Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Uncertainties Modeling for Production Planning Problems

The formulated mathematical model needs pre-determined and precise model parametersto find a solution. However, the model parameters such as coefficient value are usually not precisely known. Coefficient plays a pivotal role sincethe coefficientcouldprovide important information in relationship between algebraic and linguistic expression. Existing method which is commonly used to generate the precise parametric valuesis unable to handle the coexistence of fuzzy information. Moreover, selecting real numbers for coefficients in random process increases the complexity inprogramming process. Hence, we proposed a fuzzy random regression method in this paper to estimate the precise coefficient values which contains fuzzy random information. An illustrative numerical example is provided to deduce coefficient values from different data representation which included the fuzziness and randomness.The coefficients were treated based on the property of fuzzy random regression. The approach results show that we have the significant capabilities to estimate the coefficient value and improve the model which retain the simultaneous uncertainties and set up in production planning problem.
ELM-Induced Energy and Momentum Transport in ASDEX Upgrade

Heat and momentum transport play a key role in achieving high confinement in fusion plasmas. Recent advances in the diagnostic capabilities at AUG now allow us to measure the edge profiles on a sub-ms to ms time-scale and with a spatial resolution of less than 5 mm, making it ideal to study the profile recovery after an ELM crash. Here, we present the dynamic behaviour of the energy and momentum transport during edge localized mode cycles at the plasma edge of AUG by combining a comprehensive set of pedestal measurements with interpretive and predictive modelling. Themain ion temperature and toroidal rotation profiles weremeasured in helium plasmas with unprecedented temporal resolution of 250 mus. A local increase of Ti close to the separatrix is observed at the ELM onset, thus reducing the gradient in the pedestal, similar to the behaviour in D plasmas [1]. Shortly after the initial separatrix increase, the whole profile drops and then the pedestal starts to build up again. The pre-ELM profile is fully recovered 3-4 ms after the ELM crash. Transport analysis of the ion energy reveals that the ion heat transport is at the neoclassical level before the ELM crash in the region where the edge ion temperature gradient is maximal. Further inwards, the ion heat transport is about a factor of 4-5 above the neoclassical level. The dynamics of the edge ion heat transport during the pedestal build-up after the crash is also consistent with neoclassical theory [2]. Helium plasmas provide the unique opportunity to measure both main ion and impurity flows simultaneously. Compared to the impurity (here nitrogen) toroidal rotation, which exhibits a local minimum at the plasma edge during the inter-ELM phase [3], the edge main ion toroidal rotation has a much less pronounced dip and is rather flat. During the ELM themain ion toroidal rotation in the pedestal drops by about 5-10 km/s. This is in contrast to the behaviour of the impurity toroidal rotation, which shows a flattening of the toroidal dip feature [1]. TRANSP simulations and predictive modelling with ASTRA solving the toroidal momentum balance including diffusion, pinch and external sources are used to quantify how much momentum is transported during the ELM and will be presented. [1] M. Cavedon et al PPCF 59 105007 (2017) [2] E. Viezzer et al NF 58 026031 (2018) [3] T. Pütterich et al PRL 102 (2009)
Large-Scale Nonparametric and Semiparametric Inference for Large, Complex, and Noisy Datasets

Massive Data bring new opportunities and challenges to data scientists and statisticians. On one hand, Massive Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the size and dimensionality of Massive Data introduce unique statistical challenges and consequences for model misspecification. Some important factors are as follows. Complexity: Since Massive Data are often aggregated from multiple sources, they often exhibit heavy-tailedness behavior with nontrivial tail dependence. Noise: Massive Data usually contain various types of measurement error, outliers, and missing values. Dependence: In many data types, such as financial time series, functional magnetic resonance image (fMRI), and time course microarray data, the samples are dependent with relatively weak signals. These challenges are difficult to address and require new computational and statistical tools. More specifically, to handle these challenges, it is necessary to develop statistical methods ii ABSTRACT that are robust to data complexity, noise, and dependence. Our work aims to make headway in resolving these issues. Notably, we give a unified framework for analyzing high dimensional, complex, noisy datasets having temporal/spatial dependence. The proposed methods enjoy good theoretical properties. Their empirical usefulness is also verified in large-scale neuroimage and financial data analysis.that are robust to data complexity, noise, and dependence. Our work aims to make headway in resolving these issues. Notably, we give a unified framework for analyzing high dimensional, complex, noisy datasets having temporal/spatial dependence. The proposed methods enjoy good theoretical properties. Their empirical usefulness is also verified in large-scale neuroimage and financial data analysis. Advisors: Han Liu, PhD and Brian Caffo, PhD Committee: Terri Beaty, PhD (chair); Suchi Saria, PhD Alternates: Hongkai Ji, PhD; Fernando Pineda, PhD
LandInG 1.0: a toolbox to derive input datasets for terrestrial ecosystem modelling at variable resolutions from heterogeneous sources

Abstract. We present the Land Input Generator (LandInG) version 1.0, a new toolbox for generating input datasets for terrestrial ecosystem models (TEMs) from diverse and partially conflicting data sources. While LandInG 1.0 is applicable to process data for any TEM, it is developed specifically for the open-source dynamic global vegetation, hydrology, and crop growth model LPJmL (Lund–Potsdam–Jena with managed Land). The toolbox documents the sources and processing of data to model inputs and allows for easy changes to the spatial resolution. It is designed to make inconsistencies between different sources of data transparent so that users can make their own decisions on how to resolve these should they not be content with the default assumptions made here. As an example, we use the toolbox to create input datasets at 5 and 30 arcmin spatial resolution covering land, country, and region masks, soil, river networks, freshwater reservoirs, irrigation water distribution networks, crop-specific annual land use, fertilizer, and manure application. We focus on the toolbox describing the data processing rather than only publishing the datasets as users may want to make different choices for reconciling inconsistencies, aggregation, spatial extent, or similar. Also, new data sources or new versions of existing data become available continuously, and the toolbox approach allows for incorporating new data to stay up to date.
Prospects for dark matter detection in the extended halos of dwarf irregular galaxies with gamma rays

We investigate rotationally supported dwarf irregular (dIrr) galaxies as new targets for indirect dark matter (DM) searches with gamma-ray telescopes. Differently from pressure-supported objects, the kinematics of dIrr galaxies provides a good estimation of their DM halo density distribution. We then are able to calculate the astrophysical factors (or J-factors) of a sample of 36 dIrr galaxies. The range of the calculated values extends from $\sim10^{14}$ to $\sim \text{few}\hspace{0.1cm}\times$ $10^{17}\,\text{GeV}^2\text{cm}^{-5}$ and it turns out to be comparable with the J-factors of the well-known dwarf spheroidal galaxies, that is in the range from $\sim10^{12}$ to $\sim10^{20}\,\text{GeV}^2\text{cm}^{-5}$ including the uncertainties. However, the star-forming region in dIrrs constitutes an extra background, that is instead negligible in dwarf spheroidal galaxies. Therefore, we study the possibility of masking the unresolved star-forming regions of these galaxies with the intention of searching for the secondary gamma rays produced by DM annihilation events in their extended halos. We also individuate two galaxies IC10 and Wolf-Lundmark-Melotte (WLM) as the best candidates for indirect DM searches in our galaxy sample. Finally, we get the prospective constraints on the DM annihilation cross-section by means of the current and next-generation gamma-ray telescopes.
Research on the near field sound source localization method for small snapshots

The performance of standard MVDR algorithm would be decreased sharply when the sampled data is small. In this paper, vector MVDR algorithm and the diagonal loading are proposed to improve the robustness. Firstly, the sample date covariance matrix is whitened to obtain more uniform noise, and then use the standard deviation of covariance matrix as the loading amount to complete diagonal loading. Finally, the effectiveness and robustness of the proposed algorithm is verified by simulation. Simulation results reveal that it has higher positioning accuracy with small sample, with the increase of SNR and snapshots, the estimation’s RMSE of target location decreases continuously.
NON-LINEAR SECTIONAL ANALYSIS OF CONCRETE ENCASED STEEL STUB COLUMN SUBJECTED TO AXIAL LOAD

: This paper presents a numerical parametric study of Concrete Encased Steel Column (CESC) due to centrically and eccentrically axial load. To model the confining pressure of rebar and steel profile, modified Mander’s equations were used. The non-linear sectional analysis was performed using the fiber-based method. The CESC section was discretized using Netgen 2D meshing algorithm. The developed CESC model was validated using the available test results in the literature. After the model was validated, parametric studies were conducted to investigate the behaviors of CESC with different concrete compression strength, confinement bar diameter, and confining distance. The parametric studies found that the columns with higher concrete compression strength tend to have higher axial and flexural capacity but reduce the overall ductility. Increasing the confinement bar diameter slightly increases the axial and flexural capacity and ductility. On the other hand, utilizing tighter confinement space resulted in higher ductility but with insignificant increase in axial and flexural capacity.
Color UAV Image Edge Detection Based on Improved Fireworks Algorithm

Image edge detection plays a crucial role in image analysis and recognition. However, when dealing with color images captured by unmanned aerial vehicles (UAVs), there are certain limitations, such as large operations, multiple noise sources, easy distortion, and missing information in edge detection. To address these shortcomings, this study proposes a UAV color image edge detection method based on an enhanced fireworks algorithm. In this method, the color image pixels of the UAV are represented using quaternions. The explosion amplitude formula of the fireworks is divided into two categories based on the mean value of the number of fireworks explosions. For each category, an explosion formula is proposed, and the explosion mutation operator of the fireworks algorithm is improved accordingly. By applying the proposed algorithm, the preliminary edges of a UAV color image are obtained. Additionally, a novel approach for color image edge refinement is introduced. This approach involves classifying the edge points based on their degree of attachment, which leads to the formation of the edges in a UAV color image. Experimental results demonstrate that the algorithm proposed in this study offers several advantages, including fast calculation, strong denoising capability, and high-quality edge detection.
Generalized Hamming weights of a class of linear codes

In [22]}, Kelan Ding and Cunsheng Ding presented a class of linear codes and determined their weight distributions. In this paper, we focus on the generalized Hamming weights of these codes. In order to do so, we study the quadratic forms over subspaces of finite field and obtain some interesting results about subspaces and their dual spaces. On this basis, we solve all the generalized Hamming weights of these linear codes.
Variable selection wrapper in presence of correlated input variables for random forest models

In most data analytic applications in manufacturing, understanding the data‐driven models plays a crucial role in complementing the engineering knowledge about the production process. Identifying relevant input variables, rather than only predicting the response through some “black‐box” model, is of great interest in many applications. There is, therefore, a growing focus on describing the contributions of the input variables to the model in the form of “variable importance”, which is readily available in certain machine learning methods such as random forest (RF). Once a ranking based on the importance measure of the variables is established, the question of how many variables are truly relevant in predicting the output variable rises. In this study, we focus on the Boruta algorithm, which is a wrapper around the RF model. It is a variable selection tool that assesses the variable importance measure for the RF model. It has been previously shown in the literature that the correlation among the input variables, which is often a common occurrence in high dimensional data, distorts and overestimates the importance of variables. The Boruta algorithm is also affected by this resulting in a larger set of input variables deemed important. To overcome this issue, in this study, we propose an extension of the Boruta algorithm for the correlated data by exploiting the conditional importance measure. This extension greatly improves the Boruta algorithm in the case of high correlation among variables and provides a more precise ranking of the variables that significantly contribute to the response. We believe this approach can be used in many industrial applications by providing more transparency and understanding of the process.
Cone decompositions of non-simple polytopes

The interplay between simple convex lattice polytopes and projective toric varieties has been extensively studied. If a lattice polytope P ⊂ Rd is nonsimple (meaning that there is at least one vertex with more than d incident edges), we can still associate to P a projective toric variety, now with worse than orbifold singularities, by a generalization of the Delzant construction [6]. These singular toric varieties appear in many contexts. They can arise, for instance, as a result of singular symplectic reduction on toric manifolds (see for example [8] for the case of isolated singularities) and as Calabi Yau manifolds, with implications in mirror symmetry and string theory [11]. It is then interesting to extend the existing simple-polytope decompositions to non-simple polytopes. Some of these formulas [3, 13] were obtained by exploring the relation between simple lattice regular polytopes and smooth toric varieties, and then generalized to all simple lattice polytopes using combinatorial methods. In particular, the decompositions obtained in [3] were suggested by the work of Witten on localization on critical values of the square of the moment map. This localization technique was developed by Paradan [16] and later generalized by Harada-Karshon [10]. Perhaps the most well-known polytope decomposition is the BrianchonGram formula [7] which expresses the characteristic function 1P of a convex polytope P as the alternating sum of the characteristic functions of the
Drivers and risk of business process outsourcing in South African mobile telecommunication industry

Mobile telecommunications service providers outsource various activities like logistics, assembly operations, manufacturing and design. However, little research has been conducted in Southern Africa to establish the actual drivers triggering mobile telecommunication companies to implement business process outsourcing (BPO) and the risks associated with implementation. This research aims to redress the existing knowledge gap and the limited body of literature by conducting research to establish the drivers and risks of implementing BPO. A structured, closed-ended questionnaire collected raw data from 210 employees. Descriptive statistics were conducted to establish the drivers triggering mobile telecommunication companies to implement BPO. Overall, the results concluded that some of the key drivers of implementing BPO by the telecom operators are reduction in capital investment, thereby freeing up limited capital funds more available for core areas, and the company can have access to unique resources skills and talents. The results also indicated that the risks of BPO implementation include the potential loss of control over critical functions and the difficulty of ensuring service quality and consistency from the service provider. The results will help the corporate management to make decisions about outsourcing based on quantifiable results instead of managerial estimates. While the study confined itself to the mobile telecommunications companies, more research can be conducted in other South African industries, most notably banks and learning institutions that are also important BPO hubs worthy of further examination.
The Effect Of Environmental Performance On Firm Value Using Financial Performance As Mediator Variable

Purpose: This study aims to examine the effect of environmental performance on firm value directly or indirectly by employing financial performance as a mediator variable. Design/methodology/approach: This research employs quantitative research. The population of the research is all non-financial companies on the Indonesia Stock Exchange (IDX) in the 2014-2019 period with a sample of 282 firm-years. The employed data analysis technique is Warp PLS 6.0 carried out in the two testing stages; the testing of the direct and the indirect effects of environmental performance variable on firm value. Findings: Environmental performance has a significant positive effect on firm value; environmental performance has no effect on financial performance; financial performance has a significant positive effect on firm value; and financial performance is not proven as a variable that mediates the effect of environmental performance on firm value. Research limitations: The measurement of financial performance employs only one proxy; ROE. Measurement of financial performance may use various proxies to produce more accurate findings. Practical implications: This research provides an overview of the importance of implementing environmental performance for a company in order to increase firm value or company reputation among investors in particular and society in general. Originality/value: This research is a development of previous studies, which produced varying research results, with the addition of financial performance as a mediator variable. Paper type: This research is categorized as a research paper. Keywords: Environmental Performance, Financial Performance and Firm Value
APS : Low energy tertiary beam line design for the CERN neutrino platform project

In the framework of CERN neutrino platform project at the CERN SPS North Area, two new beam lines have been designed as extensions of the existing secondary beam lines, able to provide low energy particles in the momentum range of 0.4 to 12 GeV/c. The layout of these very low energy beam lines, their design parameters as well as outlook on their expected performance, are summarized in this paper.
A PTV method based on ultrasound imaging and feature tracking in a low-concentration sediment-laden flow

This study aims to provide a particle tracking velocimetry (PTV) method based on ultrasound imaging and feature-tracking in a low-concentration sediment-laden flow. A phased array probe is used to generate a 2D ultrasound image at different times. Then, the feature points are extracted to be tracked instead of the centroids of the particle image. In order to better identify the corresponding feature point, each feature is described by an oriented angle and its location. Then, a statistical interpolation procedure is used to yield the displacement vector on the desired grid point. Finally a correction procedure is adopted because the ultrasound image is sequentially acquired line by line through the field of view. A simple test experiment was carried out to evaluate the performance. The ultrasound PTV system was applied to a sediment-laden flow with a low concentration of 1‰, and the speed was up to 10 cm s−1. In comparison to optical particle image velocimetry (PIV), ultrasound imaging does not have a limitation in optical access. The feature-tracking method does not have a binarisation and segmentation procedure, which can result in overlapping particles or a serious loss of particle data. The feature-tracking algorithm improves the peak locking effect and measurement accuracy. Thus, the ultrasound PTV algorithm is a feasible alternative and is significantly more robust against gradients than the correlation-based PIV algorithms in a low-concentration sediment-laden fluid.
Physics with the CHIMERA detector at LNS CATANIA methodology-experimental results - Future development

he new Charged Heavy Ion Mass and Energy Resolving Array CHIMERA was designed to significantly contribute to Multifragmentation studies in Heavy Ion collisions at Fermi energy (20A.MeV < E < 100A.MeV). The multi detector is presently being set up at LNS in Catania. On April 2000 the first physical campaign, the REVERSE experiment, was started and data analysis is going on. Tile nuclear systems Sn-124 + Ni-64, Sn-124 + Al-27 and Sn-112 + Ni-58 at 35 A.MeV were investigated in reverse kinematics. Preliminary results have proved experimental capability of the apparatus for the full isotopic identification of both light charged particles (1
Automated Synthesis of Safe Timing Behaviors for Requirements Models Using CCSL

As a promising requirement-level specification language for timing behavior modeling, the clock constraint specification language (CCSL) has become popular in the model-driven design community for safety-critical embedded systems. However, due to the skyrocketing design complexity, in practice, it is hard for requirement engineers to accurately construct requirement models with expected timing behaviors using CCSL, especially, for safe timing behaviors. Although more and more CCSL synthesis approaches are designed to facilitate the generation of CCSL specifications, most of them cannot be used directly for the synthesis of requirements models. This is because existing CCSL synthesis methods: 1) focus on filling the holes of CCSL constraints rather than completing requirements models and 2) rely heavily on limited observations of system behaviors, while the (temporal) safety properties of target systems are neglected. To address these issues, this article proposes a novel method that enables the automated synthesis of safe timing behaviors for requirements models. By specifying the safety timing properties of target systems using safely-LTL, our approach adopts CCSL as an intermediate representation of requirement synthesis, where incomplete requirements models coupled with safely-LTL-based properties are encoded into CCSL constraints with holes. Guided by the samples (expected behaviors) provided by requirement engineers, our approach can automatically figure out the complete version of incomplete requirements models. Comprehensive experimental results on two complex case studies demonstrate that our approach can not only quickly and efficiently synthesize requirement models but also guarantee that the synthesized models satisfy specified safety properties in Safely-LTL form.