Miracle-dz/UniFAR-FTUs huggingface dataset:
Review on Stress-Fractional Plasticity Models

Fractional calculus plays an increasingly important role in mechanics research. This review investigates the progress of an interdisciplinary approach, fractional plasticity (FP), based on fractional derivative and classic plasticity since FP was proposed as an efficient alternative to modelling state-dependent nonassociativity without an additional plastic potential function. Firstly, the stress length scale (SLS) is defined to conduct fractional differential, which influences the direction and intensity of the nonassociated flow of geomaterials owing to the integral definition of the fractional operator. Based on the role of SLS, two branches of FP, respectively considering the past stress and future reference critical state can be developed. Merits and demerits of these approaches are then discussed, which leads to the definition of the third branch of FP, by considering the influences of both past and future stress states. In addition, some specific cases and potential applications of the third branch can be realised when specific SLS are adopted.
Stress relaxation in a dilute bacterial suspension: the active–passive transition

This paper follows a recent article of Nambiar et al. (J. Fluid Mech., vol. 812, 2017, pp. 41–64) on the linear rheological response of a dilute bacterial suspension (e.g. E. coli) to impulsive starting and stopping of simple shear flow. Here, we analyse the time dependent nonlinear rheology for a pair of linear flows – simple shear (a canonical weak flow) and uniaxial extension (a canonical strong flow), again in response to impulsive initiation and cessation. The rheology is governed by the bacterium orientation distribution which satisfies a kinetic equation that includes rotation by the imposed flow, and relaxation to isotropy via rotary diffusion and tumbling. The relevant dimensionless parameters are the Péclet number $Pe\equiv \dot{\unicode[STIX]{x1D6FE}}\unicode[STIX]{x1D70F}$ , which dictates the importance of flow-induced orientation anisotropy, and $\unicode[STIX]{x1D70F}D_{r}$ , which quantifies the relative importance of the two intrinsic orientation decorrelation mechanisms (tumbling and rotary diffusion). Here, $\unicode[STIX]{x1D70F}$ is the mean run duration of a bacterium that exhibits a run-and-tumble dynamics, $D_{r}$ is the intrinsic rotary diffusivity of the bacterium and $\dot{\unicode[STIX]{x1D6FE}}$ is the characteristic magnitude of the imposed velocity gradient. The solution of the kinetic equation is obtained numerically using a spectral Galerkin method, that yields the rheological properties (the shear viscosity, the first and second normal stress differences for simple shear, and the extensional viscosity for uniaxial extension) over the entire range of $Pe$ . For simple shear, we find that the stress relaxation predicted by our analysis at small $Pe$ is in good agreement with the experimental observations of Lopez et al. (Phys. Rev. Lett., vol. 115, 2015, 028301). However, the analysis at large $Pe$ yields relaxations that are qualitatively different. Upon step initiation of shear, the rheological response in the experiments corresponds to a transition from a nearly isotropic suspension of active swimmers at small $Pe$ , to an apparently (nearly) isotropic suspension of passive rods at large $Pe$ . In contrast, the computations yield the expected transition to a nearly flow-aligned suspension of passive rigid rods at high $Pe$ . We probe this active–passive transition systematically, complementing the numerical solution with analytical solutions obtained from perturbation expansions about appropriate base states. Our study suggests courses for future experimental and analytical studies that will help understand relaxation phenomena in active suspensions.
Online Edge Learning Offloading and Resource Management for UAV-Assisted MEC Secure Communications

The mobile and flexible unmanned aerial vehicle (UAV) with mobile edge computing (MEC) can effectively relieve the computing pressure of the massive data traffic in 5G Internet of Things. In this paper, we propose a novel online edge learning offloading (OELO) scheme for UAV-assisted MEC secure communications, which can improve the secure computation performance. Moreover, the problem of information security is further considered since the offloading information of terminal users (TUs) may be eavesdropped due to the light-of-sight characteristic of UAV transmission. In the OELO scheme, we maximize the secure computation efficiency by optimizing TUs' binary offloading decision and resource management while guaranteeing dynamic task data queue stability and minimum secure computing requirement. Since the optimization problem is fractionally structured, binary constrained and multi-variable coupled, we first utilize the Dinkelbach method to transform the fractionally structured problem into a tractable form. Then, OELO generates the offloading decision based on deep reinforcement learning (DRL) and optimizes the resource management in an iterative manner through successive convex approximation (SCA). Simulation results show that the proposed scheme achieves better computing performance and enhances the stability and security compared with benchmarks.
Prioritizing Risks in Last Mile Delivery: A Bayesian Belief Network Approach

The remarkable explosion of e-commerce has marked the latest years of different industries and put forward a higher requirement for the last mile delivery. The last mile delivery is one of the most complex, costly, and inefficient processes along the entire logistics fulfillment chain in an e-commerce context. Its corresponding risks are major contributors to delivery failure. This work proposes a comprehensive framework on risk identification and analysis in the last mile delivery to support delivery planning. Risks were deduced from available literature, and others were induced through semi-structured interviews with experts in the field. Risks are categorized and the relative probability and severity of individual risks are determined. This study adopts a Bayesian Belief Network (BBN) model to identify the interdependency among risks and rank them, as the conventional ranking methods fail to take interdependency into account. The results indicate that privacy concerns, IT, and natural disasters are the most critical risks. This study will aid logistics service providers to ultimately deciding the solutions of last mile delivery that need to be utilized by prioritizing last mile delivery possible risks to increase their competitiveness and market share and minimize delivery costs.
THE LOW FREQUENCY OF DUAL ACTIVE GALACTIC NUCLEI VERSUS THE HIGH MERGER RATE OF GALAXIES: A PHENOMENOLOGICAL MODEL

Dual active galactic nuclei (AGNs) are natural byproducts of hierarchical mergers of galaxies in the ΛCDM cosmogony. Recent observations have shown that only a small fraction (∼0.1%–2.5%) of AGNs at redshift z ≲ 0.3 are dual with kpc-scale separations, which is rather low compared to the high merger rate of galaxies. Here we construct a phenomenological model to estimate the number density of dual AGNs and its evolution according to the observationally estimated major merger rates of galaxies and various scaling relations on the properties of galaxies and their central massive black holes. We show that our model reproduces the observed frequency and separation distribution of dual AGNs provided that significant nuclear activities are triggered only in gas-rich progenitor galaxies with central massive black holes and only when the nuclei of these galaxies are roughly within the half-light radii of their companion galaxies. Under these constraints, the observed low dual AGN frequency is consistent with the relatively high merger rate of galaxies and supports the hypothesis that major mergers lead to AGN/QSO activities. We also predict that the number of kpc-scale dual AGNs decreases with increasing redshift and only about 0.02%–0.06% of AGNs are dual AGNs with double-peaked narrow line features at redshifts of z ∼ 0.5–1.2. Future observations of high-redshift dual AGNs would provide a solid test for this prediction.
Spin transfer torque devices utilizing the giant spin Hall effect of tungsten

We report a giant spin Hall effect in β-W thin films. Using spin torque induced ferromagnetic resonance with a β-W/CoFeB bilayer microstrip, we determine the spin Hall angle to be |θSHβ-W|=0.30±0.02, large enough for an in-plane current to efficiently reverse the orientation of an in-plane magnetized CoFeB free layer of a nanoscale magnetic tunnel junction adjacent to a thin β-W layer. From switching data obtained with such 3-terminal devices, we independently determine |θSHβ-W|=0.33±0.06. We also report variation of the spin Hall switching efficiency with W layers of different resistivities and hence of variable (α and β) phase composition.
Bulletin of Electrical Engineering and Informatics

Received Jun 23, 2022 Revised Sep 8, 2022 Accepted Sep 27, 2022 The phase synchronization control technique of shunt active power filter (SAPF) is discussed in this article. Due to the harmful effects of harmonic currents on power systems, the issue of decreasing their effects has generated a lot of research. A SAPF is acknowledged as the most dependable instrument in this area. The SAPF will detects the current harmonics present in the power system and also generates and injects the corrective current mitigation in the power system to reduce the current harmonics. This implies that, aside from the capacity to create compensating current, it is more essential to ensure that the SAPF can work in phase with the operational power system so that the compensating current will properly injected. As a result, while constructing SAPF control algorithms, correct synchronisation techniques must be incorporated. In this article, the different types of phase synchronization approach have been discussed such as phaselocked loop (PLL), adaptive linear neuron (Adaline), and unit vector. The results of the study may be used as a guideline and source of information for determining the best method for synchronising SAPF with the associated power system.
Polarization as a probe of dusty environments around Type Ia supernovae: radiative transfer models for SN 2012dn

Geometry of circumstellar (CS) medium around supernovae (SNe) provides important diagnostics to understand the nature of their progenitors. In this paper, properties of CS dust around SN 2012dn, a super-Chandrasekhar candidate Type Ia supernova (SC-SN), have been studied through detailed three dimensional radiation transfer simulations. With the detected near-infrared excess from SN 2012dn, we show that it has a disk-like dusty CS environment whose mass is roughly consistent with a branch of an accreting white dwarf system (the single degenerate scenario). We show that a similar system should produce polarization signals up to $\sim 8$ \% in the $B$ band, depending on the viewing direction if polarimetric observation is performed. We predict that the maximum polarization is reached around $\sim 60$ days after the $B$-band maximum. We show that the temporal and wavelength dependence of the polarization signals, together with other unique features, can be easily distinguished from the interstellar polarization and intrinsic SN polarization. Indeed, the small polarization degree observed for normal Type Ia SNe (SNe Ia) can constrain a parameter space in the CS dust mass and distribution. We thus encourage multi-band polarimetric observations for SNe Ia, especially for outliers including SC-SNe for which some arguments for the single degenerate scenario exist but the polarization data are very rare so far.
Model Predictive Control Based on ILQR for Tilt-Propulsion UAV

The transition flight of tilt-propulsion UAV is a complex and time-varying process, which leads to great challenges in the design of a stable and robust controller. This work presents a unified model predictive controller, which can handle the full envelope from vertical take-off and landing to cruise flight, to mean that the UAV can achieve a near-optimal transition flight under uncertainty conditions. Firstly, the nonlinear dynamic model of the tilt-propulsion UAV is developed, in which the aerodynamic/propulsion coupling effect of the ducted propeller is considered. Then, a control framework, including global trajectory planning and finite horizon control, is designed. Taking the planned global trajectory as the reference input, a controller is proposed with an inner layer based on ILQR optimization and an outer layer based on feedback correction and forward rolling of the MPC frame. The ILQR-MPC controller has high computational efficiency to deal with nonlinear problems, and has the ability to give full play to UAV’s control ability and suppress uncertainty. Finally, the simulation results show that ILQR-MPC controller obviously performs better than the ILQR feedforward controller, and gains a scheduling PID controller and MPC controller.
SEXTANS' COLD SUBSTRUCTURES AS A DYNAMICAL JUDGE: CORE, CUSP, OR MOND?

The cold dark matter model predicts cuspy dark matter (DM) halos. However, it has been found that in some low-mass galaxies, cored dark halos provide a better description of their internal dynamics. Here we give constraints on the dark halo profile in the Sextans dwarf spheroidal galaxy by studying the longevity of two cold kinematic substructures detected in this galaxy. We perform N-body simulations of a stellar clump in the Sextans dwarf galaxy, including a live DM halo and the main stellar component. We find that if the dark halo is cuspy, stellar clumps orbiting with semi-major axis ≈400 pc are disrupted in ∼5 Gyr, even if the clump is initially as compact stellar cluster with a radius of rc = 5 pc. Stellar clusters in an initial orbit with semi-major axis ⩽250 pc may survive to dissolution, but their orbits decay toward the center by dynamical friction. In contrast, the stellar clumps can persist for a Hubble time within a cored DM halo, even if the initial clump's radius is as extended as rc = 80 pc. We also study the evolution of the clump in the MONDian context. In this scenario, we find that even an extended stellar clump with radius rc = 80 pc survives for a Hubble time, but an unrealistic value for the stellar mass-to-light ratio of 9.2 is needed.
Fe K emission from active galaxies in the COSMOS field

We present a rest-frame spectral stacking analysis of ∼1000 X-ray sources detected in the XMM-COSMOS field to investigate the iron-K line properties of active galaxies beyond redshift z ∼ 1. In Type I AGN that have a typical X-ray luminosity of LX ∼ 1.5 × 10 44 (erg s −1 )a ndz ∼ 1.6 the cold Fe K at 6.4 keV is weak (EW ∼ 0.05 keV), which agrees with the known trend. In contrast, high-ionization lines of Fe xxv and Fe xxvi are pronounced. These high-ionization Fe K lines appear to have a connection with high accretion rates. While no broad Fe emission is detected in the total spectrum, it might be present, albeit at low significance (∼2σ), when the X-ray luminosity is restricted to the range below 3 × 10 44 erg s −1 , or when an intermediate range of Eddington ratio around λ ∼ 0.1 is selected. In Type II AGN, both cold and high-ionzation lines become weak with increasing X-ray luminosity. However, we detected strong high-ionization Fe K (EW ∼ 0.3 keV) in the spectrum of objects at z > 2, while we found no 6.4 keV line. We also found that the primary source of the high-ionization Fe K emission are those objects detected with Spitzer-MIPS at 24 μm.
Pollen profile of bee pollen from semiarid Northeastern Brazil

The Northeastern Brazil has xerical vegetation with different biomes. Its botanical heterogeneity represents shelter for diverse fauna. The region is rich in bees, which demand the resources offered by the plants, and provide pollination services. Bee pollen has been used as a beneficial food source for humans due to its high nutritional content and being a natural antioxidant. Pollen analysis can determine the plant species visited by bees, using them as the source of pollen supply. This study aimed to identify the main pollen types present in the pollen collected by Apis mellifera in the Northeastern Brazil and to define its botanical affinity. Commercial bee pollen samples produced in the region were treated by acetolysis method, mounted on slides, and the frequency of occurrence was estimated for each type of pollen. It was possible to distinguish 113 pollen types belonging to 35 botanical families, distributed in 92 genera. Fabaceae and Asteraceae were the families that most contributed to pollen types. Pollen types of Cocos nucifera and Myrcia were found in more than 50% of the samples. The families Arecaceae and Fabaceae showed high beekeeping potential. There is a striking similarity between the pollen samples from the states of Pernambuco and Rio Grande Norte.
How can innovation district performance be assessed? Insights from South East Queensland, Australia

Purpose Many cities across the world are actively investing in ways to excel in the innovation economy through the development of innovation districts as one of the most popular policy options. While innovation districts are among the leading drivers of innovation activities in cities, they are also high-cost and high-risk investments. Besides, holistic approaches for assessing these districts’ multifaceted performances are scarce. Bridging this knowledge gap is critical, hence, this paper aims to explore how innovation district performance can be assessed through a classification framework. Design/methodology/approach The paper introduces a multidimensional innovation district classification framework and applies it into Australian innovation districts with divergent features, functions, spatial and contextual characteristics. The study places 30 innovation districts from South East Queensland under the microscope of the framework to assess the multifaceted nature of innovation district performance. It uses qualitative analysis method to analyse both the primary and secondary data, and descriptive analysis with basic excel spreadsheet calculations to analyse the validity of the data. Findings The data analysis clusters 30 innovation districts from South East Queensland under three performance levels – i.e. desired, acceptable and unsavoury – concerning their form, feature and function characteristics. Originality/value The results disclose that the framework is a practical tool for informing planners, developers and managers on innovation district performances, and it has the capability to provide guidance for policymakers on their policy and investment decisions regarding the most suitable innovation district types and characteristics to consider.
A CH star in the globular cluster NGC 6426

We report on the serendipitous discovery of a carbon star near the centre of the low-metallicity globular cluster NGC 6426. We determined its membership and chemical properties using medium-resolution spectra. The radial velocity of -159 km/s makes it a member of the cluster. We used photometric data from the literature and the COMARCS stellar atmospheric models to derive its luminosity, effective temperature, surface gravity, metallicity, and approximate C, N, and O abundance ratios. According to these properties, we suggest that this star is a genuine carbon rich low-metallicity AGB star.
The merger of two compact stars: a tool for dense matter nuclear physics

We discuss the different signals, in gravitational and electromagnetic waves, emitted during the merger of two compact stars. We will focus in particular on the possible contraints that those signals can provide on the equation of state of dense matter. Indeed, the stiffness of the equation of state and the particle composition of the merging compact stars, strongly affect e.g. the life time of the post-merger remnant and its gravitational wave signal, the emission of the short gamma-ray-burst, the amount of ejected mass and the related kilonova. The first detection of gravitational waves from the merger of two compact stars in August 2017, GW170817, and the subsequent detections of its electromagnetic counterparts, GRB170817A and AT2017gfo, is the first example of the era of "multi-messenger astronomy": we discuss what we have learned from this detection on the equation of state of compact stars and we provide a tentative interpretation of this event, within the two families scenario, as due to the merger of a hadronic star with a quark star.
Coulomb blockade microscopy of spin density oscillations and fractional charge in quantum spin Hall dots

We evaluate the spin density oscillations arising in quantum spin Hall quantum dots created via two localized magnetic barriers. The combined presence of magnetic barriers and spin-momentum locking, the hallmark of topological insulators, leads to peculiar phenomena: a half-integer charge is trapped in the dot for antiparallel magnetization of the barriers, and oscillations appear in the in-plane spin density, which are enhanced in the presence of electron interactions. Furthermore, we show that the number of these oscillations is determined by the number of particles inside the dot, so that the presence or the absence of the fractional charge can be deduced from the in-plane spin density. We show that when the dot is coupled with a magnetized tip, the spatial shift induced in the chemical potential allows to probe these peculiar features.
A Comparison Analysis of Machine Learning Algorithms on Cardiovascular Disease Prediction

People nowadays are engrossed in their daily routines, concentrating on their jobs and other responsibilities while ignoring their health. Because of their hurried lifestyles and disregard for their health, the number of people becoming ill grows daily. Furthermore, most of the population suffers from a disease such as cardiovascular disease. Cardiovascular disease kills 35% of the world's population, according to W.H. O. A person's life can be saved if a heart disease diagnosis is made early enough. Still, it can also be lost if the diagnosis is constructed incorrectly. Therefore, predicting heart disease will become increasingly relevant in the medical sector. The volume of data collected by the medical industry or hospitals, on the other hand, can be overwhelming at times. Time-series forecasting and processing using machine learning algorithms can help healthcare practitioners become more efficient. In this study, we discussed heart disease and its risk factors and machine learning techniques and compared various heart disease prediction algorithms. Predicting and assessing heart problems is the goal of this research.