Almost sure behavior of the zeros of iterated derivatives of random polynomials

Let $Z_1,\, Z_2,\dots$ be independent and identically distributed complex random variables with common distribution $\mu$ and set $$P_n(z) := (z - Z_1)\cdots (z - Z_n)\,.$$ Recently, Angst, Malicet and Poly proved that the critical points of $P_n$ converge in an almost-sure sense to the measure $\mu$ as $n$ tends to infinity, thereby confirming a conjecture of Cheung-Ng-Yam and Kabluchko. In this short note, we prove for any fixed $k\in \mathbb{N}$, the empirical measure of zeros of the $k$th derivative of $P_n$ converges to $\mu$ in the almost sure sense, as conjectured by Angst-Malicet-Poly.Comment: 8 page

A New Linear Printed Vivaldi Antenna Array with Low Side Lobe Level and High Gain for the Band 3.5 GHz

This paper proposes a new design of low sidelobe level (SLL) and high gain linear printed Vivaldi antenna array. The array composes of two parts, which are a linear Vivaldi antenna array and a back reflector. The array consists of 10 single Vivaldi antennas and a series-fed network, those are based on Roger RO4003C substrate (ε = 3.55) with the dimension of 140 x 450 x 1.524 mm3. A new Bat algorithm with the amplitude-only control technique has been applied to optimize the output coefficients of the series-fed network for gaining a low SLL. The simulation results indicate that the proposed antenna provides a low SLL of -29.2 dB in E-plane with a high gain of 16.5 dBi at the frequency of 3500 MHz. A prototype of the proposed antenna array has been fabricated. The measured data has a good agreement with the simulated data

Stabilization for equal-order polygonal finite element method for high fluid velocity and pressure gradient

This paper presents an adapted stabilisation method for the equal-order mixed scheme of finite elements on convex polygonal meshes to analyse the high velocity and pressure gradient of incompressible fluid flows that are governed by Stokes equations system. This technique is constructed by a local pressure projection which is extremely simple, yet effective, to eliminate the poor or even non-convergence as well as the instability of equal-order mixed polygonal technique. In this research, some numerical examples of incompressible Stokes fluid flow that is coded and programmed by MATLAB will be presented to examine the effectiveness of the proposed stabilised method

Towards enhanced surface roughness modeling in machining: an analysis of data transformation techniques

Data transformation methods are utilized to convert datasets into non-integer formats, potentially altering their distribution patterns. This implies that the variance and standard deviation of the dataset may be altered after the dataset undergoes data transformation operations. Improving model accuracy is a primary application of these methods. This study compares the efficacy of three data transformation techniques: square root transformation, logarithmic transformation, and inverse transformation. The comparison is conducted within the context of developing a surface roughness model for a turning process. Eighteen experiments are performed using the Box-Behnken method, with surface roughness chosen as the response variable. The surface roughness dataset undergoes transformation using the mentioned methods. Four surface roughness regression models are then built: one without transformation, one with square root transformation, one with logarithmic transformation, and one with inverse transformation. Evaluation metrics include coefficient of determination (R-Sq), adjusted coefficient of determination (R-Sq(adj)), Mean Absolute Error (%MAE), and Mean Squared Error (%MSE). Results indicate logarithmic transformation as the most effective, followed by square root transformation, in enhancing model accuracy. The surface roughness model utilizing data transformation exhibits high R-Sq and R-Sq(adj) values, at 0.8792 and 0.7434 respectively. On the other hand, this model has %MAE and %MSE values of only 10.33 and 2.05 respectively. Conversely, inverse transformation exhibits the least effectiveness among the three method

Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data

Accurate forecasting and analysis of emerging pandemics play a crucial role in effective public health management and decision-making. Traditional approaches primarily rely on epidemiological data, overlooking other valuable sources of information that could act as sensors or indicators of pandemic patterns. In this paper, we propose a novel framework called MGL4MEP that integrates temporal graph neural networks and multi-modal data for learning and forecasting. We incorporate big data sources, including social media content, by utilizing specific pre-trained language models and discovering the underlying graph structure among users. This integration provides rich indicators of pandemic dynamics through learning with temporal graph neural networks. Extensive experiments demonstrate the effectiveness of our framework in pandemic forecasting and analysis, outperforming baseline methods across different areas, pandemic situations, and prediction horizons. The fusion of temporal graph learning and multi-modal data enables a comprehensive understanding of the pandemic landscape with less time lag, cheap cost, and more potential information indicators

Corrosion protection of carbon steel by an epoxy resin containing organically modified clay

This study focusses on the use of montmorillonite clay (MMT) treated with an organic compound (aminotrimethylphosphonic acid (ATMP)) and dispersed in an epoxy resin to improve corrosion protection of carbon steel. X-ray diffraction was performed to verify that the individual silicate layers were separated and dispersed in the epoxy resin. Corrosion resistance of the coated steel was evaluated by electrochemical impedance spectroscopy (EIS) and local electrochemical impedance spectroscopy (LEIS). Three systems were tested: the epoxy clear-coat, the epoxy resin containing 2 wt.% clay and the epoxy resin containing 2 wt.% clay modified byATMP (ATMP-modified clay). From conventional EIS, it was shown that the incorporation of clay or ATMP-modified clay in the epoxy matrix significantly improved the barrier properties of the coating. The corrosion resistance of the carbon steel coated by the epoxy resin containing ATMP-modified clay was higher than that obtained for the system containing non-treated clay. Local electrochemical measurements performed on scratched samples revealed the inhibitive role of ATMP at the carbon steel/coating interface

A Target Threat Assessment Method for Application in Air Defense Command and Control Systems

Introduction. This paper presents a solution for threat assessment of air targets using the fuzzy logic inference method. The approach is based on the Sugeno fuzzy model, which has multiple inputs representing target trajectory parameters and a single output representing the target threat value. A set of IF–THEN fuzzy inference rules, utilizing the AND operator, is developed to assess the input information.Aim. To develop and test an algorithm model to calculate the threat value of an air target for use in real-time automated command and control systems.Materials and methods. An algorithm model was developed using a fuzzy model to calculate the threat value of a target. The model is presented in the form of a flowchart supported by a detailed stepwise implementation process. The accuracy of the proposed algorithm was evaluated using the available toolkit in MATLAB. Additionally, a BATE software testbed was developed to assess the applicability of the algorithm model in a real-time automated command and control system.Results. The efficiency of the proposed fuzzy model was evaluated by its simulation and testing using MATLAB tools on a set of 10 target trajectories with different parameters. Additionally, the BATE software was utilized to test the model under various air defense scenarios. The proposed fuzzy model was found to be capable of efficiently computing the threat value of each target with respect to the protected object.Conclusion. The proposed fuzzy model can be applied when developing tactical supporting software modules for real-time air defense command and control systems.Introduction. This paper presents a solution for threat assessment of air targets using the fuzzy logic inference method. The approach is based on the Sugeno fuzzy model, which has multiple inputs representing target trajectory parameters and a single output representing the target threat value. A set of IF–THEN fuzzy inference rules, utilizing the AND operator, is developed to assess the input information.Aim. To develop and test an algorithm model to calculate the threat value of an air target for use in real-time automated command and control systems.Materials and methods. An algorithm model was developed using a fuzzy model to calculate the threat value of a target. The model is presented in the form of a flowchart supported by a detailed stepwise implementation process. The accuracy of the proposed algorithm was evaluated using the available toolkit in MATLAB. Additionally, a BATE software testbed was developed to assess the applicability of the algorithm model in a real-time automated command and control system.Results. The efficiency of the proposed fuzzy model was evaluated by its simulation and testing using MATLAB tools on a set of 10 target trajectories with different parameters. Additionally, the BATE software was utilized to test the model under various air defense scenarios. The proposed fuzzy model was found to be capable of efficiently computing the threat value of each target with respect to the protected object.Conclusion. The proposed fuzzy model can be applied when developing tactical supporting software modules for real-time air defense command and control systems

zk-SNARKs from Codes with Rank Metrics

Succinct non-interactive zero-knowledge arguments of knowledge (zk-SNARKs) are a type of non-interactive proof system enabling efficient privacy-preserving proofs of membership for NP languages. A great deal of works has studied candidate constructions that are secure against quantum attackers, which are based on either lattice assumptions, or post-quantum collision-resistant hash functions. In this paper, we propose a code-based zk-SNARK scheme, whose security is based on the rank support learning (RSL) problem, a variant of the random linear code decoding problem in the rank metric. Our construction follows the general framework of Gennaro et al. (CCS\u2718), which is based on square span programs (SSPs). Due to the fundamental differences between the hardness assumptions, our proof of security cannot apply the techniques from the lattice-based constructions, and indeed, it distinguishes itself by the use of techniques from coding theory. We also provide the scheme with a set of concrete parameters

Treatment of landfill leachate through struvite precipitation and nitrogen removal bacteria and poly-phosphate bacteria (in-pots experiment)

Abstract— Landfill leacheate is a type of wastewater which contains large amounts of nitrogen and phosphorus, therefore it needed to be treated before releasing to directly to the environment. The combination between struvite precipitation and nitrogen removal and poly-P bacteria into wastewater for landfill leachate treatment has been found to be a cost-effective practive, a viable technology in terms of environmental protection and sustainability, especially in the developing-countries. For optimum struvite crystallization from landfill leachate, the Mg:PO4 molar ratio as (1.2:1) was used, the pH of reaction was adjusted to 9 and the sample was stirred continously during 40 minutes. The supernatant sample was then added 1% nitrogen removal bacteria (Pseudomonas stutzeri D3b strain) and 1% poly-P bacteria (Kurthia sp. TGT1013L strain), 5 g glucose/L and aeration 12/24h during 3 days, ammonium concentration reduced significantly from 1076 mg/L to 1.5 mg/L and orthophosphate concentration decreased noticeably from 24.91 mg/L to 7.6 mg/L