario) involving qualitative or imprecise information is not solvable by classical set theory. To overcome the shortcoming of classical set theory, Zadeh (Inf Control 8(3):338–356, 26) introduced the concept of fuzzy sets that generalizes the concept of classical sets. Fuzzy set theory allows modelling and handling of imprecise information in an effective way. As a special class of fuzzy sets, fuzzy numbers (FN) which are very much important in decision making was introduced by Dubois and Prade (Int J Syst Sci 9:631–626, 12). The available methods for solving multi-criteria decision making problems (MCDM) are problem dependent in nature due to the partial ordering on the class of FN. Total ordering on the class of FN by countable number of real-valued parameters was achieved by Wang and Wang (Fuzzy Sets Syst 243:131–141, 21). A complete ranking on the class of trapezoidal fuzzy numbers (TrFNs) using finite number of score functions is achieved in this paper. In this paper, a new ranking procedure (complete) on the class of TrFNs using the concepts of mid-point, radius, left and right fuzziness of TrFN is proposed and further we introduce a method for solving fuzzy multi-criteria decision making (Fuzzy MCDM) problem. Finally, comparisons of our proposed method with familiar existing methods are listed.

     

Modeling and optimization of the hot embossing process for micro- and nanocomponent fabrication

Hot embossing, a polymer molding process conceived by Forschungszentrum Karlsruhe, is one of the established replication processes for microstructures The process is especially well suited for manufacturing small and medium series of microcomponents (SPIE Conference 1997; Polymer News 25:224–229, 2000; J Micromech Microeng 14:R1–14, 2004; Sensors Actuators 3:130–135, 2000). However, a wider application of the process currently is seriously hampered by the lack of adequate simulation tools for process optimization and part design. This situation is becoming more critical, as the dimension of the microstructures shrink from micron and submicron levels to the nanoscale and as productivity requirements dictate the enlargement of formats to process larger numbers of devices in parallel. Based on the current scientific work (Forschungszentrum Karlsruhe, FZKA-Bericht 7058 2003; DTIP Conference Montreux 2004; Microsystem Tech 10:432–437 2004), a German–Canadian cooperation has been started. The objective of this cooperation is to fill the gap mentioned above by developing reliable computer models and simulation tools for the hot embossing process and to incorporate these models in a user-friendly computer code. The present paper will give an overview of the activities in the project. The activities related to material characterization, especially the development of a viscoelastic material model, the characterization of friction between polymer and mold during demolding, the development of an 8-in. microstructured mold, and the fabrication of nanostructured molds will be discussed.

     

Diagnosis of Alzheimer's disease using Naive Bayesian Classifier

In the world of modern medicine, though there is lot of medical achievements, some diseases still continue to pest the human race. Unfortunately, dementia is one such disease. All over the world, a large number of people are suffering from dementia. Dementia is a brain-related disease. Diagnosis of the disease at the earlier stage is the requirement of the day. Alzheimer's disease (AD) is one of the types of the dementia, and around 60 % of demented are affected from Alzheimer’s disease (Salmon and Bondi in Neuro psychological assessment of dementia. National Institutes of Health, 2010). All over the world, there are around 35 million people suffering from AD and this number is expected to double by 2030 and more than triple by 2050, that is to 115 million (Prince et al. in World Alzheimer report 2013: journey of caringan analysis of long-term care for dementia. Kings College, London, 2013). Diagnosis of this disease at an early stage will help the patients to lead a quality life for the remaining tenure of their life. In this paper, the authors have collected data of 466 subjects by conducting neuropsychological tests. The authors focus on diagnosis of AD for neuropsychological tests using Naive Bayes.

     

Team reflection makes resilience-related knowledge explicit through collaborative sensemaking: observation study at a rail post

Resilience is defined as the ability to adaptively deal with system boundaries in the face of the unexpected and unforeseen (Branlat and Woods in AAAI Fall Sympoisum, 2010. http://www.aaai.org/ocs/index.php/FSS/FSS10/paper/viewPaper/2238). We hypothesize that drawing upon resilience-related knowledge is a prerequisite for such adaptivity. This paper proposes team reflection (Ellis et al. in Curr Dir Psychol Sci 23(1):67–72, 2014) as a macrocognitive function to make the resilience-related knowledge explicit. This knowledge is implicitly available with individual team members active at the sharp end but is never explicitly shared due to invisibility of goal-relevant constraints. To overcome this invisibility, we suggest an application that makes changes in the current rail socio-technical system visible in terms of the three system boundaries, a variation of the originally proposed by Rasmussen (Saf Sci 27(2/3):183–213, 1997): safety, performance and workload. This allows a team of rail signallers to analyse movements towards system boundaries and share knowledge on these movements. An observational study at a rail control post was conducted to assess the value of team reflection in making resilience-related knowledge explicit. For this purpose, we developed a first prototype of the application concerning the performance boundary only. Using naturalistic observations of a team during a week, we observed how they reflected at the end of their shift on salient system changes. A global content analysis was used to show the relevance of the content to resilience and to test the increase in the resilience-related knowledge throughout the observation period. A specific case of a human approaching the rail tracks, as a potential suicide, was analysed in detail. The results show the value of team reflection on system movements towards their boundaries, thus making goal-relevant constrained knowledge explicit within the operational rail environment.

     

Leontief Meets Markov: Sectoral Vulnerabilities Through Circular Connectivity

Economists have been aware of the mapping between an Input-Output (I-O, hereinafter) table and the adjacency matrix of a weighted digraph for several decades (Solow, Econometrica 20(1):29–46, 1952). An I-O table may be interpreted as a network in which edges measure money flows to purchase inputs that go into production, whilst vertices represent economic industries. However, only recently the language and concepts of complex networks (Newman 2010) have been more intensively applied to the study of interindustry relations (McNerney et al. Physica A Stat Mech Appl, 392(24):6427–6441, 2013). The aim of this paper is to study sectoral vulnerabilities in I-O networks, by connecting the formal structure of a closed I-O model (Leontief, Rev Econ Stat, 19(3):109–132, 1937) to the constituent elements of an ergodic, regular Markov chain (Kemeny and Snell 1976) and its chance process specification as a random walk on a graph. We provide an economic interpretation to a local, sector-specific vulnerability index based on mean first passage times, computed by means of the Moore-Penrose inverse of the asymmetric graph Laplacian (Boley et al. Linear Algebra Appl, 435(2):224–242, 2011). Traversing from the most central to the most peripheral sector of the economy in 60 countries between 2005 and 2015, we uncover cross-country salient roles for certain industries, pervasive features of structural change and (dis)similarities between national economies, in terms of their sectoral vulnerabilities.

     

The Tinker tool for graphical tactic development

PSGraph (Grov et al. in LPAR. Springer, Berlin, pp 324–339, 2013) is a graphical language to support the development and maintenance of proof tactics for interactive theorem provers. By using labelled hierarchical graphs this formalisation improves upon analysis and maintenance found in traditional tactic languages. Tool support for PSGraph is achieved by Tinker (Grov et al. in UITP 2014, ENTCS, vol 167. Open Publishing Association, London, pp 23–34, 2014; Lin et al. in Tools and algorithms for the construction and analysis of systems. Springer, Berlin, pp 573–579, 2016): a theorem prover-independent system, which is connected to several different provers, with a graphical user interface including novel features to develop and debug proof tactics graphically. In this paper we provide a detailed and formal account of PSGraph and show how theorem prover independence is achieved by Tinker. We then show practical use of PSGraph and Tinker by developing several proof patterns using the language and tool.

     

Pedestrian detection based on the privileged information

The pedestrian detection is always a challenging issue in the computer vision. Unlike the object recognition problem, the detection’s speed is a critical factor. In order to accelerate detection speed while maintaining competitive accuracy, in this paper we introduce a new model: twin support vector machine based on privileged information (called TSVMPI, in this paper) (Qi et al. in Neurocomputing 129:146–152, 1) to detect pedestrian. TSVMPI uses two nonparallel hyperplane classifiers to decide the label of an unknown sample and is superior to the standard SVM, especially in the linear kernel case, resulting in a significant advantage to deal with the special task. All experimental results demonstrate our strategy’s effectiveness and show that the privileged information indeed offers a significant improvement for the pedestrian detection.

     

A New Adaptive Explicit Nonlinear Model Predictive Control Design for a Nonlinear MIMO System: An Application to Twin Rotor MIMO System

This paper proposed an adaptive explicit nonlinear model predictive control (AENMPC) technique using multiple estimation models with a convex combination framework [18] for a class of nonlinear MIMO systems. Here, the explicit solution for the control signal is obtained from an optimal performance index which can be formulated without online optimization. In this work, a closed-form control law is developed by approximating the tracking error in the receding horizon by its Taylor series expansion. The control performance of any model-based control technologies explicitly depends on the quality of the unknown system parameters hence an adaptive parameter estimator is used to estimate the system parameter online [16,17]. To ensure the boundedness of the estimated parameter within a predefined compact region, a projection based adaptive law is used [43]. Using an aerodynamic laboratory set-up, known as the twin-rotor multi-input multi-output system (TRMS), the effectiveness of the proposed control algorithm has been verified. The complete state information of the system to the proposed adaptive controller is given from an extended Kalman filter based state observer. The performance of the proposed adaptive control algorithm has been verified successfully in simulations as well as real-time experimental setup of the TRMS model and compared with an existing control approach.

     

Analytical model of squeeze film air damping of perforated plates in the free molecular regime

In this paper, an analytical model of squeeze film damping (SQFD) of perforated plates in the free molecular regime is developed, which is based on: (1) the modification of the perforated energy transfer model (P-ETM) (Li and Hu, J Micromech Microeng 21:025006, 2011) by giving the probability of molecules entering the gap through holes; (2) the application of Sumali’s formula (J Micromech Microeng 17:2231–2240, 2007) to relate to the Monte Carlo model (MC) (Hutcherson and Ye, J Micromech Microeng 14:1726–1733, 2004) quantitatively. The analytical model can model the perforation effect on SQFD of plates of various hole sizes. Compared with experiment data and numerical models, the analytical model is proved to be accurate, easy to operate. The effect of gap distance on SQFD of perforated plate in the free molecular regime is discussed. Due to perforation effect, as gap distance increases, the damping constant of non-perforated plate decreases faster than that of perforated plate of the same size.

     

Highly sensitive refractive index sensor based on degeneracy in specialty optical fibers: a new approach

We study the phenomenon of deliberate inter-modal interactions in a specially designed index guided Microstructured Optical Fiber (MOF) by exploiting multipole expansion method (White et al. 2002). The MOF is designed in such a way that the first layer of holes is judiciously filled with a material having refractive index slightly greater than the background material or core and remaining holes are filled with air. Accordingly, we find an interesting phenomenon of mode crossing between the fundamental mode and a targeted defect mode while tuning the wavelength. Exploring this transition wavelength of the mode crossing, we propose a design of a fiber optic sensor for refractive index measurement (Silva et al. 2014) with enhanced sensitivity.

     

Tutoring writing spelling skills within a web-based platform for children

According to the NMC Horizon Report (Johnson et al. in Horizon Report Europe: 2014 Schools Edition, Publications Office of the European Union, The New Media Consortium, Luxembourg, Austin, 2014 [1]), data-driven learning in combination with emerging academic areas such as learning analytics has the potential to tailor students’ education to their needs (Johnson et al. 2014 [1]). Focusing on this aim, this article presents a web-based (training) platform for German-speaking users aged 8–12.Our objective is to support primary-school pupils—especially those who struggle with the acquisition of the German orthography—with an innovative tool to improve their writing and spelling competencies. On this platform, which is free of charge, they can write and publish texts supported by a special feature, called the intelligent dictionary. It gives automatic feedback for correcting mistakes that occurred in the course of fulfilling a meaningful writing task. Consequently, pupils can focus on writing texts and are able to correct texts on their own before publishing them. Additionally, they gain deeper insights in German orthography. Exercises will be recommended for further training based on the spelling mistakes that occurred. This article covers the background to German orthography and its teaching and learning as well as details concerning the requirements for the platform and the user interface design. Further, combined with learning analytics we expect to gain deeper insight into the process of spelling acquisition which will support optimizing our exercises and providing better materials in the long run.

     

Properties of a Variational Model for Video Inpainting

We consider a variational model analyzed in March and Riey (Inverse Probl Imag 11(6): 997–1025, 2017) for simultaneous video inpainting and motion estimation. The model has applications in the field of recovery of missing data in archive film materials. A gray-value video content is reconstructed in a spatiotemporal region where the video data is lost. A variational method for motion compensated video inpainting is used, which is based on the simultaneous estimation of apparent motion in the video data. Apparent motion is mathematically described by a vector field of velocity, denoted optical flow, which is estimated through gray-value variations of the video data. The functional to be minimized is defined on a space of vector valued functions of bounded variation and the relaxation method of the Calculus of Variations is used. We introduce in the functional analyzed in March and Riey(Inverse Probl Imag 11(6): 997–1025, 2017) a suitable positive weight, and we show that diagonal minimizing sequences of the functional converge, up to subsequences as the weight tends to infinity, to minimizers of an appropriate limit functional. Such a limit functional is the relaxed version of a functional, modified with suitable improvements, proposed by Lauze and Nielsen (2004) and which permits an accurate joint reconstruction both of the optical flow and of the video content.

     

Design and analysis of a large-scale COVID-19 tweets dataset

As of July 17, 2020, more than thirteen million people have been diagnosed with the Novel Coronavirus (COVID-19), and half a million people have already lost their lives due to this infectious disease. The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. Since then, social media platforms have experienced an exponential rise in the content related to the pandemic. In the past, Twitter data have been observed to be indispensable in the extraction of situational awareness information relating to any crisis. This paper presents COV19Tweets Dataset (Lamsal 2020a), a large-scale Twitter dataset with more than 310 million COVID-19 specific English language tweets and their sentiment scores. The dataset’s geo version, the GeoCOV19Tweets Dataset (Lamsal 2020b), is also presented. The paper discusses the datasets’ design in detail, and the tweets in both the datasets are analyzed. The datasets are released publicly, anticipating that they would contribute to a better understanding of spatial and temporal dimensions of the public discourse related to the ongoing pandemic. As per the stats, the datasets (Lamsal 2020a, 2020b) have been accessed over 74.5k times, collectively.

     

Hidden Markov model a tool for recognition of human contexts using sensors of smart mobile phone

A fast and accurate computational model of HMM (Hidden Markov Model) is proposed for Activity Recognition System using inbuilt sensors of Smart Mobile Phone. Twelve features are calculated from the captured data and the feature vectors are divided into two vectors which are used as inputs to HMM. All computational methods follow probability theories and for measuring differences of two probability based events we used K–L divergence of Kullback and Leibler (Ann Math Stat 22(1):79–86, 1951) known as KLD (Kullback & Leibler Divergence). For comparing of feature values of ground truth and that of experimental values, we have developed an algorithm D-HMM (Divisional-HMM, proposed algorithm). Results show better recognition than existing HF-SVM (Hardware Friendly Support Vector Machine) and also better than our previous work of CFT (Conditional Features using Threshold, a method developed for using different schemes of threshold values for selection and matching purposes of feature values).

     

Model checking: recent improvements and applications

Model checking (Baier and Katoen in Principles of model checking, MIT Press, Cambridge, 2008; Clarke et al. in Model checking, MIT Press, Cambridge, 2001) is an automatic technique to formally verify that a given specification of a concurrent system meets given functional properties. Its use has been demonstrated many times over the years. Key characteristics that make the method so appealing are its level of automaticity, its ability to determine the absence of errors in the system (contrary to testing techniques) and the fact that it produces counter-examples when errors are detected, that clearly demonstrate not only that an error is present, but also how the error can be produced. The main drawback of model checking is its limited scalability, and for this reason, research on reducing the computational effort has received much attention over the last decades. Besides the verification of qualitative functional properties, the model checking technique can also be applied for other types of analyses, such as planning and the verification of quantitative properties. We briefly discuss several contributions in the model checking field that address both its scalability and its applicability to perform planning and quantitative analysis. In particular, we introduce six papers selected from the 23rd International SPIN Symposium on Model Checking Software (SPIN 2016).

     

Prediction and optimization of unsteady forced convection around a rounded cornered square cylinder in the range of Re

An unsteady two-dimensional laminar forced convection heat transfer around a square cylinder with the rounded corner edge is numerically investigated for Re = 80–180 and non-dimensional corner radius, r = 0.50–0.71 at Pr = 0.71 (Air). A structured non-uniform mesh is used for the computational domain discretization, and the finite-volume-method-based commercial code FLUENT is used for numerical simulation. The heat transfer characteristics over the rounded corner square cylinder are analyzed with average Nusselt number (Nu _avg) at various Re and various corner radii. The heat transfer characteristic is predicted by gene expression programming (GEP), and the GEP-generated explicit equation of Nu _avg is utilized in particle swarm optimization to optimize the corner radii for maximum heat transfer rate. The data required for the training the GEP model have been collected from the authors’ recent published article (Neural Comput Appl, 2015. doi:10.1007/s00521-015-2023-8). It is found that the heat transfer rate of a circular cylinder can be enhanced 12 % by introducing a new cylinder geometry of corner radius r = 0.51.

     

Combining bag-of-words and sentiment features of annual reports to predict abnormal stock returns

Automated textual analysis of firm-related documents has become an important decision support tool for stock market investors. Previous studies tended to adopt either dictionary-based or machine learning approach. Nevertheless, little is known about their concurrent use. Here we use the combination of financial indicators, readability, sentiment categories, and bag-of-words (BoW) to increase prediction accuracy. This paper aims to extract both sentiment and BoW information from the annual reports of US firms. The sentiment analysis is based on two commonly used dictionaries, namely a general dictionary Diction 7.0 and a finance-specific dictionary proposed by Loughran and McDonald (J Finance 66:35–65, 2011. doi:10.1111/j.1540-6261.2010.01625.x). The BoW are selected according to their tf–idf. We combine these features with financial indicators to predict abnormal stock returns using a multilayer perceptron neural network with dropout regularization and rectified linear units. We show that this method performs similarly as naïve Bayes and outperforms other machine learning algorithms (support vector machine, C4.5 decision tree, and k-nearest neighbour classifier) in predicting positive/negative abnormal stock returns in terms of ROC. We also show that the quality of the prediction significantly increased when using the correlation-based feature selection of BoW. This prediction performance is robust to industry categorization and event window.