Miracle-dz/UniFAR-FTUs huggingface dataset:
Dust Survival in Galactic Winds

We present a suite of high-resolution numerical simulations to study the evolution and survival of dust in hot galactic winds. We implement a novel dust framework in the Cholla hydrodynamics code and use wind tunnel simulations of cool, dusty clouds to understand how thermal sputtering affects the dust content of galactic winds. Our simulations illustrate how various regimes of cloud evolution impact dust survival, dependent on cloud size, wind properties, and dust grain size. We find that significant amounts of dust can survive in winds in all scenarios, even without shielding from the cool phase of outflows. We present an analytic framework that explains this result, along with an analysis of the impact of cloud evolution on the total fraction of dust survival. Using these results, we estimate that 60 percent of dust that enters a starburst-driven wind could survive to populate the halo, based on a simulated distribution of cloud properties. We also investigate how these conclusions depend on grain size, exploring grains from 0.1 micron to 10 Angstrom. Under most circumstances, grains smaller than 0.01 micron cannot withstand hot-phase exposure, suggesting that the small grains observed in the CGM are either formed in situ due to shattering of larger grains, or must be carried there in cool phase outflows. Finally, we show that the dust-to-gas ratio of clouds declines as a function of distance from the galaxy due to cloud-wind mixing and condensation. These results provide an explanation for the vast amounts of dust observed in the CGMs of galaxies and beyond.
Largest common subgraph of two forests

A common subgraph of two graphs $G_1$ and $G_2$ is a graph that is isomorphic to subgraphs of $G_1$ and $G_2$. In the largest common subgraph problem the task is to determine a common subgraph for two given graphs $G_1$ and $G_2$ that is of maximum possible size ${\rm lcs}(G_1,G_2)$. This natural problem generalizes the well-studied graph isomorphism problem, has many applications, and remains NP-hard even restricted to unions of paths. We present a simple $4$-approximation algorithm for forests, and, for every fixed $\epsilon\in (0,1)$, we show that, for two given forests $F_1$ and $F_2$ of order at most $n$, one can determine in polynomial time a common subgraph $F$ of $F_1$ and $F_2$ with at least ${\rm lcs}(F_1,F_2)-\epsilon n$ edges. Restricted to instances with ${\rm lcs}(F_1,F_2)\geq cn$ for some fixed positive $c$, this yields a polynomial time approximation scheme. Our approach relies on the approximation of the given forests by structurally simpler forests that are composed of copies of only $O(\log (n))$ different starlike rooted trees and iterative quantizations of the options for the solutions.
Energy and Time Histograms: Fluxes determination in calorimeters

In high-granularity calorimetry, as proposed for detectors at future Higgs factories, the requirements on electronics can have a strong impact on the design of the detector, especially via the cooling and acquisition systems. This project aims to establish the typical fluxes in the calorimeters: deposited energy, number of cells above the electronics threshold, additional heat, and associated data, etc. Here, a software package is presented which outputs histograms for different selections of the calorimeter components from fully simulated physics and background events. The ILD calorimeter system is taken as a specific example, upon which different histograms are obtained for representative parts of the calorimeter and for various machine configurations. Examples of histograms are shown, along with all details of the data used and the simulation.
A two-line representation of stationary measure for open TASEP

We show that the stationary measure for the totally asymmetric simple exclusion process on a segment with open boundaries is given by a marginal of a two-line measure with a simple and explicit description. We use this representation to analyze asymptotic fluctuations of the height function near the triple point for a larger set of parameters than was previously studied. As a second application, we determine a single expression for the rate function in the large deviation principle for the height function in the fan and in the shock region. We then discuss how this expression relates to the expressions available in the literature.
Exchange-correlation energy from Green's functions

DFT calculations yield useful ground-state energies and densities, while Green's function techniques (such as $GW$) are mostly used to produce spectral functions. From the Galitskii-Migdal formula, we extract the exchange-correlation of DFT directly from a Green's function. This spectral representation provides an alternative to the fluctuation-dissipation theorem of DFT, identifying distinct single-particle and many-particle contributions. Results are illustrated on the uniform electron gas and the two-site Hubbard model.
D4C glove-train: solving the RPM and Bongard-logo problem by distributing and Circumscribing concepts

This paper achieves noteworthy progress in the realm of abstract reasoning, particularly in addressing Raven's Progressive Matrices (RPM) and Bongard-Logo challenges. Initially, we introduce Lico-Net, a novel baseline model that resolves RPM problems with remarkable accuracy. Leveraging this foundation, we advance with the D3C approach, which advocates representing the underlying concepts in abstract reasoning problems through distributions. This perspective enhances the performance of both Lico-Net and a baseline model excelling in Bongard-Logo tasks. To bolster the computational efficiency of D3C, we present the D3C-cos variant, offering a streamlined yet precise solution. Furthermore, we propose the D2C method, redefining conceptual boundaries within these domains and bridging the divide between high-level abstractions and their lower-dimensional counterparts. Finally, we extend our methodology to D4C, employing adversarial techniques to refine conceptual boundaries further and demonstrate substantial improvements in both RPM and Bongard-Logo challenges. Overall, our contributions present a fresh outlook and practical advancements in the field of abstract reasoning.
Revisiting phonon thermal transport in two-dimensional gallium nitride: higher-order phonon-phonon and phonon-electron scattering

Two-dimensional gallium nitride (2D-GaN) has great potential in power electronics and optoelectronics. Heat dissipation is a critical issue for these applications of 2D-GaN. Previous studies showed that higher-order phonon-phonon scattering has extremely strong effects on the lattice thermal conductivity of 2D-GaN, which exhibits noticeable discrepancies with lattice thermal conductivity calculated from molecular dynamics. In this work, it is found that the fourth-order interatomic force constants (4th-IFCs) of 2D-GaN are quite sensitive to atomic displacement in the finite different method. The effects of the four-phonon scattering can be severely overestimated with non-convergent 4th-IFCs. The lattice thermal conductivity from three-phonon scattering is reduced by 65.6% due to four-phonon scattering. The reflection symmetry allows significantly more four-phonon processes than three-phonon processes. It was previously thought the electron-phonon interactions have significant effects on the lattice thermal conductivity of two-dimensional materials. However, the effects of phonon-electron interactions on the lattice thermal conductivity of both n-type and p-type 2D-GaN at high charge carrier concentrations can be neglected due to the few phonon-electron scattering channels and the relatively strong four-phonon scattering.
Cosmological forecasts from the combination of Stage-IV photometric galaxy surveys and the magnification from forthcoming GW observatories

In this work we have investigated the synergy between Stage-IV galaxy surveys and future GW observatories for constraining the underlying cosmological model of the Universe, focussing on photometric galaxy clustering, cosmic shear and GW magnification as cosmological probes. We have implemented a Fisher matrix approach for the evaluation of the full $6\times2$pt statistics composed by the angular power spectra of the single probes together with their combination. For our analysis, we have in particular considered dynamical dark energy and massive neutrino scenarios. We have found that the improvement to galaxy survey performance is below 1\%, in the case $\ell^{\rm GW}_{\rm max}=100$ and a luminosity distance error of $\sigma_{d_L}/d_L=10\%$. However, when extending the analysis to $\ell^{\rm GW}_{\rm max}=1000$, we find that the GW magnification improves the galaxy survey performance on all the cosmological parameters, reducing their errors by $3\%$-$5\%$, when $\sigma_{d_L}/d_L=10\%$, and by $10\%$-$18\%$ when $\sigma_{d_L}/d_L=1\%$, especially for $M_\nu$, $w_0$ and $w_a$. However, here our analysis is unavoidably optimistic: a much more detailed and realistic approach will be needed, especially by including systematic effects. But we can conclude that, in the case of future gravitational wave observatories the inclusion of the gravitational wave magnification can improve Stage-IV galaxy surveys performance on constraining the underlying cosmological model of the Universe.
The rise and fall (and rise) of Winston Island

On 20th February 2016, the most energetic cyclone in recorded history, the category 5 Tropical Cyclone (TC) Winston, tracked through central Fiji causing widespread damage and loss of life. At its peak, ten-minute sustained wind speeds of 230 kph were recorded, with wind gusts of over 300 kph. The eye of the cyclone passed within 25 km of Qamea Island in northeastern Fiji, with the strongest winds occurring from the northwest. After the cyclone had passed, a cay had formed on the southern fringing reef system ~1 km offshore of Qamea Island. The initial survey of the new cay (given the name Winston Island), found a mound of broken coral, sand and other debris some 1.8 m high by 105 m long and 15 m wide. Six weeks later the cay had reduced to only a fraction of the initial size (<30 m by 0.6 m high). It was expected that the cay was fated to disappear completely and the barrier reef flat to return to the pre-cyclone condition. However, 4 months later Winston Island had increased in size to be 126 m long and 2.0 m high, and has continued to slowly grow in size. While the formative mechanism was confidently determined (i.e. 2.5-3.0 m waves generated by TC Winston), and the initial chronic reduction in material was also intuitive, the mechanism which led to the subsequent rebuilding of Winston Island warranted investigation. This paper investigates the formative mechanisms that resulted in Winston Island and documents the subsequent changes to its morphology over the following year.
Electromagnetic inverse wave scattering in anisotropic media via reduced order modeling

The inverse wave scattering problem seeks to estimate a heterogeneous, inaccessible medium, modeled by unknown variable coefficients in wave equations, from transient recordings of waves generated by probing signals. It is a widely studied inverse problem with important applications, that is typically formulated as a nonlinear least squares data fit optimization. For typical measurement setups and band-limited probing signals, the least squares objective function has spurious local minima far and near the true solution, so Newton-type optimization methods fail. We introduce a different approach, for electromagnetic inverse wave scattering in lossless, anisotropic media. Our reduced order model (ROM) is an algebraic, discrete time dynamical system derived from Maxwell's equations with four important properties: (1) It is data driven, without knowledge of the medium. (2) The data to ROM mapping is nonlinear and yet the ROM can be obtained in a non-iterative fashion. (3) It has a special algebraic structure that captures the causal Wave propagation. (4) The ROM interpolates the data on a uniform time grid. We show how to obtain from the ROM an estimate of the wave field at inaccessible points inside the unknown medium. The use of this wave is twofold: First, it defines a computationally inexpensive imaging function designed to estimate the support of reflective structures in the medium, modeled by jump discontinuities of the matrix valued dielectric permittivity. Second, it gives an objective function for quantitative estimation of the dielectric permittivity, that has better behavior than the least squares data fitting objective function. The methodology introduced in this paper applies to Maxwell's equations in three dimensions. To avoid high computational costs, we limit the study to a cylindrical domain filled with an orthotropic medium, so the problem becomes two dimensional.
Application of Deep Learning Reduced-Order Modeling for Single-Phase Flow in Faulted Porous Media

We apply reduced-order modeling (ROM) techniques to single-phase flow in faulted porous media, accounting for changing rock properties and fault geometry variations using a radial basis function mesh deformation method. This approach benefits from a mixed-dimensional framework that effectively manages the resulting non-conforming mesh. To streamline complex and repetitive calculations such as sensitivity analysis and solution of inverse problems, we utilize the Deep Learning Reduced Order Model (DL-ROM). This non-intrusive neural network-based technique is evaluated against the traditional Proper Orthogonal Decomposition (POD) method across various scenarios, demonstrating DL-ROM's capacity to expedite complex analyses with promising accuracy and efficiency.
Construction of Curriculum System for Intelligent Manufacturing Engineering Speciality

The rapid development of the manufacturing industry has led to a significant increase in the demand for talent in the field of modern mechanical manufacturing, particularly in the area of intelligent manufacturing. Intelligent manufacturing represents the profound integration of next-generation information and communication technologies with advanced manufacturing techniques. This paper aims to establish a curriculum system suitable for cultivate intelligent manufacturing professionals who meet the requirements of the engineering field. By combining the needs of industrial development and talent cultivation, the construction of the curriculum system for intelligent manufacturing engineering is explored through the application of engineering professional certification methods and principles. This exploration encompasses aspects such as the theoretical curriculum, practical curriculum and assessment systems.
Fuel Optimized Predictive Following in Low Speed Conditions

The situation when driving in dense traffic and at low speeds is called Stop and Go. A controller for automatic following of the car in front could under these conditions reduce the driver’s workload and keep a safety distance to the preceding vehicle through different choices of gear and engine torque. The aim of this thesis is to develop such a controller, with an additional focus on lowering the fuel consumption. With help of GPS, 3D-maps and sensors information about the slope of the road and the preceding vehicle can be obtained. Using this information the controller is able to predict future possible control actions and an optimization algorithm can then find the best inputs with respect to some criteria. The control method used is Model Predictive Control (MPC) and as the name indicate a model of the control object is required for the prediction. To find the optimal sequence of inputs, the optimization method Dynamic Programming choose the one which lead to the lowest fuel consumption and satisfactory following. Simulations have been made using a reference trajectory which was measured in a real traffic jam. The simulations show that it is possible to follow the preceding vehicle in a good way and at the same time reduce the fuel consumption with approximately 3 %.
and Obesity: Targets and Therapy

Background: Advanced glycation end products, selectins, and adiponectin play important roles in the development of atherosclerosis in individuals with diabetes. Sitagliptin has been shown to reduce the concentration of glycated hemoglobin in diabetic patients. However, its effects on soluble receptor for advanced glycation end products (sRAGEs), selectins, and adiponectin in these patients are poorly understood. This study was conducted to assess the effects of sitagliptin on the circulating levels of sRAGEs, monocyte chemoattractant protein-1 (MCP-1), selectins, and adiponectin in patients with type 2 diabetes. Methods: Diabetic patients eligible for sitagliptin monotherapy or combination therapy (eg, sitagliptin plus a sulfonylurea) were administered sitagliptin (50 mg/day) for 6 months. Levels of soluble P-selectin (sP-selectin), soluble E-selectin (sE-selectin), soluble vascular cell adhesion molecule-1 (sVCAM-1), MCP-1, sRAGEs, and adiponectin were measured by ELISA at baseline and after 3 and 6 months of treatment. Results: At baseline, the levels of MCP-1, sP-selectin, sE-selectin, and sVCAM-1 were higher and the level of adiponectin was lower in diabetic patients than in nondiabetic patients. Sitagliptin therapy for 3 and 6 months significantly reduced plasma levels of sP-selectin, sE-selectin, sVCAM-1, and MCP-1 relative to baseline, while significantly increasing adiponectin levels. sRAGEs did not exhibit a statistical significance, although there was an increasing tendency. Furthermore, the reductions in sP-selectin, sE-selectin, sVCAM-1, and MCP-1 during sitagliptin therapy were significantly greater in responders, defined as patients with a significant increase in adiponectin levels, than in nonresponders. In contrast, responders showed a significant increase in the plasma concentration of sRAGEs. Conclusion: Sitagliptin shows an adiponectin-dependent anti-atherothrombotic effect, which may be beneficial for primary prevention of atherothrombosis, in patients with type 2 diabetes.
Analysing Employee Training Policies of the High Revenue Companies: Examples From Turkey

Purpose: The present study explores the primary characteristics of employee training practices by way of analysing the employee training policies of the top two companies with the highest sales revenue in Turkey. The primary characteristics of employee training practices are examined within the scope of the corporate employee training activities and the practices to cope with the challenges in the organization of employee training. Methods: Employing qualitative approach, document analysis was conducted within the context of two cases. The two companies with the highest sales revenue in Turkey were selected as cases in the study. The companies were determined through criterion sampling in line with the financial performance based on net sales revenue, stability of financial performance and potential for corporate training policy development criteria. The six years of sustainability and activity reports of the companies were analysed through thematic analysis. The documents were analysed through thematic analysis using open coding. Employee training policies and practices were presented under the sub-themes of personnel training types, subjects of personnel training, training infrastructure, factors considered in the design of training processes and supportive practices for training processes. Findings: Employee training of the companies mostly consists of formal and non-formal training activities designed according to the needs of employees. These training activities
Ceramic thermal barrier coating materials

This chapter gives a summary of the use of ceramic materials such as the standard yttria stabilized zirconia (YSZ) and some further candidate materials, Al 2 O 3 , TiO 2 , mullite, CaO/MgO + ZrO 2 , CeO 2 + YSZ, Y 2 O 3 + HfO 2 , zircon, La 2 Zr 2 O 7 , La 2 Ce 2 O 7 , LaMgAl 11 O 19 , SrZrO 3 , etc. as TBC materials. The thermophysical properties and corrosion-resistance characteristic of the TBC candidate materials and their thermal cycling behavior are discussed. The primary future development directions for ceramic TBC materials are also given in this chapter.
Design and Analysis of Dual Layer Probe Feed Coaxial Patch Antenna Array for Sub-6GHz Applications

The purpose of this research is to design and evaluate the performance of a dual-layer probe feed 16-element low-profile rectangular coaxial feed patch antenna (RCPA) array on two low-cost FR-4 substrate layers with a thickness of 1.6mm to achieve high gain and directivity for sub-6 GHz applications. At 3.5GHz resonant frequency, a single RCPA and $4 \times 4$ array are designed, and a T-junction equal power splitting power divider is used to feed an array network. The overall size of the proposed $4 \times 4$ array is $209 \times 180 \times 1.6$ mm$^{3}$ for both layers and it achieves more than double the gain and directivity, outperforming the single RCPA. The performance of the array in terms of reflection coefficient, gain, directivity, VSWR, impedance matching, bandwidth, and radiation patterns is analyzed through simulation and found that it gives good agreement with significant improvement in gain, directivity, and bandwidth demonstrated by the designed 16-element array highlights its suitability for wireless applications below 6 GHz.
Domain Based Network Intrusion Detection System For Iot

Ensuring security of computer networks is crucial in today’s interconnected world. By identifying and reducing security risks, intrusion detection systems (IDS) play a crucial part in protecting network infrastructures. While machine learning (ML) approaches have improved IDS accuracy, there is a lack of generalized frameworks that can be implemented across different datasets, hindering practical deployment. Additionally, the disconnect between ML models and domain knowledge has reduced the explainability of IDS systems. To address these gaps, this research work designed an automated IDS framework that enhances scalability and interpretability along with real time inference. The framework focuses on automation of preprocessing, feature selection and the domain feature extraction using different algorithms. Additionally, edge deployment in improving intrusion detection for IoT devices. The results are evaluated using five Machine Learning models and contrasted using an automated and a manual feature selection procedure for each of the 10 attacks. The system concentrates on three categories of assaults for deployment purposes: Distributed Denial of Service (DDoS), Denial of Service (DoS), and normal traffic. To classify these assaults, a logistic regression model is trained and the prediction is displayed using Streamlit UI.
A Lightweight CNN Model for Breast Cancer Classification

Breast cancer is a dominant cancer worldwide and is increasing in developing countries where the majority of cases are diagnosed in later stage. Early detection of cancer helps to control it in the early stage itself and decrease the death rate. There were over 2 million new cases in 2021, making it a significant health problem in present days. Malignant is the cancer cells that invade and kill nearby tissue and spread into different parts of the body. Unlike Malignant, Benign does not invade to other parts of the body. The key challenge is to classify the tumor as malignant or benign. To cater to this problem, novel Convolutional Neural Network (CNN) architecture is introduced to identify and classify the tumor as either malignant or benign. The proposed CNN model is experimented on the Wisconsin Breast Cancer Dataset (WBCD) and it is able to achieve 99% of accuracy.