Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of surface cracking in steel pipes based on vibration data using a multi-class support vector machine classifier

In this study, we focused at the development and verification of a robust framework for surface crack detection in steel pipes using measured vibration responses; with the presence of multiple progressive damage occurring in different locations within the structure. Feature selection, dimensionality reduction, and multi-class support vector machine were established for this purpose. Nine damage cases, at different locations, orientations and length, were introduced into the pipe structure. The pipe was impacted 300 times using an impact hammer, after each damage case, the vibration data were collected using 3 PZT wafers which were installed on the outer surface of the pipe. At first, damage sensitive features were extracted using the frequency response function approach followed by recursive feature elimination for dimensionality reduction. Then, a multi-class support vector machine learning algorithm was employed to train the data and generate a statistical model. Once the model is established, decision values and distances from the hyper-plane were generated for the new collected data using the trained model. This process was repeated on the data collected from each sensor. Overall, using a single sensor for training and testing led to a very high accuracy reaching 98% in the assessment of the 9 damage cases used in this study.
Entanglement and defect entropies in gauge/gravity duality

In this thesis we investigate and use geometrical prescriptions for the calculation of entanglement entropy in field theories that have a gravity dual according to gauge/gravity duality. The main results of this work will arise from the application of our findings to the study of entanglement and defect entropies in a holographic model of the Kondo effect. Gauge/gravity duality is an important tool for the study of strongly coupled systems. We give a short review over the related idea of the holographic principle and the realisation of the AdS/CFT correspondence in string theory. We also introduce the concept of entanglement entropy and review the methods of holographically calculating it. We then apply recent prescriptions for calculating holographic entanglement entropy in gravitational theories with higher curvature terms to specific example spacetimes, such as stationary black holes, and obtain analytical solutions for extremal surfaces defining entanglement entropy that wrap around the black holes. We argue that these surfaces are unphysical by discussing how they violate certain well motivated causality constraints. We then investigate the geometrical properties of certain models of dualities between AdS spaces and boundary CFTs, with a special interest in a recently proposed holographic model of the Kondo effect. Understanding the impact of energy conditions on the allowed bulk geometries will be one of the main results of this thesis. We then apply the knowledge gained from these studies to the specific Kondo model, and numerically calculate entanglement and impurity entropies. These quantities can be interpreted in terms of the RG flow that the Kondo model undergoes. It will also be discussed in detail to which extend the holographic model reproduces field theory expectations, and how it can be improved. Furthermore, we investigate recent proposals of defining holographic measures of complexity. This is a quantity in quantum information theory. We end with an outlook on possible future research directions.
Nuances of the Composite Face-lift Technique

The composite face-lift is a well-described, comprehensive facial rejuvenation technique that results in harmonious repositioning of all components of the aging face. It is distinguished by the superior-medial movement of the cheek and the septal reset maneuver. The composite rhytidectomy reverses the unbalanced vectors of the traditional face-lift in patients with the lateral sweep and may reestablish the youthful convexity in patients with hollow eyes.
Gastrointestinal Tumors of the Colon and Rectum

Gastrointestinal stromal tumors (GISTs) of the colon and rectum are the most common mesenchymal tumors of the gastrointestinal tract. GISTs of the colon and rectum constitute (cid:1) 5% of all cases. Although colorectal GISTs can be small and found incidentally, the majority appear to be high risk and carry a signiﬁcant likelihood of recurrent and metastatic disease. Surgery remains the mainstay of treatment for primary disease. There is now considerable interest in GISTs because they can be treated effectively with targeted molecular therapies, speciﬁcally tyrosine kinase inhibitors (TKIs), such as imatinib mesylate and sunitinib malate. GISTs are best treated by a multidisciplinary team comprised of the surgeon, medical oncologist, pathologist, and radiologist in the initial evaluation, management, and in continued follow-up. Increasing the number of resectable cases through pharmacologic debulking, optimizing the timing of surgery and organ preservation, reducing recurrence and surgical morbidity, prolonging survival, and possibly enhancing response to imatinib through surgical cytoreduction are all potential beneﬁts of multidisciplinary management.
Response to Total Sleep Deprivation Before and During Treatment with Fluvoxamine or Maprotiline in Patients with Major Depression - Results of a Double-Blind Study

Summary To test the hypothesis that the antidepressant efTects oftotal sleep deprivation (TSD) are linked to the sero tonergic and/or noradrenergic system the authors carried out a double-blind study (fluvoxamine versus maprotiline) in 42 inpatients with endogenous depression (ICD). Patients were randomized to a four-week treatment with either flu voxamine (100 - 300 mg/day) or maprotiline (100 300 mg/day). In addition, patients underwent a TSD proce dure before and after one week of antidepressant medica tion. There was a statistically significant reduction of depres sion ratings (HDRS) in both the fluvoxamine and maproti line group. The day-I response to TSD before antidepressive medication was not associated with a c1ear relationship to the outcome after four weeks of treatment with either fluvox amine or maprotiline. On the other hand, the day-2 response to TSD was significantly correlated with a good outcome to subchronic treatment with maprotiline. Furthermore, the re sults of the autors' data suggest that a favorable short-term outcome of TSD may be connected to antidepressants en hancing the serotonergic neurotransmission. The global comparison between fluvoxamine and maprotiline revealed that the group of patients treated with fluvoxamine had a sig nificantly higher efficiency index (CGI) than the maprotiline group; fluvoxamine was rated to be tolerataed excellently in 70 % ofthe patients whereas this percentage was only 43 % in the
Non-parametric Bayesian models for structured output prediction

Structured output prediction is a machine learning tasks in which an input object is not just assigned a single class, as in classi cation, but multiple, interdependent labels. This means that the presence or value of a given label a ects the other labels, for instance in text labelling problems, where output labels are applied to each word, and their interdependencies must be modelled. Non-parametric Bayesian (NPB) techniques are probabilistic modelling techniques which have the interesting property of allowing model capacity to grow, in a controllable way, with data complexity, while maintaining the advantages of Bayesian modelling. In this thesis, we develop NPB algorithms to solve structured output problems. We rst study a map-reduce implementation of a stochastic inference method designed for the in nite hidden Markov model, applied to a computational linguistics task, part-of-speech tagging. We show that mainstream map-reduce frameworks do not easily support highly iterative algorithms. The main contribution of this thesis consists in a conceptually novel discriminative model, GPstruct. It is motivated by labelling tasks, and combines attractive properties of conditional random elds (CRF), structured support vector machines, and Gaussian process (GP) classi ers. In probabilistic terms, GPstruct combines a CRF likelihood with a GP prior on factors; it can also be described as a Bayesian kernelized CRF. To train this model, we develop a Markov chain Monte Carlo algorithm based on elliptical slice sampling and investigate its properties. We then validate it on real data experiments, and explore two topologies: sequence output with text labelling tasks, and grid output with semantic segmentation of images. The latter case poses scalability issues, which are addressed using likelihood approximations and an ensemble method which allows distributed inference and prediction. The experimental validation demonstrates: (a) the model is exible and its constituent parts are modular and easy to engineer; (b) predictive performance and, most crucially, the probabilistic calibration of predictions are better than or equal to that of competitor models, and (c) model hyperparameters can be learnt from data.
Numerical modeling of turbulent flow structure and heat transfer in a droplet-laden swirling flow in a pipe with a sudden expansion

ABSTRACT This contribution presents the mathematical model to simulate the swirling turbulent gas-droplet flow in a sudden pipe expansion. The set of axisymmetrical steady-state Reynolds averaged Navier–Stokes equations (RANS) for the two-phase flow is utilized. The dispersed phase is modeled by the Eulerian approach. The flow swirl causes an increase in the intensity of heat transfer (more than 1.5 times compared with the nonswirling mist flow at other identical inlet conditions). Evaporation of the droplets leads to a significant increase in the heat transfer intensity in the swirling two-phase flow (more than 2.5 times compared with the single-phase flow).
Computed Tomography Enterography and Magnetic Resonance Enterography: The Future of Small Bowel Imaging

In the last 5 years, computed tomography enterography (CTE) and to a lesser extent magnetic resonance enterography (MRE) have supplanted the routine small bowel series and enteroclysis in the evaluation of many small bowel diseases, especially Crohn’s disease. Both CTE and MRE use similar methods of bowel lumen opaciﬁcation and distension and both have distinct advantages and disadvantages. Both have been most extensively studied in patients with Crohn’s disease. What is certain is that these cross-sectional examinations have largely replaced the historic ﬂuoroscopic examinations in the evaluation of the small bowel.
Intestinal Fistulas

Enterocutaneous fistulas represent a very complex and life-threatening complication following surgery or as a result of a perforative disease process. At least half of the more common postoperative enterocutaneous fistulas will heal without further operations. However, the management involves a complex process of several phases. Following recognition of an enterocutaneous fistula, sepsis must first be controlled. Healing will be a lengthy process and nutrition must be begun early. Following stabilization of the patient, the fistula is investigated to define whether it is simple or complex and to demonstrate the anatomy. Several factors determine whether a given fistula is likely to heal or will ultimately require additional operations. For some fistulas, healing can be shortened by the use of adjunctive therapies such as vacuum-assisted wound care or fibrin glue infusion. For those fistulas that will require additional operations, the gold standard is resection of the involved intestines with primary anastomosis and repair of the abdominal wall defect. Other novel techniques may be useful for special situations. The successful treatment of an enterocutaneous fistula involves a methodical plan carefully evaluating each phase of management.
Experimental preparation of an arbitrary tunable Werner state

We propose a simple and reliable method for engineering a tunable Werner state and realize it experimentally. The whole setup mainly includes a parametric down-conversion source and a controllable depolarization channel which consists of a Sagnac-like interferometer and some complete dephasing operations. The generated Werner states are characterized by quantum tomography, and very high concurrences and state fidelities can be achieved.
Almost sure convergence for weighted sums of WNOD random variables and its applications to non parametric regression models

ABSTRACT In this article, some results on almost sure convergence for weighted sums of widely negative orthant dependent (WNOD) random variables are presented. The results obtained in the article generalize and improve the corresponding one of J. Lita Da Silva. [(2015), “Almost sure convergence for weighted sums of extended negatively dependent random variables. ” Acta Math. Hungar. 146 (1), 56–70]. As applications, the strong convergence for the estimator of non parametric regression model are established.
IMMUNE SUPPRESSION IN BALB/c MICE BEARING THE LYMPHOCYTE BUT DEFECTIVE MACROPHAGE FUNCTION PLASMACYTOMA TEPC-183 : EVIDENCE FOR NORMAL

This paper analyses impairment of the primary immune response of mice bearing the plasmacytoma TEPC-183. Healthy animals and mice bearing the reported non-immunosuppressive tumour MOPC-104E were used as controls. The defect was shown to affect both primary IgG and IgM responses to chicken cells (CRBC) and to be related to tumour size. However, the primary immune depression could be overcome either by increasing the antigen dose or by using Freund’s complete adjuvant together with antigen. Secondary responses were also depressed. This depression was more pronounced if the animals was primed after, rather than before, tumour implantation. Further studies involved the measurement of primary immune responses of immunologically deprived syngeneic mice, after they had been reconstituted with cells from normal or tumour-bearing mice. Lymphocyte reconstitution experiments were carried out in mice which had been irradiated with 950R. Various lymphoid preparations from TEPC-183-bearing mice were as effective as those from healthy controls in reconstituting primary immune responses. When mice were deprived of macrophage function by the method of Corczynsky et a/. (1971) using large doses of horse red cells macrophage preparations from normal mice were able to partially restore primary immune responsiveness. However, macrophages from TEPC-183-bearing mice were unable to bring about such restoration. It is concluded that the impairment of the primary immune response of mice bearing the plasmacytoma TEPC-183 is due to a macrophage. rather than a lymphocyte, abnormality. However, none of these transfer studies suggested that positive suppression of primary immune responses was being mediated by cells from TEPC-183-bearing mice.
Reconstruction of Acquired Perineovulvar Defects: A Proposal of Sequence

Acquired perineovulvar defects are usually the result of excision of vulvar intra-epithelial neoplasia (VIN) or invasive squamous cell carcinoma. Because both VIN and vulvar carcinoma have a tendency toward local recurrence, future reconstructive options should be reckoned with during treatment of the primary and all subsequent (pre-) malignant perineovulvar lesions. Hence, a proposal of sequence of reconstructive options for these defects is called for. In cases where local skin ﬂaps sufﬁce, these are preferably designed in such a fashion as not to sever the branches of the internal pudendal vascular system. In cases where either a pudendal thigh ﬂap or an infragluteal ﬂap may be used to close the perineovulvar defect, the pudendal thigh ﬂap is to be preferred to preserve the infragluteal ﬂap for future use. Only when these ﬂaps no longer are available or sufﬁcient to cover the defect should the gluteal thigh ﬂap be applied. The use of myocutaneous ﬂaps is rarely indicated to close isolated superﬁcial perineovulvar defects.
A Novel Indexing based Decision tree model on high Dimension Data Stream

As the dimension increases, the complexity to find the nearest neighbor also increases. In one dimension we can find the nearest point very efficiently. But when the dimension increases, the search efficiency depends on how many points we have to search. In this thesis, we propose a simple and efficient technique on how to organize the points in the high dimensional space which also allows searching the nearest point efficiently. We have used a memory efficient database organization of the points in the high dimension, and the searching algorithm is based on limiting the search within some particular indexes of the database. A comparative analysis reveals that our database organization is faster than Etree, R-tree , and the searching algorithm is also very efficient.
Intercalibration of the GPM Microwave Radiometer Constellation

AbstractThe Global Precipitation Measurement (GPM) mission is a constellation-based satellite mission designed to unify and advance precipitation measurements using both research and operational microwave sensors. This requires consistency in the input brightness temperatures (Tb), which is accomplished by intercalibrating the constellation radiometers using the GPM Microwave Imager (GMI) as the calibration reference. The first step in intercalibrating the sensors involves prescreening the sensor Tb to identify and correct for calibration biases across the scan or along the orbit path. Next, multiple techniques developed by teams within the GPM Intersatellite Calibration Working Group (XCAL) are used to adjust the calibrations of the constellation radiometers to be consistent with GMI. Comparing results from multiple approaches helps identify flaws or limitations of a given technique, increase confidence in the results, and provide a measure of the residual uncertainty. The original calibration difference.
Effect of PS-PVD production throughput on Si nanoparticles for negative electrode of lithium ion batteries

Silicon nanoparticles (Si-NPs) have been produced by plasma spray physical vapor deposition at throughput as high as 1 kg h−1 (17 g min−1) and the effect on the battery performance is investigated. When the Si powder feed-rate is changed from 1 to 17 g min−1, although the average primary particle size increases to 50 nm, the cycle capacity of the batteries using these Si-NPs is improved slightly owing to their less agglomerated structure. In contrast, when Ni is added to Si feedstock, the cycle capacity is improved at 1 g min−1 due to modified Si-NP structure having SiNi2 interface. Whereas, the batteries with the Si-NP produced at 17 g min−1 shows significant decrease in the cycle capacity because of the excess Ni silicide formation that is resulted from the elevated co-condensation point and the increased reaction area at high throughputs despite the constant Ni concentration in the feedstock.
Simple and efficient tree pattern matching

A simple and efficient algorithm for an extension of the subtree isomorphism problem is presented that computes a certificate for each rooted subtree of a given forest, thereby partitioning the set of rooted subtrees into isomorphism equivalence classes. The partitioning can be used to find all occurrences of a pattern tree in a text tree, or even all occurrences of every subtree of a pattern in a text, and the algorithm handles multiple pattern trees and also multiple text trees. The method combines a bottom-up forest traversal algorithm with a simple numbering scheme for rooted trees. The algorithm runs in expected time linear in the number of nodes, and can be applied to rooted trees of unbounded degree, either unordered or ordered, labeled or unlabeled. The algorithm also solves the problem of finding all $k$-th largest common subtrees and all $k$-th most often repeated subtrees. A C++ implementation of the algorithm using LEDA is given in full detail.
A Differential Equations Project: Drone Heading Home

Abstract Project-based learning supports unique and authentic problems in differential equations courses. An intuitive, interesting and deep differential equations project is described. The description illustrates a case study of guiding students through a complex project and the technical and personal rewards gained from it. Valuable advice is provided to instructors in the instructional process, and the rubric is also made available.
Optimal control for a higher-order nonlinear parabolic equation describing crystal surface growth

Abstract. In this paper, we shall study the optimal control of the initial-boundary value problem of a higher-order nonlinear parabolic equation describing crystal surface growth. The existence and uniqueness of weak solutions to the problem are given. According to the variational method, optimal control theories and distributed parameter system control theories, we can deduce that the norm of the solution is related to the control item and initial value in the special Hilbert space. The optimal control of the problem is given, the existence of optimal solution is proved and the optimality system is established.
Multi-objective optimisation of the model parameters for the realisable k-ε turbulence model

The broad family of k-e turbulence models is very often used in engineering computational fluid dynamics (CFD). All these models contain semi-empirical parameters that have been determined based on idealised flows. However, it is important to recall that the model parameters are not universal and not even necessarily constant. Consequently, a large span of model parameters can be found in the scientific literature. The objective of the present study is to determine optimised but generally applicable model parameters for the prediction of turbulent quantities for the realisable k-e model. Four canonic flow configurations are considered simultaneously in the optimisation: channel flow, flow over a backward-facing step, jet flow, and flow over a periodic hill. The optimisation problem thus involves several concurrent objectives that must be fulfilled simultaneously. The obtained optimised model parameters do not differ tremendously from the standard recommended values. Nevertheless, for all the configurations the optimised model delivers better, or at least equally good results compared with experimental measurements. Therefore, employing the model parameter values proposed in this work is recommended, since a slightly improved prediction is obtained at no additional numerical cost.
Peptide refinement by using a stochastic search

Identifying a peptide on the basis of a scan from a mass spectrometer is an important yet highly challenging problem. To identify peptides, we present a Bayesian approach which uses prior information about the average relative abundances of bond cleavages and the prior probability of any particular amino acid sequence. The scoring function proposed is composed of two overall distance measures, which measure how close an observed spectrum is to a theoretical scan for a peptide. Our use of our scoring function, which approximates a likelihood, has connections to the generalization presented by Bissiri and co‐workers of the Bayesian framework. A Markov chain Monte Carlo algorithm is employed to simulate candidate choices from the posterior distribution of the peptide sequence. The true peptide is estimated as the peptide with the largest posterior density.