Miracle-dz/UniFAR-FTUs huggingface dataset:
A Mild and Efficient Oxidative Deprotection of THP Ethers with NBS in the Presence of β-Cyclodextrin in Water

An efficient and user-friendly procedure has been developed for the oxidative deprotection of tetrahydropyranyl (THP) ethers with N-bromosuccinimide (NBS) using β-cyclodextrin (β-CD) in water. A series of tetrahydropyranyl ethers were oxidatively deprotected at room temperature in impressive yields.
Synthetic Aperture Sequential Beamforming and other Beamforming Techniques in Ultrasound Imaging

This thesis consists of various subjects and applications within beamforming in general, and subjects within synthetic aperture focusing. An insight into the software architecture and beamformer design principles of a software beamformation toolbox is given. Some of the many considerations in relation to beamformer development and implementation are shared. In the delay-and-sum beamformer the sample index is not necessarily discrete. Some form of interpolation is needed and the influence on image quality has been investigated. The interpolation schemes investigated include linear, polynomial, and up-sampling and FIR filtering. Directional beamforming for angle estimation of the velocity vector has been investigated using simulations and measurements. Using the measurements more than 96%valid estimates were produced for the flow angles θ = {60◦,75◦,90◦} and with a bias below2◦ and a standard deviation below5◦. The two synthetic aperture imaging techniques described in this thesis are both candidates for a realistic implementation in a commercial scanner. In one technique synthetic aperture focusing (SAF) is applied to 2-dimensional imaging with a single rotating mechanically focused concave element. Emission and reception are done while the transducer element continuously rotates and the received RF signals are stored. The geometrical focal point can be considered as a point source emitting a spherical wave in a limited angular region. For each image point in a high resolution image line (HRL) it must be determined which emissions that have a wave field that encompasses the image point. These emissions contribute to the HRL, and samples from each of them are selected according to the focusing delays, and added together. Due to the rotation, the synthesized aperture only experiences a moderate expansion. This is not sufficient to reduce the extent of the wide point spread function of a single emission. The advantage of SAF is the increase in SNR. For the setup with focal depth at 20 mm the SAF SNR gain is 11 dB. The other synthetic aperture focusing technique is similar but has been revised toward linear array imaging. The technique is realized using two beamformers, and denoted Synthetic Aperture Sequential Beamforming (SASB). The VS is now created from an electronic focused subaperture in the first beamformer. Receive focusing is a simple fixed focusing with receive focal point in the transmit focal point, and the first beamformer could easily be analog and thereby save many ADC’s. The focused RF-lines from the first beamformer are stored and transferred to the 2nd beamformer. Here it is exploited that a single image point is represented in multiple output lines from the first beamformer. There is an substantial improvement in lateral resolution using SASB compared to dynamic receive focusing (DRF). The improvement in FWHM
Sub-Image Method Based on Feature Sampling and Feature Fusion for Face Recognition

In this paper,a sub-image method based on feature sampling and feature fusion(called as RS_SpCCA) is proposed. RS_SpCCA first performs a random subspace method in sub-images which are partitioned in a deterministic way.Then,the method obtains correlation features by fusing sampled features and global feature extracted by certain feature extraction method and finally,constructs component classifiers on corrleation features.In this method,the purpose of sampling feature is to construct more diverse component classifiers,and the purpose of the fusing feature is to make good use of the global information.The experimental results on AR,Yale and ORL three face image databases show that sub-image method based on feature sampling and feature fusion(RS_SpCCA) is superior to both SpCCA and Semi-RS which only use feature sampling or feature fusion.
SVM-based Deep Stacking Networks

The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies turn to nonneural network approaches to build diverse deep structures, and the Deep Stacking Network (DSN) model is one of such approaches that uses stacked easy-to-learn blocks to build a parameter-training-parallelizable deep network. In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning. A BP-like layer tuning scheme is also proposed to ensure holistic and local optimizations of stacked SVMs simultaneously. Some good math properties of SVM, such as the convex optimization, is introduced into the DSN framework by our model. From a global view, SVM-DSN can iteratively extract data representations layer by layer as a deep neural network but with parallelizability, and from a local view, each stacked SVM can converge to its optimal solution and obtain the support vectors, which compared with neural networks could lead to interesting improvements in anti-saturation and interpretability. Experimental results on both image and text data sets demonstrate the excellent performances of SVM-DSN compared with some competitive benchmark models.
Specification and Verification of Web Service Composition Based on XYZ/ADL

Web service composition is the hotspot in the field of Web services.Many methods are proposed to describe and verify its correctness.This paper researches specification and verification of Web services composition from software architecture.Refinement checking and model checking are two important formal verification methods.This paper explores the problem of Web service composition based on both software architecture description language XYZ/ADL and formal verification,then proposes a specification and verification method of Web service composition.XYZ/ADL is the extension of the temporal logic language XYZ/E.Considering most Web service with real time characteristics,we can use XYZ/RE which is the real time extension of XYZ/E to express time constraints of the system.For systems with time constraints,we transform the system description to corresponding timed automata according to the mapping rules,then use refinement checking and model checking to verify the correctness of web service composition.Experiments demonstrate feasibility and validity of above idea.
Dynamic wireless sensor and actor networks event driven localization algorithm

Dynamic wireless sensor and actor networks(DWSANs) are composed of a large number of resource-limited sensor nodes and a small number of resource unlimited reactive sensor nodes,which is the derivatives from traditional wireless sensor networks(WSNs). It has very strong practicality and perspective.On the basis of analysis of characteristics of dynamic wireless sensor and actor networks,an event driven localization scheme based on the RSSI range measuring is proposed.The localization algorithm is simulated using OPNET network simulation software,the result proves that the localization algorithm has the feature of higher accuracy and energy efficient.
Electron and spin density analysis of spin-projected unrestricted Hartree-Fock density matrixes of radicals

Electron density analysis of Hartree-Fock wave functions has been largely limited to closed shell systems to date since unrestricted HF wave functions (UHF) are spin contaminated. Spin-projection techniques have been shown to annihilate these spin contaminations successfully. The authors have developed methods for the electron and spin density analysis of projected UHF density matrices of radicals without and with the inclusion of annihilation of the largest spin contaminant via spin projection. In this presentation, electron and spin density functions determined at the UHF and PUHF levels are compared for variety of radicals and differences in their topological and integrated properties are discussed. Methyl and allyl radical are discussed to graphically illustrate the effects of spin annihilation. Representative series of simply binary radicals of first and second row atoms, XH{sub n}, of substituted methyl radicals, CH{sub 2}X, and of selected aza-and oxo-analogues of allyl radical have been considered and trends are discussed. While topological parameters and integrated properties of the electron density differ only very slightly, the authors have found that spin populations determined at the UHF and PUHF levels differ significantly. Comparisons also are made with higher level CASSCF data.
Method of Image Edge Detection Based on Extended Mathematical Morphology

Aiming at the problem that classical morphological method for edge detection can remove the image noise but is difficult to retain the details of the edge,a method that can effectively remove the noise and can accurately detect the details of edges is introduced.The image filtering open and close operation is performed with the large-scale contour structural elements,the gradient operation is conducted by the aid of the small-scale structural elements after the expansion and corrosion operation in the classical morphotogy,and then the edge of the image containing noise information is obtained.Experiments show that the method can effectively remove the image noise while detecting the image edges accurately,and the guantity of computation is relatively small.
Ultrasonic Characterization of Residual Stress in Materials Based on Phase-frequency Relationship

To nondestructively test residual stress in materials, a time of flight measurement of ultrasonic propagation is proposed based on phase-frequency relationship and then an ultrasonic characterization of residual stress is developed based on acoustoelasticity, in which a combined measurement of longitudinal and transverse waves are used. The proposed ultrasonic measurement of residual stress can be applied not only to test the residual stress with a dual-directions, but also to make an exquisite measurement of time of sound flight in a short distance. The accuracy can reach to a sensitivity of 0.3 ns for the specimen of 9 mm’s length under a stress load of 20 MPa. The proposed method is carried out to measurement the distribution of residual stresses in the welded joints of 16Mn steels. Then, theoretical computations and X-ray diffraction also are used to verify the measured results. The result shows that the proposed ultrasonic method has a good repeatability and reliability in experiments, which can be used to test residual stress for welded joints in two axis directions.
Study of the Tracking Parameter of High Speed Following Photography at Initial Stage

This paper built the model of the initial process of following photography for high speed object at short range and worked out acceleration and deceleration time of the mirror by numerical calculation.Then it reckoned the acceleration and deceleration time estimate formulas and proved them though simulation.The results manifested the modeling and the estimate formulas were fit for the initial stage requires.It supplied a tracking model and a method of high speed following photography design for parameters.
Task Scheduling in Distributed Sy stem Based on Dynamic Multi Processing Nodes

【Abstract】Aiming at the problem of poor control ability of distributed system with fixed nodes, this paper gives a task scheduling model of distributed system. The problem of task scheduling in single node is discussed. In order to depict the capability of load-tolerance clearly, the processing intensity concept is drawn. The method of task scheduling among dynamic multi processing nodes is deduced to optimize the characteristic of responsibility time in task scheduling. Simulation experiments show that this algorithm has a better control ability, and it can reduce load of processor, improve the performance of the task-processed delay and use the resource of system more legitimately.
TEXT DEPENDENT SPEAKER IDENTIFICATION BASED ON SPECTROGRAMS

The goal of this paper is to study a new approach to text dependent speaker identiflcation using spectrograms. This, mainly, revolves around trapping the complex patterns of variation in frequency and amplitude with time while an individual utters a given word through spectrogram segmentation. These segmented spectrograms are used as a database to successfully identify the unknown individual from his/her voice. This is a notable drift from the usual Gaussian Mixture Models (GMM) and the Vector Quantization (VQ) techniques for text-dependent speaker identiflcation/veriflcation. The features used for identifying, rely on optimal spectrogram segmentation and the euclidean distance of the distributional features of the spectrograms of the unknown voice with that of a given known speaker in the database. Performance of this novel approach on a sample collected from 40 speakers show that this methodology can be efiectively used to produce a desirable success rate.
A Computational Framework to Model and Learn Context-Specific Gene Regulatory Networks from Multi-Source Data

Reverse engineering gene regulatory networks (GRNs) is an important problem in the domain of Systems Biology. Learning GRNs is challenging due to the inherent complexity of the real regulatory networks and the heterogeneity of samples in available biomedical data. Real world biological data are commonly collected from broad surveys (profiling studies) and aggregate highly heterogeneous biological samples. Popular methods to learn GRNs simplistically assume a single universal regulatory network corresponding to available data. They neglect regulatory network adaptation due to change in underlying conditions and cellular phenotype or both. This dissertation presents a novel computational framework to learn common regulatory interactions and networks underlying the different sets of relatively homogeneous samples from real world biological data. The characteristic set of samples/conditions and corresponding regulatory interactions defines the cellular context (context). Context, in this dissertation, represents the deterministic transcriptional activity within the specific cellular regulatory mechanism. The major contributions of this framework include - modeling and learning context specific GRNs; associating enriched samples with contexts to interpret contextual interactions using biological knowledge; pruning extraneous edges from the context-specific GRN to improve the precision of the final GRNs; integrating multi-source data to learn inter and intra domain interactions and increase confidence in obtained GRNs; and finally, learning combinatorial conditioning factors from the data to identify regulatory cofactors. The framework, Expattern, was applied to both real world and synthetic data. Interesting insights were obtained into mechanism of action of drugs on analysis of NCI60 drug activity and gene expression data. Application to refractory cancer data and Glioblastoma multiforme yield GRNs that were readily annotated with context-specific phenotypic information. Refractory cancer GRNs also displayed associations between distinct cancers, not observed through only clustering. Performance comparisons on multi-context synthetic data show the framework Expattern performs better than other comparable methods.
Investigation of models with temporal and spatialinterference in image based dosimetry of 177Lu-labelled radioligand therapies

Purpose: Multiple measurements have been required to estimate the radiation dose to the kidneys resulting from [Lu]DOTATATE therapy for neuroendocrine tumors. The aim of this study was to investigate the influence of early time-point measurement in the renal dose calculation. Procedures: Anterior/posterior whole-body planar scintigraphy images were acquired at approx. 1, 24, 48, and 72 h after administration of [Lu]DOTATATE. Furthermore, we acquired planar 1-bed dynamic recordings in 12 frames (5 min each) during the first hour. We assessed kidney exposure with a three-phase model consisting of a linear increase to the maximum within the initial minutes p.i. , followed a bi-exponential decline. This three-phase-model served as reference for evaluating accuracy of dose estimates in 105 kidneys calculated by conventional mono-exponential fitting of the final three and four whole-body images. Results: Mean effective half-life times for the reference model were 25.8±12.0 min and 63.9 ±17.6 h, predicting a mean renal dose of 5.7±2.1 Gy. The effective half-life time was 46.3±15.4 h for the last four and 63.3±17.0 h for the last three data points. The mean start of the first wholebody measurement was 1.2±0.1 h p.i. The ratio of fast to slow phases was 28.1±23.9 % at this time point, which caused a mean absolute percentage dose deviation of 12.4 % for four data points, compared to 3.1 % for three data points. At a mean time of 2.4 h p.i. (max 5.1 h), the ratio of fast to slow phase declined below 5 %. Conclusions: Kinetic analysis of renal uptake using dynamic planar scans from the first hour after injection revealed a fast and a slow washout phase. Although the fast phase did not contribute substantially to the estimated renal dose, it could influence planar measurements performed within the first hours. We found that the presence of two clearance phases can hamper accurate dose estimation based on a single-phasemodel, resulting in approximately 12.4% dose underestimation, thus potentially resulting in overtreatment. In the absence of dynamic initial recordings, the first dosimetry measurements should therefore be obtained later than 3–5 h after [Lu]DOTATATE injection. Omitting the early whole-body image reduced the dose estimation error to 3.1 %.
Study on nonpremixed methane/air combustion from flame structure and NOX emission aspect for different burner head structures

In the present study, the air turbulator, which is a part of a nonpremixed burner, is investigated numerically in terms of its effects on the diffusion methane flame structure and NOX emissions. A computational fluid dynamics (CFD) code was used for the numerical analysis. At first, four experiments were conducted using natural gas fuel. In the experimental studies, the excess air ratio was taken constant as 1.2, while the fuel consumption rate was changed between 22 and 51 Nm3/h. After the experimental studies, the CFD studies were carried out. Pure methane was taken as fuel for the simulations. The nonpremixed combustion model with the steady laminar flamelet model (SFM) approach was used in the combustion analyses. Methane‐air extinction mechanism with 17 species and 58 reactions was used for the simulations. The results obtained from the CFD studies were confronted with the measurements of the flue gas emissions in the experimental studies. Then, a modified burner head was analysed numerically for the different air turbulator blade numbers and angles. The CFD results show that increasing the air turbulator blade number and angle causes the thermal NO emissions to be reduced in the flue gas by making the flame in the combustion chamber more uniform than the original case. This new flame structure provides better mixing of the fuel and combustion air. Thus, the diffusion flame structure in the combustion chamber takes the form of the partially premixed flame structure. The maximum reduction in the thermal NO emissions in the flue gas is achieved at 38% according to the original case.
Sliding mode observers for fault detection and isolation

This thesis describes the use of a class of sliding mode observers for fault detection and iso­ lation purposes. Existing work has shown that the equivalent output error injection term as­ sociated with the sliding mode observer, which represents the average value of the nonlinear switched term (which induces and maintains the sliding motion), if properly scaled, yields ac­ curate reconstructions of actuator faults. Existing observer design methods generate a certain class of observer gains, but do not utilise all degrees of freedom. In this thesis, a new method, exploiting this freedom is presented. The method uses Linear Matrix Inequalities and is easily implementable using standard software packages. New methods for accurately reconstructing sensor faults are also presented where appropriate filtering of certain measurable signals yields a fictitious system in which the original sensor faults are treated as actuator faults. Using the principles of actuator fault reconstruction in the existing work, sliding mode observers can be designed for the fictitious system to accurately reconstruct the sensor faults. This improves on the previous work where effectively only the steady state components of the sensor faults could be reconstructed. A new method using Linear Matrix Inequalities is presented, to syn­ thesise observers which can robustly reconstruct faults in the presence of a class system of uncertainty, minimising the effect of the uncertainty on the fault reconstruction in an £ 2 sense. The robust fault reconstruction scheme is demonstrated by means of a case study, which is a nonlinear model of an aero-engine. System identification is used to obtain a linear model of the engine. An uncertainty representation is also obtained about which the observer is designed. The results from the case study show that the robust fault reconstruction scheme works and is effective.
Recursive pruning FFT algorithm based on successive gliding

FFT arithmetic based on successive gliding window is frequently applied in signal spectral line analysis.However,because of the enormous computational complexity,it is hard to be realized in the real time system when window-order is larger or gliding-step is smaller.An improved algorithm based on recursive Pruning FFT is brought forward in this paper.This algorithm makes the most of the last window FFT result and then converts the input sequence into a special pruning sequence which contains only a few nonzero data points,thus can not only reduce the number of operations involved in the calculation of the discrete Fourier transform(DFT)but also improve the flexibility of input and output data points.
A guide to Public Debt equations

This document is a practical guide to the main equations describing and related to Public Debt. We derive all the equations presented and highlight their mathematical meaning. An application of these equations is in Cafiso (2012) for some EU countries.This is the 3rd version of this document (November 2012). The main novelty with respect to the previuos version (January 2012) is the introduction of section 3 which considers the case of foreign-currency denominated debt.