Miracle-dz/UniFAR-FTUs huggingface dataset:
Fatigue effect on phase transition of pedestrian movement: experiment and simulation study

How to model pedestrian movement is an intriguing problem in the area of statistical physics. As a common phenomenon of pedestrian movement, fatigue has a significant negative effect on pedestrian movement, especially when pedestrians move or run with heavy luggage, rescue the wounded in disaster, climb stairs and etc. According to the field observations and previous researches, fatigue coefficient is defined as the decrease of desired velocity in this study. However, previous researches lacked quantitative analysis of the effect of fatigue on pedestrian speed. It has been a great challenge to study the effect of fatigue on pedestrian flow, since pedestrians of heterogeneous walking abilities and the change of pedestrians’ moving properties need to be taken into consideration. Thus, at first, a series of pedestrian experiments, under three different conditions, were conducted to formulate the empirical relationship among fatigue, average free velocity, and walking distance. Then the empirical formulation of pedestrian fatigue was imported into the multi-velocity field floor cellular automata (FFCA) model for following pedestrian dynamics analysis. The velocity ratio was adjusted dynamically to adapt the change of pedestrians’ velocity due to fatigue. The fatigue, entrance flow rate and pedestrian’s initial desired velocity are found to have significant effects on the pedestrian flow. The space-time distributions of pedestrian density and velocity were explored in detail, with phase transition analyses from a free flow phase to a congestion phase. Additionally, the ‘density wave’ in the system can be observed if a certain ratio of burdened pedestrians lay in the high density region. The envelope of the ‘density wave’ reaches its maximum amplitude around the entrance position, and gradually diminishes away from the entrance.
A fully coupled THM model based on a non‐equilibrium thermodynamic approach and its application

In this study, a fully coupled thermo‐hydro‐mechanical (THM) theoretical model, called the TSINGHUA‐THERMOSOIL model, which is applicable in saturated soils, was developed based on a non‐equilibrium thermodynamic approach (see Groot S, Mazur P. Non‐Equilibrium thermodynamics. Amsterdam: North‐Holland Pub. Co., 1962). This model simplifies the mathematical description of a THM coupling system to the modeling of a group of migration coefficients and energy functions by means of the non‐equilibrium thermodynamic theory. The important concept of granular entropy was introduced for describing the granular fluctuation and the resulting transient elasticity. Therefore, non‐elastic mechanical constitutive relations that do not require such concepts as yield surface and flow rule are obtained. The physical process of bound pore water transforming into free water when the temperature rises was taken into consideration for describing the phenomenon of the volumetric compression of normally consolidated and slightly over‐consolidated clays under non‐isothermal conditions. The behavior of thermal consolidation and drained shear tests at different temperatures for saturated Kaolin clay with different over‐consolidation ratios (OCR) were simulated using this model. The effect of the OCR value, the heating rate and temperature cycling on the thermal consolidation and the effect of the temperature on the shear strength were analyzed and compared with existing experimental data. The results showed that the model proposed in this paper can accurately describe the thermal consolidation and the basic law of shearing for saturated soil, which preliminarily proves the validity of the model. Copyright © 2016 John Wiley & Sons, Ltd.
A Note on Generalized

Whitney’s theorem on line graphs is extended to the class of generalized line graphs defined by Hoffman. Whitney [4] showed that if G and H are connected graphs whose line graphs are isomorphic, then G and H are isomorphic, except in the case {G, H}={K,, Kl,3}. Moreover, if G and H have more than four vertices, then any isomorphism from L(G) to L(H) induces an isomorphism from G to H. In this note the result is extended to generalized line graphs. These graphs, introduced by Hoffman [3], are important because of Hoffman’s theorem that any sufficiently large graph whose least eigenvalue is -2 or greater, is a generalized line graph. The proof here includes a new proof of Whitney’s theorem in the case when G is not bipartite; the bipartite case is much easier.
A Homogenisation Method for a FSI Problem: Application to a Tube Bundle Row

A research program is ongoing at CEA in the framework of ASTRID project, for developing and validating tools for the analysis of the mechanical dynamic behaviour of Fast Breeder Reactor (FBR) cores. In this context, Fluid Structure Interaction is a recurring problem, especially in case of tube bundles in nuclear reactor cores or steam generators. The large number of elements makes modelling difficult to perform and any direct numerical simulation at whole core scale unachievable. In order to overcome this limitation, we resort to homogenisation techniques which allow to describe the global dynamic behaviour of the whole fluid-structure system. The purpose of this study is to better understand the effects of the spatial variation of the tube displacement field on the whole bundle dynamics. Thanks to multiple scales, a complete development of the problem is conducted. An analysis of the effects of spatial variations of the long scale displacement field on the tube bundle’s dynamics is led. As a first step we consider the homogenisation of Euler linear equations that gives us the possibility to make a thorough assessment of the problem with the extension to follow to Navier-Stokes equations.Copyright © 2017 by ASME
A Novel Spectral Clustering Based on Nonlinear Low Dimensional Embedding Feature Selection

Spectral clustering is a clustering method based on algebraic graph theory. It has solid theoretical foundation and good performance of clustering. However, during the process of nonlinear low rank approximation, the traditional spectral clustering algorithm can’t effectively remove redundant features leading to the phenomenon that the local area can not distinguish. It also suffers from the high computational complexity of eigen-decomposition when dealing with the high dimensional data. In order to resolve the aforementioned problems, in this paper a novel Spectral clustering algorithm called LF-SC is proposed. Firstly, based on the nonlinear low dimensional embedding feature selection, we realize dimension reduction. The multi clustering structure of the data is captured, the potential manifold structure is fully discovered, and the geometry structure of the low dimensional manifold clustering is well maintained. Secondly, utilizing the SVD instead of EVD to obtain the eigenvectors reduces the computational complexity and maintain the local structure of the data as well as low dimensional manifold. Extensive experiments show the effectiveness and efficiency of our approach.
Post-quantum key exchange based on ring-LWE

Given all the effort to develop quantum computers, cryptographers need to develop post-quantum cryptography today in order to maintain current security standards in the future. Key exchange protocols are important building blocks in cryptography and therefore one area of focus is the construction of post-quantum key exchange protocols. In this thesis we focus on two authenticated post-quantum key exchange protocols, one by Dagdelen, Ding, Snook, Zhang and Zhang and one by Peikert. We show that with our proposed parameters the estimated security of the protocol by Dagdelen, Ding, Snook, Zhang and Zhang is higher and conclude that a proper comparison of the two protocols can only be done with full initialization of the protocol by Peikert. Furthermore, we focus on the hardness of the Ring-Learning With Errors problem and we propose new parameter sets which are estimated to guarantee 128 and 256 bits of estimated security.
Does digital influencer endorsement contribute to building consumers’ attitude toward digital advertising during COVID-19 pandemic? mediating role of brand attitude

Abstract The COVID-19 pandemic is changing consumer behaviour, where consumers are shifting their activities toward digital activities. One of the popular advertising media among digital natives during the COVID-19 pandemic is digital influencers. Brands and marketers should use digital advertising and digital influencer in their digital advertising strategy to adapt to consumer behaviour changes during the COVID-19 pandemic. This study fills the gap in the advertising literature by proposing trust in digital influencers as a predictor for consumers’ attitudes toward digital advertising and evaluating brand attitude as mediating variable between consumers’ attitudes toward digital advertising and purchase intention. The data was collected through an online survey, and there were 461 respondents in Indonesia. Smart Partial Least Square was used to analyze, test, and validate the collected data. The result showed that trust in digital influencers, entertainment, information, and personalization directly influence consumers’ attitude toward digital advertising, and brand attitude has a positive and significant influence as a mediating variable between consumers’ attitude toward digital advertising and purchase intention. This study also discusses theoretical and managerial implications on how brands, marketers, and agencies can effectively use digital advertising and influencers.
An Empirical Analysis on Pattern Reconstruction for Optimal Storage of Wearable Sensor Data

In this data age, connected devices are continuously generating petabytes of images, text, and internet of things (IoT) sensor data. One approach to efficiently store this massive data is to extract the relevant and representative features and store only those features instead of the continuous streaming data. However, it raises a question as to the amount of information content we can retain from the data and if we can reconstruct the pseudo-original data when needed. By facilitating relevant and representative feature extraction, storage and reconstruction of near original pattern, we aim to address some of the challenges faced by the explosion of the streaming data. We present a preliminary study, where we explored multiple autoencoders for the concise feature extraction and reconstruction for human activity recognition (HAR) sensor data. Our Multi-Layer Perceptron (MLP) deep autoencoder achieved a storage reduction of 90.18%, where as convolutional autoencoder achieved 11.18%. For Long-Short Term Memory (LSTM) autoencoder the reduction was 91.47% and for convolutional LSTM autoencoder it was 72.35%. The storage reduction depended on the size and dimension of the concise representation. For higher dimensions of the representation, the storage reduction was low. But relevant information retention was high which was validated by the classification performed on the reconstructed data.
Option Pricing under the CEV Model in a Composite-diffusive Regime

Constant elasticity of variance model for option pricing in a composite-diffusive regime is established. We obtain the Black-Scholes differential equation and the corresponding Black-Scholes formula for the prices of European call option. Furthermore, we discuss an asymptotic expansion of the European call option price as the elasticity factor tends to 2. Keywords—pricing; CEV model; stable subordinator; asymptotic expansion
High repetition rate gain-switched Er:YAG ceramic laser at 1645 nm

We report on a gain-switched Er:YAG ceramic laser resonantly pumped by an acousto-optically modulated Er, Yb co-doped fiber laser at 1532 nm. The laser produced stable pulse trains at 1645 nm with pulse repetition rate tunable between 13–100 kHz and corresponding pulse width of 480–450 ns under a pump power-level of 8.7 W. At the 100 kHz of repetition rate, over 2.2 W of average output power was generated for 6.5 W of incident pump power, corresponding to a slope efficiency of 40% with respect to incident pump power.
Information literacy and its link to evidence-informed policymaking in Zimbabwe

This paper explored the link between information literacy (IL) and other factors that enable or inhibit the utilisation of research evidence in policymaking in Zimbabwe. The study assumes that if policymakers possess appropriate IL skills to access, assess, synthesise, and apply research evidence, they will naturally use the evidence to inform their policy decisions. Face-to-face interviews with 26 policymakers — technocrats selected from the Parliament of Zimbabwe and two ministries, Industry and Commerce, and Youth, Sport, and Recreation — produced evidence to inform the findings and conclusions. Data synthesis using thematic content analysis confirmed the findings. The results show that while IL skills are critical in enabling policymakers' use of research evidence, multiple other factors also influence the use of research evidence in policymaking due to the complexity of the process. The political and socioeconomic context plays a profound role because of the intricate and nonlinear nature of the policymaking process. Therefore, enhancing evidence use in policymaking revolves around strengthening IL skills at the individual level, including institutional and the broader policy ecosystem, by acknowledging and leveraging personal and institutional relationships. This insight illuminates the need to reorient IL programmes to link them to these other factors.
The Effects of Plastic Mulching Combined with Different Fertilizer Applications on Greenhouse Gas Emissions and Intensity, and Apple Yield in Northwestern China

Plastic mulching reduces weeds, conserves soil water, and boosts crop yield. However, most studies are insufficient when determining how plastic mulching affects greenhouse gas (GHG) emissions, particularly when used in conjunction with fertilizers. The purpose of this study was to determine the combined effect of plastic mulching and fertilizers on GHG emissions in apple orchards. A 3-year field experiment was conducted with two factors: mulching and fertilizers; (1) mulching treatments: plastic film (PM) and no mulching (NM); and (2) four fertilizer treatments: control (CK), organic fertilizer (M), inorganic fertilizer (NPK), and organic combined with inorganic fertilizer (MNPK), arranged in a two factorial randomized complete block design. The results showed that the mean annual N2O emissions ranged from 0.87 to 5.07 kg ha−1 in PM and from 0.75 to 2.90 kg ha−1 in NM. The mean CO2 emissions ranged from 2.10 to 6.68 t ha−1 in PM and from 1.98 to 4.27 t ha−1 in NM. MNPK contributed more to N2O and CO2 emissions in both PM and NM. The mean CH4 uptake rate ranged from 1.19 to 4.25 kg ha−1 in PM and from 1.14 to 6.75 kg ha−1 in NM. M treatment contributed more to CH4 uptake in both PM and NM. NKP treatments had higher greenhouse gas intensity (GHGI) in PM and NM, while MNPK and NPK treatments had higher greenhouse gas potential (GWP) in PM and NM, respectively. These results suggest that plastic film mulching significantly raises the potential for soil GHG emissions and increases apple yield.
Payment policy for a three-echelon supply chain management under advertisement-driven demand

Payment and selling are two important policies for a supply chain management. All participating supply chain players can earn profit based on the successful implementation of these two policies. The payment policy provides buyers with some extra time to pay for the product. This research introduces a single-supplier, a single-manufacturer, and multi-retailer-based three-echelon supply chain management under advertising and payment policies. Product delivery among supply chain players is made with the help of a transportation policy. The transportation policy is environment-friendly and helps retailers with their cost management. It is demonstrated that advertising positively influences sales through an advertisement driven market demand for the product. The model aims to reduce supply chain cost and maximize profit by considering a single-setup multiple-delivery policy, variable transportation cost, variable carbon emissions costs, and trade-credit policy. The objective function is optimized for cases: Case 1 and Case 2, based on the payment period. A classical optimization method is employed to obtain the solution of the model. A numerical example, sensitivity analysis, and graphical representations are given to illustrate the model. Results show that Case 2, where cycle time is greater than the payment period, is 45.36% more profitable than Case 1.
Age Invariant Face Recognition Techniques: A survey on the Recent Developments, Challenges and Potential Future Directions

- Face recognition has been a central research topic in recent years. Because it has many different application areas in automatic surveillance systems, passport control, missing person detection and criminal detection, many researchers have studied the problems of face recognition and authentication for many years. However, still, many issues remain to be addressed. The limitation of this topic includes the variability in poses, facial expressions, illumination, and a large age gap between the face images. This paper presents the survey for some commonly used techniques for Age Invariant face recognition (AIFR). A comprehensive overview of the most used age-invariant methods in face recognition is presented, with a comparative overview of different approaches and a comparison in accuracy. Some facial ageing datasets, with their essential characteristics in images, no. of subjects, and age gap, are briefly presented in a tabular view. Commonly used techniques for feature extraction and classification in this field are presented. Major topics covered are experimental results obtained from these methods, issues/challenges, the scope of future work, and conclusions.
Consensus Regularized Selection Based Prediction

CONSENSUS REGULARIZED SELECTION BASED PREDICTION by PING WANG August 2016 Advisor: Dr. Chandan K. Reddy Major: Computer Science Degree: Master of Science Integrating regularization methods within a regression framework has become a popular choice for researchers to build predictive models with lower variance and better generalization. Regularizers also aid in building interpretable models with high-dimensional data which makes them very appealing. Regularizers in general are unique in nature as they cater to data specific features such as correlation, structured sparsity, and temporal smoothness. The problem of obtaining a consensus among such diverse regularizers is extremely important in order to determine the optimal regularizer for the model. This is called the consensus regularization problem which has not received much attention in the literature, due to the inherent difficulty associated with building an integrated regularization framework. To solve this problem, in this thesis, we propose a method to generate a committee of non-convex regularized linear regression models, and use a consensus criterion to determine the optimal model for prediction. Each corresponding non-convex optimization problem in the committee is solved efficiently using the cyclic-coordinate descent algorithm with the generalized thresholding operator. Our Consensus RegularIzation Selection based Prediction (CRISP) model is evaluated on electronic health records (EHRs) obtained from a large hospital for the chronic heart failure readmission problem. We also evaluate our model on various synthetic datasets to assess its performance. The results indicate that CRISP outperforms
Curvature-tuned electronic properties of bilayer graphene in an effective four-dimensional spacetime

We show that in AB stacked bilayer graphene low energy excitations around the semimetallic points are described by massless, four dimensional Dirac fermions. There is an effective reconstruction of the 4 dimensional spacetime, including in particular the dimension perpendicular to the sheet, that arises dynamically from the physical graphene sheet and the interactions experienced by the carriers. The effective spacetime is the Eisenhart-Duval lift of the dynamics experienced by Galilei invariant Levy-Leblond spin $\frac{1}{2}$ particles near the Dirac points. We find that changing the intrinsic curvature of the bilayer sheet induces a change in the energy level of the electronic bands, switching from a conducting regime for negative curvature to an insulating one when curvature is positive. In particular, curving graphene bilayers allows opening or closing the energy gap between conduction and valence bands, a key effect for electronic devices. Thus using curvature as a tunable parameter opens the way for the beginning of curvatronics in bilayer graphene.
Towards an Automated Framework of Root Cause Analysis in the Canadian Telecom Industry

The telecommunication industry today is essential in our everyday life, and providing consistent and stable internet service to the population is the number one priority of telecom companies. That said, it is also important for companies to keep up to date with latest technologies to ensure customers are being o ffered the best services, whether it’s related to speed, reliability etc. As telecom companies introduce new services and technologies, it is important to ensure a seamless delivery and no impact to customers. When that occurs, telecom companies launch investigative processes to examine and analyze the root causes. This process is lengthy and time consuming. In this paper, I propose automating the analysis process by using association rule mining to fast track the investigative process towards identifying root causes.
STATISTICAL APPROACH OF MODULATIONAL INSTABILITY IN CYLINDRICAL/SPHERICAL NLS EQUATION

The modulational (Benjamin-Feir) instability for the cylindrical and spherical nonlinear Schrodinger equation is studied using the statistical approach. A kinetic equation for a two- point correlation function is written and following the Wigner-Moyal approach an evolution equation for the Fourier transform of the correlation function is obtained. A linear stability analysis leads to an implicit integral dispersion relation. This is solved for several forms of the unperturbed correlation functions and the instability domains are determined. Unlike the ordinary NLS case these domains are time dependent and the total growth of the instability is calculated for a Lorentzian initial spectrum.
A Piggyback-Style Algorithm for Learning Improved Shearlets and TGV Discretizations

Summary. This work demonstrates how to use a piggyback-style algorithm to compute derivatives of loss functions that depend on solutions of convex-concave saddle-point problems. Two application scenarios are presented, where the piggyback primal-dual al-gorithm is used to learn an enhanced shearlet transform and an improved discretization of the second-order total generalized variation.