Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear hydroelastic waves on a linear shear current at finite depth

This work is concerned with waves propagating on water of finite depth with a constant-vorticity current under a deformable flexible sheet. The pressure exerted by the sheet is modelled by using the Cosserat thin shell theory. By means of multi-scale analysis, small amplitude nonlinear modulation equations in several regimes are considered, including the nonlinear Schrödinger equation (NLS) which is used to predict the existence of small-amplitude wavepacket solitary waves in the full Euler equations and to study the modulational instability of quasi-monochromatic wavetrains. Guided by these weakly nonlinear results, fully nonlinear steady and time-dependent computations are performed by employing a conformal mapping technique. Bifurcation mechanisms and typical profiles of solitary waves for different underlying shear currents are presented in detail. It is shown that even when small-amplitude solitary waves are not predicted by the weakly nonlinear theory, we can numerically find large-amplitude solitary waves in the fully nonlinear equations. Time-dependent simulations are carried out to confirm the modulational stability results and illustrate possible outcomes of the nonlinear evolution in unstable cases.
Dynamic Intermode Beat Frequency Control of an Optical Frequency Comb Single Section Quantum Dot Laser by Dual-Cavity Optical Self-Injection

Dynamic frequency tuning of the 40.67 GHz intermode beat frequency of a 1255 nm emitting 1 mm long monolithic self mode-locked single section optical frequency comb InAs/InGaAs quantum dot laser across 70 MHz is experimentally demonstrated by fine-delay dual-cavity controlled all optical self-injection. Fiber-based macroscopic optical delay lengths are 9.4 m (round-trip time of 62.7 ns) and 16.5 m (round-trip time of 110.1 ns), the maximum studied microscopic delay tuning times are 40 ps and the optical self-injection strengths are below 0.02%. For selected delay times, the lowest intermode beat frequency line width amounts to 2 kHz indicating an improvement of carrier phase coherence by a factor of 700 as compared to the free-running laser. We validate these experimental results by a simple and universal stochastic time-domain model which is applied for the first time to model a self mode-locked quantum dot laser subject to optical self-injection. Modeling results are in good quantitative agreement.
A New Multi-objective Artificial Bee Colony Algorithm for Optimal Adaptive Robust Controller Design

Artificial bee colony algorithm as a recent meta-heuristic algorithm, inspired from the foraging behavior of honey bees, can be considered as a proper technique to handle optimization problems. In this paper, a multi-objective artificial bee colony algorithm is introduced in which an archive is defined to store non-dominated solutions. Furthermore, to reduce computations and to have evenly distributed solutions, the archive is pruned using a technique based on neighborhood radius concepts. A group of bees that is responsible for improvement of the solutions chooses and exploits one solution of the archive. Because of the definition of neighborhood radius and retaining adjacent solutions, the remaining solutions have an equal chance to be selected by onlooker bees. In order to examine the proposed algorithm, some benchmark functions are used and the results are compared with true Pareto fronts. Moreover, the algorithm is utilized to optimize the coefficients of a new combined controller applied to a ball-beam system. In fact, the proposed controller is a combination of robust decoupled sliding mode and adaption laws based on the gradient decent method. The objective functions are considered as the integral time of absolute of errors of the ball position and the beam angle that should be minimized with a constraint on the control effort. To evaluate and validate the suggested approach, the obtained time responses of the ball-beam system are compared with those of other recently reported controllers.
Thermomechanical responses in metal films under mechanical shock: A molecular dynamics study

Abstract A series of dynamic shock compressions of metal films are simulated using molecular dynamics method. Characteristics of mechanical shock load are analyzed theoretically. Thermomechanical responses induced by mechanical shock, including pressure, temperature, volumetric strain, and shear strain, are presented graphically and analyzed from viewpoint of thermomechanical coupling. The results show that the temperature rise caused by volumetric strain (elastic) is reversible, while that caused by shear strain is irreversible. Under the same mechanical shock load, several metal films exhibit significant differences in thermomechanical responses and the potential reasons are analyzed from the differences of thermophysical parameters.
PENERAPAN ALGORITMA RECURSIVE BEST FIRST SEARCH (RBFS) DALAM PENYELESAIAN TRAVELING SALESMAN PROBLEM (TSP) DI PT. BINTANG SERVICE MANAGEMENT

Banyaknya perusahaan dalam industri, serta kondisi perekonomian saat ini telah menciptakan suatu persaingan yang ketat antar perusahaan. Persaingan tersebut mengakibatkan mengalihdayakan proses-proses yang bukan merupakan kompetensi utama (core competence) perusahaan tersebut ke pihak lain (outsourcing) agar perusahaan dapat memfokuskan pada kompetensi utama. Masalah yang terjadi pada pihak outsourching (PT. Bintang Service Management) dengan semakin banyaknya klien adalah semakin banyak pilihan rute perjalanan yang harus dilalui pihak outsourcing untuk melakukan pengadaan barang dan pengecekan barang di mana perusahaan berangkat dari kantor dan harus mengunjungi setiap perusahaan klien tepat satu kali, kemudian kembalilagi ke kantor. Masalah ini disebut Traveling Salesman Problem (TSP). Traveling Salesman Problem dapat divisualisasikan dalam bentuk graf Hamilton untuk diselesaikan dengan algoritma Recursive Best First Search (RBFS), sementara salah satu cara untuk mempermudah proses perhitungan dapat dibuat program menggunakan bahasa Hipertext Preprocessor (PHP). Penelitian dilakukan dengan mengambil data klien dari perusahaan otsourching di wilayah Semarang, selanjutnya data dimodelkan dalam bentuk peta graf Hamilton menggunakan library LeafletJS dan data diproses menggunakan algoritma Recursive Best First Search sehingga diperoleh rute terpendek yang divisualisasikan dalam bentuk peta.hbjh Tujuan penelitian yaitu untuk mengetahui: (1) Penerapan algoritma Recursive Best First Search untuk mengatasi Traveling Salesman Problem di PT. Bintang Service Management; (2) Pembuatan program algoritma Recursive Best First Search dalam penyelesaian Traveling Salesman Problem di PT. Bintang Service Management menggunakan bahasa pemrograman Hipertext Preprocessor. Hasil dari penelitian ini yaitu sebuah sikel Hamilton dengan bobot minimum yaitu Bintang Service Management – Semesta Bilingual School – My Kopi O – Hotel Grand Edge – City One Hotel – RS Panti Wilasa Citarum – Leko Gajah Mada – Dafam Hotel – 3 Durian – Kantor Imigrasi – Rumdenim – Goori Swalayan – Payon Amartha – Bintang Service Management. Rute tersebut dapat menjadi acuan pihak outsourching dalam penentuan rute perjalanan untuk melakukan pengadaan barang dan pengecekan barang ke pihak klien perusahaan secara lebih efektif dan efisien.
Sentiment Analysis Using Machine Learning Techniques

Before buying a product, people usually go to various shops in the market, query about the product, cost, and warranty, and then finally buy the product based on the opinions they received on cost and quality of service. This process is time consuming and the chances of being cheated by the seller are more as there is nobody to guide as to where the buyer can get authentic product and with proper cost. But now-a-days a good number of persons depend upon the on-line market for buying their required products. This is because the information about the products is available from multiple sources; thus it is comparatively cheap and also has the facility of home delivery. Again, before going through the process of placing order for any product, customers very often refer to the comments or reviews of the present users of the product, which help them take decision about the quality of the product as well as the service provided by the seller. Similar to placing order for products, it is observed that there are quite a few specialists in the field of movies, who go though the movie and then finally give a comment about the quality of the movie, i.e., to watch the movie or not or in five-star rating. These reviews are mainly in the text format and sometimes tough to understand. Thus, these reports need to be processed appropriately to obtain some meaningful information. Classification of these reviews is one of the approaches to extract knowledge about the reviews. In this thesis, different machine learning techniques are used to classify the reviews. Simulation and experiments are carried out to evaluate the performance of the proposed classification methods. It is observed that a good number of researchers have often considered two different review datasets for sentiment classification namely aclIMDb and Polarity dataset. The IMDb dataset is divided into training and testing data. Thus, training data are used for training the machine learning algorithms and testing data are used to test the data based on the training information. On the other hand, polarity dataset does not have separate data for training and testing. Thus, k-fold cross validation technique is used to classify the reviews. Four different machine learning techniques (MLTs) viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), and Linear Discriminant Analysis (LDA) are used for the classification of these movie reviews. Different performance evaluation parameters are used to evaluate the performance of the machine learning techniques. It is observed that among the above four machine learning algorithms, RF technique yields the classification result, with more accuracy. Secondly, n-gram based classification of reviews are carried out on the aclIMDb dataset.
Spatio-temporal multidimensional collective data analysis for providing comfortable living anytime and anywhere

Machine learning is a promising technology for analyzing diverse types of big data. The Internet of Things era will feature the collection of real-world information linked to time and space (location) from all sorts of sensors. In this paper, we discuss spatio-temporal multidimensional collective data analysis to create innovative services from such spatio-temporal data and describe the core technologies for the analysis. We describe core technologies about smart data collection and spatio-temporal data analysis and prediction as well as a novel approach for real-time, proactive navigation in crowded environments such as event spaces and urban areas. Our challenge is to develop a real-time navigation system that enables movements of entire groups to be efficiently guided without causing congestion by making near-future predictions of people flow. We show the effectiveness of our navigation approach by computer simulation using artificial people-flow data.
WEM Algorithms and Probabilistic Learning

A new class of Expectation and Maximization algorithm is presented and applied to probabilistic learning. This algorithm can be derived from the non-negativity of the α-divergence and Bayesian computation. The design parame 七 erαspecifiesa prior probability weight for the learning. Accordingly ， this algorithm is called the α-Weighted EM algorithm (WEM ， α-EM). The traditional EM algorithm (l og-EM) corresponds to special case of α - 1. Besides the WEM ， a practically more useful version ， the W-GEM (Weighted GEM) ， which gradually updates parameters is presented. Then ， this algorithm is applied to learning in mixture-of-probability neural ne 七 works.In the discussion of update equations ， extensions of basic statistical theories such as Fisher's efficient score ， his information matrix and Cramer-Rao's inequality are given. Therein ， a concept of an aptitude number is defined in terms of the number α. N umerical experiments on such networks show that the WEM (G-WEM) outperforms the log-EM on the learning speed for appropria 七 e aptitude numbers. This paper unveils another new method. It is found 七 hateach WEM structure can be used as a building block to generate a learning systolic array. Thus ，も becomes possible 七 ocreate functionally distributed systems by appending such blocks as monitors.
Test-Drive ASP.NET MVC

It's testable. It's light-weight. It's open-source. It's . Microsoft? Yes, finally, Microsoft has figured it out. ASP.NET MVC 2.0 lets you test drive your code, control the output of your HTML, and leverage C# and . NET in an easy-to-use web framework. This book shows you all you need to know to get started developing web applications using test-driven development (TDD). You'll learn how to do everything from creating your first test, to building REST web services, to deploying your finished ASP.NET MVC applications. Test-Drive ASP.NET MVC gets you started with all the fundamentals you need, whether you're transitioning from older Microsoft technologies, or if you're coming from an open-source background and are curious about what ASP.NET MVC has to offer. ASP.NET MVC 2.0 is C# on the web done right. No more fiddling around with Viewstate, IsPostBack(), and drag-and-drop coding. Microsoft has addressed the shortcomings of ASP.NET and created a framework that goes toe-to-toe with other popular web frameworks such as Ruby on Rails. Programming in C# is fun again! This book takes a test-first approach to the main features of ASP.NET MVC. You'll get hands-on experience building and deploying a time-management web application from start to finish. From building your first page, to data access, to integrating with web services such as Blogger.com, Test-Drive ASP.NET MVC guides you step by step. You'll come away with a complete understanding of web services and data access, and you'll learn how to test each component so that your code is bug-free and maintainable from the start. You'll see how to handle security, logging, and error handling. Software isn't useful until it's deployed into production. We'll cover automated deployment using MSBuild, a build tool product that can help you get your site online, repeatably and reliably. Throughout, you'll work with open source projects that complement ASP.NET MVC, including NHibernate, MVCContrib, and Castle Windsor Container. These tools speed up developing database components, architecture layers, and testing your code. As you build and deploy your application, you'll not only learn the framework itself, but also gain valuable experience with the test-first methodology, driving your application development through small and measurable incremental improvements. (edited by author)
Simulation study for comparison of spatial autoregressive probit estimation methods

One of probit model variant with spatial dependent is spatial autoregressive (SAR) probit model. In SAR probit model, the spatial dependence structure adds complexity to the estimation of parameters. There are four methods for estimating the parameter of SAR probit model; maximum likelihood, Bayes, linearized GMM, and conditional approximate likelihood. The purpose of this article is to choose the best estimation method from four methods describes above using some extensive simulation which can handle sample sizes with large observations and various value of spatial lag coefficient, provided the spatial weight matrix is in an inconvenient sparse form, as is for large data sets, where each observation neighbors only a few other observations. The best estimation method is chosen based on the shortest confidence interval for the mean of SAR probit estimation, lowest bias, and Root Mean Square Errors (RMSE) of prediction. It was found that conditional approximate likelihood method was the best among the four methods concerning confidence interval and bias, yet regarding estimating RMSE, maximum likelihood estimation performed better. Maximum likelihood, Bayes, and conditional approximate likelihood method were better than linearized GMM in SAR probit parameter estimation for large dataset.
Topology and models of ZFC at early Universe

Recently the cosmological evolution of the universe has been considered where 3-dimensional spatial topology undergone drastic changes. The process can explain, among others, the observed smallness of the neutrino masses and the speed of inflation. However, the entire evolution is perfectly smooth from 4-dimensional point of view. Thus the raison d’etre for such topology changes is the existence of certain non-standard 4-smoothness on R4 already at very early stages of the universe. We show that the existence of such smoothness can be understood as a byproduct of the quantumness of the origins of the universe. Our analysis is based on certain formal aspects of the quantum mechanical lattice of projections of infinite dimensional Hilbert spaces where formalization reaches the level of models of axiomatic set theory.
Keeping Track with Things

The chapter begins with an evolutionary account of tracking systems, from simple detection systems to complex decoupled and highly flexible tracking systems. The important mediator is the role of the environment in providing the complexity, translucency, and hostility that produces the evolutionary pressures that result in more complex tracking systems. An evolutionary platform is provided for how modern humans could have come to innovate epistemic tracking tools (ETTs) for keeping track of salient features of the environment. Three examples of ETTs in action are given, ranging from highly iconic and contextual learning tools—such as the Mattang—to highly abstract and decoupled conventional symbol systems. Finally, it is argued that ETTs are compatible with a responsibilist-reliabilism since their correct deployment requires epistemic diligence and the reliable functioning of the tool itself. As such, a framework for understanding and exploring how we keep track with things has been given.
Dust-Acoustic Shock Waves in a Self-Gravitating Opposite Polarity Dusty Plasmas With Trapped Ions

The basic characteristics of dust-acoustic (DA) shock waves (DASHWs) in self-gravitating dusty plasmas containing massive dust of opposite polarity, trapped ions, and Boltzmann electrons has been studied. The reductive perturbation technique has been employed to derive standard modified Burgers equation (mBE). The basic properties (viz., amplitude, width, and speed) of small but finite-amplitude DASHWs are significantly modified by the combined effects of positively and negatively charged dust component, self-gravitational force, and trapped ions. The implication of our investigation can be very effective for understanding and studying the nonlinear characteristics of the DA waves (DAWs) in laboratory and space dusty plasmas.
Group B Streptococcus colonization induces Prevotella and Megasphaera abundance-featured vaginal microbiome compositional change in non-pregnant women

Background Previous studies have indicated that variations in the vaginal microbiome result in symptomatic conditions. Group B Streptococcus (GBS) is a significant neonatal pathogen and maternal vaginal colonization has been recognized as an important risk factor for neonatal disease. Therefore, it is important to discover the relationship between the composition of the vaginal microbiome and GBS colonization. This study explores the potential relationship between the composition of the vaginal microbiome and GBS colonization in non-pregnant Chinese women. Methods A total of 22 GBS-positive, non-pregnant women and 44 matched GBS-negative women were recruited for the current study. The composition of the vaginal microbiome was profiled by sequencing the 16S rRNA genes. The microbiome diversity and variation were then evaluated. Results The vaginal microbiome of the 66 subjects enrolled in the current study were compared and the results showed that GBS-positive women exhibited significant vaginal microbial differences compared with the GBS-negative women based on the analysis of similarities (r = 0.306, p < 0.01). The relative abundance of the bacterial genus Lactobacillus (p < 0.01) was significantly lower in the GBS-positive group, while the abundances of the bacterial genera Prevotella (p < 0.01), Megasphaera (p < 0.01), and Streptococcus (p < 0.01) were significantly higher in the GBS-positive group. Discussion The current study addressed significant variations across the communities of the vaginal microbiome in GBS-positive and GBS-negative women in a Chinese cohort, which paves the way for a larger cohort-based clinical validation study and the development of therapeutic probiotics in the future.
Coherent Imaging Through Multicore Fibres With Applications in Endoscopy

Imaging through optical fibres has recently emerged as a promising method of micro-scale optical imaging within a hair-thin form factor. This has significant applications in endoscopy and may enable minimally invasive imaging deep within live tissue for improved diagnosis of disease. Multi-mode fibres (MMF) are the most common choice because of their high resolution but multicore fibres (MCF) offer a number of advantages such as widespread clinical use, ability to form approximate images without correction and an inherently sparse transmission matrix (TM) enabling simple and fast characterisation. We present a novel experimental investigation into properties of MCF important for imaging, specifically: a new method to upsample and downsample measured TMs with minimal information loss, the first experimental measurement of MCF spatial eigenmodes, a novel statistical treatment of behaviour under bending based on a wireless fading model, and an experimental observation of TM drift due to self-heating effects and discussion of how to compensate this. We next present practical techniques for imaging through MCFs, including alignment, how to parallelise TM characterisation measurements to improve speed and how to use non-interferometric phase and polarisation recovery for improved stability. Finally, we present two recent applications of MCF imaging: polarimetric imaging using a robust Bayesian inference approach, and entropic imaging for imaging early-stage tumours.
Asymptotic Analysis of Frictional Contact Problem for Piezoelectric Shallow Shell

The objective of this work is to study the asymptotic justification of a new two-dimensional model for the equilibrium state of a piezoelectric linear shallow shell in frictional contact with a rigid foundation. More precisely, we consider the Signorini problem with Tresca friction of a piezoelectric linear shallow shell in contact with a rigid foundation. Then, we establish the convergence of the mechanical displacement and the electric potential as the thickness of the shallow shell goes to zero.