Miracle-dz/UniFAR-FTUs huggingface dataset:
Extraction of selected minerals from Melissa officinalis L. leaves by using two-level factorial design

In this study, a two-level factorial design was used to evaluate the significant extraction parameters in achieving higher recovery yield of Cu, Fe, Mn, and Zn contents from Melissa officinalis leaves. The independent parameters were solid-to-solvent ratio, extraction temperature, and duration of extraction. The experimental data obtained were fitted to a first-order polynomial equation using multiple regression analysis and studied by appropriate statistical methods (ANOVA). The optimum extraction conditions were as follows: solid-to-solvent ratio 1:15 g/mL, extraction temperature 100°C, and extraction time 80 min. Under these conditions, the experimental concentrations were in close agreement with the value predicted by the model. The correlation coefficients of 0.9947, 0.9966, 0.9939, and 0.9957 observed between the predicted and actual values for the response variables are evidence that the regression model can represent the experimental data well. Minerals present in the extracts may be maximized when process conditions are carefully adjusted within the reported values.
A threshold auto-adjustment algorithm of feature points extraction based on grid

When dealing with high-resolution digital images, detection of feature points is usually the very first important step. Valid feature points depend on the threshold. If the threshold is too low, plenty of feature points will be detected, and they may be aggregated in the rich texture regions, which consequently not only affects the speed of feature description, but also aggravates the burden of following processing; if the threshold is set high, the feature points in poor texture area will lack. To solve these problems, this paper proposes a threshold auto-adjustment method of feature extraction based on grid. By dividing the image into numbers of grid, threshold is set in every local grid for extracting the feature points. When the number of feature points does not meet the threshold requirement, the threshold will be adjusted automatically to change the final number of feature points The experimental results show that feature points produced by our method is more uniform and representative, which avoids the aggregation of feature points and greatly reduces the complexity of following work.
Towards Unsteady Adjoint Analysis For Turbomachinery Applications

This paper presents a general framework to derive an unsteady discrete adjoint method as part of a gradient-based aerodynamic shape optimization process. The method calculates the gradient of an objective function w.r. t. the design variables of the aerodynamic shape. The formulation of the adjoint equations is introduced within a generic time-dependent optimal design problem. Results are shown that demonstrate the solution of the unsteady adjoint equations for a representative row of nozzle guide vanes of a high pressure turbine
Two-sample Hypothesis Testing for Random Dot Product Graphs via Adjacency Spectral Embedding

We consider a semiparametric problem of two-sample hypothesis testing for a class of latent position random graphs. We formulate a notion of consistency in this context and propose a valid test for the hypothesis that two finite-dimensional random dot product graphs on a common vertex set have the same generating latent positions or have generating latent positions that are scaled or diagonal transformations of one another. Our test statistic is a function of a spectral decomposition of the adjacency matrix for each graph and our test procedure is consistent across a broad range of alternatives.
Approximation algorithms for facility location problems

One of the most important aspects of logistics is to decide where to locate new facilities such as plants, distribution centers, and retailers. Facility location models not only have important applications in designing distribution systems, but also often form identifiable parts of other practical problems. However, many discrete location problems are NP-hard and the scale of the instances arising in practice is often too large to be solved optimally. In this thesis, we focus on polynomial time approximation algorithms for solving the well-known uncapacitated facility location problem and its generalizations. In the uncapacitated problem, we are given a set of clients; a set of possible locations for facilities, the cost of opening a facility at each location, and the cost of connecting each client to a facility at each location. The objective is to open facilities at a subset of these locations, and connect each client to an open facility to minimize the sum of facility opening and connection costs. We assume that connection costs obey the triangle inequality. It is known that a 1.46-approximation algorithm for the uncapacitated problem would imply P = NP. In this thesis, we improve a long line of previous results and give a 1.52-approximation algorithm for the uncapacitated problem. We also consider several important generalizations of the uncapacitated problem, including: (1) Capacitated facility location: Each facility can serve only a certain amount of clients. We present a multi-exchange local search algorithm for this problem. We show its approximation ratio is between 3 + 22 − e and 3 + 22 + e for any given constant e > 0. (2) Multi-level facility location: The demands must be routed among the facilities in a hierarchical order. We give the best combinatorial algorithm for this problem. For the special case when there are only two levels of facilities, we give a 1.77-approximation algorithm, which is currently the best. (3) Dynamic facility location: The demand varies between time-periods. In addition to the question of where to locate facilities, this problem also addresses the question of when to locate them. We develop the first approximation algorithm for this problem.
Robust and accurate UWB‐based indoor robot localisation using integrated EKF/EFIR filtering

A novel ultra wideband (UWB)-based scheme is proposed to provide robust and accurate robot localisation in indoor environments. An extended Kalman filter (EKF), which is suboptimal, is combined in the main estimator design with an extended unbiased finite impulse response (EFIR) filter, which has better robustness. In the integrated EKF/EFIR algorithm, the EFIR filter and the EKF operate in parallel and the final estimate is obtained by fusing the outputs of both filters using probabilistic weights. Accordingly, the EKF/EFIR filter output ranges close to the most accurate one of the EKF and EFIR filters. Experimental testing has shown that the EKF/EFIR-based UWB-range robot localisation is more robust than the EKF- and EFIR-based ones in uncertain noise environments.
Turbulent Thermonuclear Combustion in Degenerate Stars

The thesis is mainly concerned with turbulence and its significance for thermonuclear burning processes in degenerate stars. Contrary to main sequence stars such as the Sun, in which a feed-back mechanism between reaction rates, expansion and cooling moderates thermonuclear burning, the physical conditions in a white dwarf of nearly critical mass entail a thermonuclear runaway, once density and temperature rise above a certain threshold. The critical mass of a white dwarf is known as Chandrasekhar mass. In the course of the run-away, carbon and oxygen is rapidly burned to heavier elements, in particular, nickel, and the star explodes due to the enormous energy release. According to our present understanding, burning in these thermonuclear supernovae progresses as deflagration, which means that ignition is caused by heat conduction rather than shock compression. Since the intrinsic propagation speed of a deflagration front is much less than the speed of sound, there must be something acting to accelerate the burning process. The agent is thought to be turbulence, which folds and wrinkles flames and thereby increases the rate of burning. A major difficulty of describing turbulent burning in numerical simulations stems from the fact that it is impossible to resolve the whole range of dynamical scales, even with the most powerful of currently available computers. This restriction leads to the concept of a large-eddy simulation, in which only the largest scales are numerically resolved. However, since the burning process is susceptible to turbulent velocity fluctuations on scales smaller than the numerical resolution, a model which accounts for effects on these scales is indispensable. The investigation of several options for such a subgrid scale model is the research subject of this Thesis. As testing and comparing different subgrid scale models systematically in full supernova simulations would be quite hard, a simplified scenario was chosen, where turbulence is artifically produced by a stochastic force field in a cubic domain. In the beginning, pure hydrodynamical turbulence was investigated, and then thermonuclear burning was added. The research goal has been to some extent a phenomenological understanding of turbulence and turbulent burning, but eventually it aims at a subgrid scale model, which makes physically sound predictions and is applicable to simulations of thermonuclear supernovae.
Erratum: Inducing and manipulating magnetization in 2D zinc–oxide by strain and external voltage (2018 J. Phys.: Condens. Matter 30 145802)

Two-dimensional (2D) structures that exhibit intriguing magnetic phenomena such as perpendicular magnetic anisotropy and its switchable feature are of great interests in spintronics research. Herein, the density functional theory studies reveal the critical impacts of strain and external gating on vacancy-induced magnetism and its spin direction in a graphene-like single layer of zinc oxide (ZnO). In contrast to the pristine and defective ZnO with an O-vacancy, the presence of a Zn-vacancy induces significant magnetic moments to its first neighboring O and Zn atoms due to the charge deficit. We further predict that the direction of magnetization easy axis reverses from an in-plane to perpendicular orientation under a practically achievable biaxial compressive strain of only ~1 – 2% or applying an electric field by means of the charge density modulation. This magnetization reversal is mainly driven by the strain- and electric-field-induced changes in the spin–orbit coupled d states of the first-neighbor Zn atom to a Zn-vacancy. These findings open interesting prospects for exploiting strain and electric field engineering to manipulate magnetism and magnetization orientation of 2D materials. in
Study of Mechanical Response of Paper-Based Microfluidic System as a Potential Milk Tester

Milk is considered a complete meal that requires supervision to determine its suitability for human consumption. The development of sustainable devices that evaluate food properties has gained importance due to the necessity of integrating these instruments into the production chain. However, the materials employed to develop it, such as polymers, semiconductors, and glass, lack sustainability and require specialized equipment to fabricate them. Different chemical techniques have been used to miniaturize these detection systems such as microfluidics, which have been used in milk component detection using colorimetry. In this work, a cantilever beam paper-based microfluidic system is proposed to evaluate differences in milk, according to nutritional information, using its electromechanical response. A 20-microliter milk drop is deposited in the system, which induces hygroexpansion and deflection due to liquid transport within the paper. Likewise, a conductive path is added on the beam top surface to supply a constant current that induces heat to evaporate the solution. According to the results obtained, it is possible to point out differences between trademarks with this microfluidic system. The novelty of this system relies on the paper electromechanical response that integrates the hygroexpansion-induced displacement, which can be used for further applications such as milk microtesters instead of colorimetric tests that use paper as a property-evaluation platform in combination with chemical reactions.
Contraindre les échanges côte-large et la pompe biologique de carbone par modélisation inverse de deux radio-isotopes (radium228 et thorium234)

Les cycles oceaniques du carbone et des principaux nutriments sont mal connus car ils sont affectes par de nombreux puits et sources physiques, chimiques ou biologiques difficiles a estimer par des mesures directes. Une maniere de mieux contraindre ces processus importants est d’utiliser l’information contenue dans des traceurs plus simples : les proxies. Le radium 228 (228Ra), emis par les plateaux continentaux, est utilise comme proxy des flux d’eau et d’elements mineraux de la cote vers l’ocean ouvert. Il permet en particulier d’estimer les flux d’eau souterraine ou SGDs (Submarine Groundwater Discharge). Le thorium 234(234Th), insoluble, permet quant a lui de contraindre la dynamique des particules par lesquelles il est adsorbe. Il est regulierement utilise pour estimer la pompe biologique du carbone (PBC), c’est-a-dire le flux de carbone de la surface vers l’ocean profond.Au cours de cette these, un modele numerique a une resolution de 2° a ete construit pour chacun de ces deux radio-isotopes, en s’appuyant sur la circulation du modele NEMO-OPA et les champs de particules du modele PISCES. Plusieurs parametres inconnus des modeles ont ete contraints par des observations dans le cadre d’une methode inverse.La modelisation inverse du 228Ra a permis d’estimer les flux de 228Ra venant de 38 regions cotieres. En revanche, l’estimation des SGDs est imprecise, car les SGDs sont difficiles a distinguer d’une autre source de 228Ra: la diffusion par les sediments.La modelisation inverse du 234Th a permis d’estimer les coefficients de partage du 234Th, qui representent l’affinite de differents types de particules pour cet isotope. Elle a aussi permis d’estimer les erreurs associees a quelques simplifications courantes dans les etudes de la PBC fondees sur le 234Th.
Data-Driven Prior Distributions for A Bayesian Phase-2 COPD Dose-Finding Clinical Trial

ABSTRACT The prior distribution reflects knowledge and uncertainty of the modeled parameters. Determining the prior distribution for a dose-finding clinical trial can be influential in its design and analysis. Using the planning of a phase 2 trial for COPD with a dose-response curve as a case study, we illustrate the use of relevant historical data for the nonlinear curve mean-model parameters as well as consideration for terms to characterize between-trial and within-trial variability. Through a predictive inference exercise, a data-driven informative prior distribution is constructed for the future study. We share our strategies on how to obtain informative Bayesian priors for both design and analysis of dose-finding clinical trials using relevant historical data and deal with the associated issues.
Transferring linear motion of an optical wedge to rotational frequency shift in an orbital angular momentum interferometer

We build a modified Mach-Zehnder (M-Z) interferometer with an embedded Dove prism in one arm to observe the interference between two conjugate orbital angular momentum (OAM) beams. By inserting and moving an optical wedge vertically in the other arm, we find that its linear motion can induce a rotational frequency shift equivalently, as a consequence of phase transfer from the path difference to the azimuthal difference between two OAM beams. The micron-scale movement of the wedge is driven by a compact motorized translation stage and is manifested by a significant rotation of the interference petal-like patterns. Our scheme offers an accurate method to measure the optical wedge angle with a simple method of digital image processing. This work may also find potential applications in the field of velocity sensing or temperature sensing.
Electroluminescence and electrical degradation of insulating polymers at electrode interfaces under divergent fields

Electrical degradation of insulating polymers at electrode interfaces is an essential factor in determining long-term reliability. A critical challenge is that the exact mechanism of degradation is not fully understood, either experimentally or theoretically, due to the inherent complex processes. Consequently, in this study, we investigate electroluminescence (EL) at the interface of an electrode and insulator, and determine the relationship between EL and electrical degradation. Using a tip-plate electrode structure, the unique features of EL under a highly divergent field are investigated. The voltage type (alternating or direct current), the polymer matrix, and the time of pressing are also investigated separately. A study of EL from insulators under a divergent field is provided, and the relationship between EL spectra and degradation is discussed. It is shown that EL spectra under a divergent field have unique characteristics compared with EL spectra from polymer films under a uniform field and the .
An Automated Python Script for Data Cleaning and Labeling using Machine Learning Technique

was to obtain a financial dataset from the top database, "Kaggle". Create a machine learning (ML) approach in Python that intends to automate the financial dataset cleaning. This covers ingesting data, addressing incomplete data, addressing anomalies, one-hot wrapping and label encoding, extracting date and time values, and data normalization. Implementing an unsupervised machine learning method that attempts to automate financial dataset labeling (k-means). Using the method includes the elbow principle, k-means clustering, data modeling of "age" versus "arrival," dimensionality reductions, computer vision, and dataset categorizing using the groupings. An e mpirical assessment of the cleaned and labeled automated trading dataset utilizing a comparison of the cleaned dataset before and after PCA adoption. The results show that the developed ML technique not only improved the performance of the audit data used in this study, but also classified the data after cleaning it and removing the unpleasant section and incomplete data, as shown by the k-means segmentation result and grouping by PCA.
Pancharactnam-Berry phase used for realizing spin-dependent propagation and polarization measurement

Pancharatnam-Berry (PB) phase is known as the geometric phase associated with the polarization transformation of light. In this work, we reveal the PB phase of light generated during the transformation from a single polarization to a spacevariant one. To realize the space-variant polarization transformation, we utilized our early developed approach based on a Sagnac interferometer. The two spin states of the output field contain different PB phases. By tailoring the PB phase, we demonstrate the spin-dependent propagations of light beams. Besides, the PB phase can also be used for measuring the polarization distribution of vector beams.
Discriminant analysis of time series using wavelets

Summary. We consider the use of wavelets for the classiﬁcation of time series. In particular we use time independent wavelet variances as inputs into diagonal quadratic discriminant analysis. Simulations studies show that our procedure performs very well resulting in small training and hold-out sample classiﬁcation errors. Applications to real data show that our procedure performs as well and in some cases better than other classiﬁcations methods.
Use of Bayesian Inference for Parameter Recovery in DC and AC Voltammetry

We describe the use of Bayesian inference for quantitative comparison of voltammetric methods for investigating electrode kinetics. We illustrate the utility of the approach by comparing the information content in both DC and AC voltammetry at a planar electrode for the case of a quasi-reversible one electron reaction mechanism. Using synthetic data (i.e. simulated data based on Butler-Volmer electrode kinetics for which the true parameter values are known and to which realistic levels of simulated experimental noise have been added), we are able to show that AC voltammetry is less affected by experimental noise (so that in effect it has a greater information content then the corresponding DC measurement) and hence yields more accurate estimates of the experimental parameters for a given level of noise. Significantly, the AC approach is shown to be able to distinguish higher values of the rate constant. The results of using synthetic data are then confirmed for an illustrative case of experimental data for the $[$Fe(CN)$_6]^{3-/ 4-}$ process.
LSST telescope modeling overview

During this early stage of construction of the Large Synoptic Survey Telescope (LSST), modeling has become a crucial system engineering process to ensure that the final detailed design of all the sub-systems that compose the telescope meet requirements and interfaces. Modeling includes multiple tools and types of analyses that are performed to address specific technical issues. Three-dimensional (3D) Computeraided Design (CAD) modeling has become central for controlling interfaces between subsystems and identifying potential interferences. The LSST Telescope dynamic requirements are challenging because of the nature of the LSST survey which requires a high cadence of rapid slews and short settling times. The combination of finite element methods (FEM), coupled with control system dynamic analysis, provides a method to validate these specifications. An overview of these modeling activities is reported in this paper including specific cases that illustrate its impact.
The Inertia Value Transformation in Maritime Applications

Due to recent developments in computer technology, computer-aided investigations of structural movements in a maritime environment have become more relevant during the last years. With regard to mechanically coupled multibody systems in fishery and offshore operations, the analysis of such systems is in the focus of research and development. To analyse multibody systems, forces and moments of all included bodies have to be defined within the same reference frame, which requires a transformation algorithm. Showing the correctness of the transformation algorithm, it must be also applicable for six degrees of freedom (6DOF) motions of a free floating single body in seaways. Therefore, the compu-tation of irregular waves is discussed before the traditional motion description of a floating structure by using the Kirchhoff equations. With these basics, an approach to calculate the motion equations of single bodies within the earth-fixed reference frame is presented before the method of the inertia value transformation. To compare the body-fixed and earth-fixed calculation method, a free-floating crew transfer vessel in irregular waves is simulated and the results are discussed. Finally, the inertia value transformation will be proved by the energy conservation principle on the example of a pure rotating rigid body with none digital calculations.
Strategies to Increasing Locally-Generated Revenue from Hotel Taxes in Padang City

One of Padang City's sources of local revenue is hotel taxes. This study aims to analyze the potential of hotel tax revenue and the factors that influence hotel tax revenue, as well as to formulate strategies to increasing hotel tax revenue in the city of Padang. The primary data collection used a purposive sampling technique, while the Slovin method was used to determine the 60 hotel samples to reveal the average hotel rates. The analytical method used is the analysis of potential tax calculations, multiple linear regression, the SWOT and the QSPM matrix. The results show the potential for hotel taxes that can be optimized in the amount of Rp 17,6 billion to Rp 35,9 billion. Factors that affect hotel tax revenues in Padang City are occupancy rates and the number of hotel rooms. One of the strategies that can be implemented to increasing hotel taxes is to socialize information about the importance of tax obligations to regionally registered and unregistered hotel taxpayers and service providers.