Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the SIF for the Multiple Crack Problems

In elastic fracture mechanics the evaluation of the stress intensity factor (SIF) for multiple cracks problems is an important issue. In the paper the scaled boundary finite element method (SBFEM) is used to solve the SIF of mode I of multiple crack problems. The solving domain is partitioned into several sub-domains according to the number of cracks. Every sub-domain has its own scaling center. The characteristics of the SBFEM are preserved in every sub-domain. Numerical examples show that the SBFEM is effective with high accuracy in evaluating the multiple cracks fracture problems. It can be applied to treat the anisotropic materials conveniently. The stress intensity factors of unequal double-edged cracks in orthotropic material are provided.
Development of Acoustical Prediction Model for Asphalt Pavements Using Grey System Approach

ABSTRACT Traffic noise resulted from various transportation vehicles is normally the most significant sources of noise in the environment of urban areas. Previous research results have shown that traffic noise is mainly generated from tire/road noise for high velocity vehicles. For traffic noise assessment, the controlling factors for the acoustical properties and the tire/road noise on asphalt pavements are not known very well, such as vehicle categories, tire types and material characteristics of pavement on field. In addition, many of the existing prediction models for acoustical properties and traffic noise still have application limitations and accuracy problems. Acoustical properties and tire/road noise are measured from laboratory and field tests conducted in this study. The grey model with Fourier residual correction is introduced to predict normal incidence sound absorption coefficient and tire/road noise. The applicability of developed prediction models is further evaluated by statistical analysis and Absolute Mean Error (AME). The evaluation results confirming the models are useful and accurate.
Business Plan Development Activities as a Pedagogical Tool in Entrepreneurship Education

Entrepreneurship education is increasingly being offered to engineering students as a way to broaden their skills and differentiate themselves in the job market. Entrepreneurship courses and programs typically include experiential learning activities to help students gain skills and confidence in a number of areas. One of the most commonly used is business plan development, which serves as a simulation of the process of creating a new venture. The purpose of this study is to explore and analyze business plan development to identify the activities it encompasses and the manner in which they can be assessed. Business plan content areas were identified and categorized, and expected learning outcomes were created using Bloom's revised taxonomy. The intent of the research is to: 1) begin to build consensus around some of the key elements of entrepreneurship education, 2) provide a foundation for better understanding the value and relevance of entrepreneurial knowledge and skills increasingly being delivered to engineering students, and 3) articulate the benefits of using business plan development as a pedagogical tool.
An adaptive short-time compensation scheme for improving power system stability and reducing turbine-generator shaft torsional torques

This paper presents an adaptive short-time compensation and reclosing technique for improving power system stability and reducing turbine-generator shaft torsional torques. The fundamental concept of the proposed technique is to interrupt unsymmetrical faults on "double-circuit" transmission lines using selective-pole switching. The transmission system is then balanced by switching on compensating capacitors in the interrupted phases. Adaptive reclosing is then applied to reclose the tripped phases and the compensating capacitors are switched off. The validity and effectiveness of the proposed technique are demonstrated through time-domain simulation studies on a test system using the EMTDC program.
Parameter estimation and uncertainty quantification using information geometry

In this work, we: (i) review likelihood-based inference for parameter estimation and the construction of confidence regions; and (ii) explore the use of techniques from information geometry, including geodesic curves and Riemann scalar curvature, to supplement typical techniques for uncertainty quantification, such as Bayesian methods, profile likelihood, asymptotic analysis and bootstrapping. These techniques from information geometry provide data-independent insights into uncertainty and identifiability, and can be used to inform data collection decisions. All code used in this work to implement the inference and information geometry techniques is available on GitHub.
Learning State Representations via Retracing in Reinforcement Learning

We propose learning via retracing, a novel self-supervised approach for learning the state representation (and the associated dynamics model) for reinforcement learning tasks. In addition to the predictive (reconstruction) supervision in the forward direction, we propose to include"retraced"transitions for representation / model learning, by enforcing the cycle-consistency constraint between the original and retraced states, hence improve upon the sample efficiency of learning. Moreover, learning via retracing explicitly propagates information about future transitions backward for inferring previous states, thus facilitates stronger representation learning for the downstream reinforcement learning tasks. We introduce Cycle-Consistency World Model (CCWM), a concrete model-based instantiation of learning via retracing. Additionally we propose a novel adaptive"truncation"mechanism for counteracting the negative impacts brought by"irreversible"transitions such that learning via retracing can be maximally effective. Through extensive empirical studies on visual-based continuous control benchmarks, we demonstrate that CCWM achieves state-of-the-art performance in terms of sample efficiency and asymptotic performance, whilst exhibiting behaviours that are indicative of stronger representation learning.
Accreting protoplanets: Spectral signatures and magnitude of gas and dust extinction at H α

Context. Accreting planetary-mass objects have been detected at H α, but targeted searches have mainly resulted in non-detections. Accretion tracers in the planetary-mass regime could originate from the shock itself, making them particularly susceptible to extinction by the accreting material. High-resolution (R > 50 000) spectrographs operating at H α should soon enable one to study how the incoming material shapes the line profile. Aims. We calculate how much the gas and dust accreting onto a planet reduce the H α flux from the shock at the planetary surface and how they affect the line shape. We also study the absorption-modified relationship between the H α luminosity and accretion rate. Methods. We computed the high-resolution radiative transfer of the H α line using a one-dimensional velocity–density–temperature structure for the inflowing matter in three representative accretion geometries: spherical symmetry, polar inflow, and magnetospheric accretion. For each, we explored the wide relevant ranges of the accretion rate and planet mass. We used detailed gas opacities and carefully estimated possible dust opacities. Results. At accretion rates of Ṁ ≲ 3 × 10−6 MJ yr−1, gas extinction is negligible for spherical or polar inflow and at most AH α ≲ 0.5 mag for magnetospheric accretion. Up to Ṁ ≈ 3 × 10−4 MJ yr−1, the gas contributes AH α ≲ 4 mag. This contribution decreases with mass. We estimate realistic dust opacities at H α to be κ ~ 0.01–10 cm2 g−1, which is 10–104 times lower than in the interstellar medium. Extinction flattens the LH α –Ṁ relationship, which becomes non-monotonic with a maximum luminosity LH α ~ 10−4 L⊙ towards Ṁ ≈ 10−4 MJ yr−1 for a planet mass ~10 MJ. In magnetospheric accretion, the gas can introduce features in the line profile, while the velocity gradient smears them out in other geometries. Conclusions. For a wide part of parameter space, extinction by the accreting matter should be negligible, simplifying the interpretation of observations, especially for planets in gaps. At high Ṁ, strong absorption reduces the H α flux, and some measurements can be interpreted as two Ṁ values. Highly resolved line profiles (R ~ 105) can provide (complex) constraints on the thermal and dynamical structure of the accretion flow.
ALMA Observations of NGC 6334S. II. Subsonic and Transonic Narrow Filaments in a High-mass Star Formation Cloud

We present a study of narrow filaments toward a massive infrared dark cloud, NGC 6334S, using the Atacama Large Millimeter/submillimeter Array. Thirteen gas filaments are identified using the H13CO+ line, while a single continuum filament is revealed by the continuum emission. The filaments present a compact radial distribution with a median filament width of ∼0.04 pc, narrower than the previously proposed “quasi-universal” 0.1 pc filament width. The higher spatial resolution observations and higher density gas tracer tend to identify even narrower and lower mass filaments. The filament widths are roughly twice the size of embedded cores. The gas filaments are largely supported by thermal motions. The nonthermal motions are predominantly subsonic and transonic in both identified gas filaments and embedded cores, which may imply that stars are likely born in environments of low turbulence. A fraction of embedded objects show a narrower velocity dispersion compared with their corresponding natal filaments, which may indicate that turbulent dissipation is taking place in these embedded cores. The physical properties (mass, mass per unit length, gas kinematics, and width) of gas filaments are analogous to those of narrow filaments found in low- to high-mass star-forming regions. The more evolved sources are found to be farther away from the filaments, a situation that may have resulted from the relative motions between the young stellar objects and their natal filaments.
MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network

Deformable registration consists of finding the best dense correspondence between two different images. Many algorithms have been published, but the clinical application was made difficult by the high calculation time needed to solve the optimisation problem. Deep learning overtook this limitation by taking advantage of GPU calculation and the learning process. However, many deep learning methods do not take into account desirable properties respected by classical algorithms. In this paper, we present MICS, a novel deep learning algorithm for medical imaging registration. As registration is an ill-posed problem, we focused our algorithm on the respect of different properties: inverse consistency, symmetry and orientation conservation. We also combined our algorithm with a multi-step strategy to refine and improve the deformation grid. While many approaches applied registration to brain MRI, we explored a more challenging body localisation: abdominal CT. Finally, we evaluated our method on a dataset used during the Learn2Reg challenge, allowing a fair comparison with published methods.
Parasitic capacitance of submicrometer MOSFET's

We systematically investigated the dependence of parasitic capacitance on gate length, gate electrode thickness, and gate oxide thickness using a 2-D device simulator. We showed that the model commonly used for parasitic capacitance is not accurate and also showed that more the rigorous model proposed by Kamchouchi should be used for submicrometer devices. Furthermore, we proposed a simple model that ensures the same accuracy as that of the Kamchouchi model.
Scale-Invariant Strength Assortativity of Streaming Butterflies

Bipartite graphs are rich data structures with prevalent applications and identifier structural features. However, less is known about their growth patterns, particularly in streaming settings. Current works study the patterns of static or aggregated temporal graphs optimized for certain down-stream analytics or ignoring multipartite/non-stationary data distributions, emergence patterns of subgraphs, and streaming paradigms. To address these, we perform statistical network analysis over web log streams and identify the governing patterns underlying the bursty emergence of mesoscopic building blocks, 2,2-bicliques known as butterflies, leading to a phenomenon that we call"scale-invariant strength assortativity of streaming butterflies". We provide the graph-theoretic explanation of this phenomenon. We further introduce a set of micro-mechanics in the body of a streaming growth algorithm, sGrow, to pinpoint the generative origins. sGrow supports streaming paradigms, emergence of 4-vertex graphlets, and provides user-specified configurations for the scale, burstiness, level of strength assortativity, probability of out-of-order records, generation time, and time-sensitive connections. Comprehensive Evaluations on pattern reproducing and stress testing validate the effectiveness, efficiency, and robustness of sGrow in realization of the observed patterns independent of initial conditions, scale, temporal characteristics, and model configurations. Theoretical and experimental analysis verify the robust ability of sGrow in generating streaming graphs based on user-specified configurations that affect the scale and burstiness of the stream, level of strength assortativity, probability of-of-order streaming records, generation time, and time-sensitive connections.
A NICER look at thermonuclear X-ray bursts from Aql X-1

We present spectral and temporal properties of all the thermonuclear X-ray bursts observed from Aql X-1 by the Neutron Star Interior and Composition Explorer (NICER) between 2017 July and 2021 April. This is the first systematic investigation of a large sample of type I X-ray bursts from Aql X-1 with improved sensitivity at low energies. We detect 22 X-ray bursts including two short recurrence burst events in which the separation was only 451 s and 496 s. We perform time resolved spectroscopy of the bursts using the fixed and scaled background (f_a method) approaches. We show that the use of a scaling factor to the pre-burst emission is the statistically preferred model in about 68% of all the spectra compared to the fixed background approach. Typically the f_a values are clustered around 1-3, but can reach up to 11 in a burst where photospheric radius expansion is observed. Such f_a values indicate a very significant increase in the pre-burst emission especially at around the peak flux moments of the bursts. We show that the use of the f_a factor alters the best fit spectral parameters of the burst emission. Finally, we employed a reflection model instead of scaling the pre-burst emission. We show that reflection models also do fit the spectra and improve the goodness of the fits. In all cases we see that the disc is highly ionized by the burst emission and the fraction of the reprocessed emission to the incident burst flux is typically clustered around 20%.
Diffracting and Non-diffracting random fields

We have generated and propagated both diffracting and non-diffracting speckles using the scattering of perfect optical vortices. The diffracting speckles have been realized in the near ﬁeld and non-diffracting speckles have been realized in the far ﬁeld, that is after taking the Fourier transform of near-ﬁeld speckles using a simple convex lens. The exact analytical expressions have been provided for the size of both diffracting as well as non-diffracting speckles and compared with our experimental results. We found that the experimental results are in good agreement with the theoretical results. These results may ﬁnd applications in classical cryptography and communication as we have both varying and non-varying random ﬁeld patterns with propagation distance. size of far ﬁeld speckles is independent of the propagation distance. We also verify our theoretical results experimentally for the near ﬁeld and far ﬁeld speckles.
Towards Empirical Sandwich Bounds on the Rate-Distortion Function

Rate-distortion (R-D) function, a key quantity in information theory, characterizes the fundamental limit of how much a data source can be compressed subject to a fidelity criterion, by any compression algorithm. As researchers push for ever-improving compression performance, establishing the R-D function of a given data source is not only of scientific interest, but also sheds light on the possible room for improving compression algorithms. Previous work on this problem relied on distributional assumptions on the data source (Gibson, 2017) or only applied to discrete data (Blahut, 1972; Arimoto, 1972). By contrast, this paper makes the first attempt at an algorithm for sandwiching the R-D function of a general (not necessarily discrete) source requiring only i.i. d. data samples. We estimate R-D sandwich bounds for a variety of artificial and real-world data sources, in settings far beyond the feasibility of any known method, and shed light on the optimality of neural data compression (Ball\'e et al., 2021; Yang et al., 2022). Our R-D upper bound on natural images indicates theoretical room for improving state-of-the-art image compression methods by at least one dB in PSNR at various bitrates. Our data and code can be found at https://github.com/mandt-lab/empirical-RD-sandwich.
Optical response of the Leggett mode in multiband superconductors in the linear response regime

We study optical responses of Leggett modes in multiband superconductors in the linear response regime. The Leggett mode is a collective mode unique to multiband superconductors that arises from relative phase ﬂuctuations of superconducting orders for diﬀerent bands. We use the Ginzburg-Landau (GL) description to study the collective modes in multiband systems. We ﬁnd that multiband superconductors generally allow a linear coupling between the Leggett mode and external electric ﬁelds due to the presence of a cross term between diﬀerent components of superconducting orders in the GL theory. The presence of a linear coupling for the Leggett mode is in sharp contrast with the absence of that for Higgs (amplitude) modes in single-band superconductors that only support nonlinear optical responses such as third harmonic generation (THG). We further conﬁrm such a linear coupling in multiband superconductors by a more microscopic description based on a diagrammatic approach. We study the collective modes within the random phase approximation (RPA) and compute their contribution to the linear optical conductivity. These ﬁndings suggest a new route to observe the Leggett mode by optical absorption.
LDP-Net: An Unsupervised Pansharpening Network Based on Learnable Degradation Processes

Pansharpening in remote sensing image aims at acquiring a high-resolution multispectral (HRMS) image directly by fusing a low-resolution multispectral (LRMS) image with a panchromatic (PAN) image. The main concern is how to effectively combine the rich spectral information of LRMS image with the abundant spatial information of PAN image. Recently, many methods based on deep learning have been proposed for the pansharpening task. However, these methods usually have two main drawbacks: 1) requiring HRMS for supervised learning; and 2) simply ignoring the latent relation between the MS and PAN image and fusing them directly. To solve these problems, we propose a novel unsupervised network based on learnable degradation processes, dubbed as LDP-Net. A reblurring block and a graying block are designed to learn the corresponding degradation processes, respectively. In addition, a novel hybrid loss function is proposed to constrain both spatial and spectral consistency between the pansharpened image and the PAN and LRMS images at different resolutions. Experiments on GaoFen-2, Worldview-2, and Worldview-3 images demonstrate that our proposed LDP-Net can fuse PAN and LRMS images effectively without the help of HRMS samples, achieving promising performance in terms of both qualitative visual effects and quantitative metrics.
One-shot Visual Reasoning on RPMs with an Application to Video Frame Prediction

—Raven’s Progressive Matrices (RPMs) are frequently used in evaluating human’s visual reasoning ability. Researchers have made considerable effort in developing a system which could automatically solve the RPM problem, often through a black-box end-to-end Convolutional Neural Network (CNN) for both visual recognition and logical reasoning tasks. Towards the objective of developing a highly explainable solution, we propose a One-shot Human-Understandable ReaSoner (Os-HURS), which is a two-step framework including a perception module and a reasoning module, to tackle the challenges of real-world visual recognition and subsequent logical reasoning tasks, respectively. For the reasoning module, we propose a “2+1” formulation that can be better understood by humans and signiﬁcantly reduces the model complexity. As a result, a precise reasoning rule can be deduced from one RPM sample only, which is not feasible for existing solution methods. The proposed reasoning module is also capable of yielding a set of reasoning rules, precisely modeling the human knowledge in solving the RPM problem. To validate the proposed method on real-world applications, an RPM-like One-shot Frame-prediction (ROF) dataset is constructed, where visual reasoning is conducted on RPMs constructed using real-world video frames instead of synthetic images. Experimental results on various RPM-like datasets demonstrate that the proposed Os-HURS achieves a signiﬁcant and consistent performance gain compared with the state-of-the-art models.
Charge localization in strongly correlated 
κ−(BEDT−TTF)2Cu[N(CN)2]I
 due to inherent disorder

In order to understand the physical properties of the series of organic conductors κ-(BEDTTTF)2Cu[N(CN)2]X with X = Cl, Br, and I, not only electronic correlations but also the effect of disorder has to be taken into account. While for Cland Br-containing salts the influence of both parameters were investigated and a universal phase diagram was proposed, the position of κ-(BEDT-TTF)2Cu[N(CN)2]I is still not settled. Here we have conducted transport, infrared, and dielectric measurements on single crystals of the title compound to clarify its electronic state at low temperatures. The correlation strength was determined as U/W ≈ 2.2; thus this salt is placed deeper in an insulating state compare to the two sister compounds. We found that inherent disorder leads to a Coulomb localized insulating state similar to the moderately x-ray-irradiated κ-(BEDTTTF)2Cu[N(CN)2]Cl.