Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatio-temporal Graph Learning for Epidemic Prediction

The COVID-19 pandemic has posed great challenges to public health services, government agencies, and policymakers, raising huge social conflicts between public health and economic resilience. Policies such as reopening or closure of business activities are formulated based on scientific projections of infection risks obtained from infection dynamics models. Though most parameters in epidemic prediction service models can be set with domain knowledge of COVID-19, a key parameter, namely, human mobility, is often challenging to estimate due to complex spatio-temporal correlations and social contexts under escalating COVID-19 facilities. Moreover, how to integrate the various implicit features to accurately predict infectious cases is still an open issue. To address this challenge, we formulate the problem as a spatio-temporal network representation problem and propose STEP, a Spatio-Temporal Epidemic Prediction framework, to estimate pandemic infection risk of a city by integrating various real-world conditions (e.g., City Risk Index, climate, and medical conditions) into graph-structured data. We also employ a multi-head attention mechanism in representation learning to extract implicit features for a given city. Extensive experiments have been conducted upon the real-world dataset for 51 states (50 states and Washington, D.C.) of the USA. Experimental results show that STEP can yield more accurate pandemic infection risk estimation than baseline methods. Moreover, STEP outperforms other methods in both short-term and long-term prediction.
Propagation of a spheromak: 1. Some comparisons of cylindrical and spherical magnetic clouds

A series of our papers in the Journal of Geophysical Research, 1995-1996, was devoted to simulations of propagation of cylindrical magnetic clouds (flux ropes) having different orientation of their axes to the ecliptic plane and initial parameters. In this paper we supplement our study with the case of detached spherical plasmoids. By varying the velocity, density, temperature, and the magnetic field strength inside clouds, we simulate a number of plasmoid scenarios that can be compared with observations and with existing models and simulations of flux ropes. Initially, the spherical clouds have a poloidal magnetic field configuration within a sphere. During the propagation they evolve into toroids (i.e., closed flux ropes). Radial profiles of magnetic field and plasma quantities in these toroids are similar to cylindrical magnetic clouds. However, they are different in the central (now external) part of the cloud, where the poloidal axis was originally situated, that is, in the toroid's hole. Here the magnetic field is greatly enhanced but does not rotate, and the temperature decrease is absent. The deceleration and transit time to 1 AU is comparable between spherical and cylindrical clouds. The shock wave ahead of a spherical cloud is about 2 times closer than for a corresponding cylindrical cloud.
A Multiscale Protein Abundance Structured Population Kinetic Model Systematically Explores the Design Space of Constitutive and Inducible CAR-T cells

Engineered chimeric antigen receptor (CAR)-T cells are designed to bind to antigens overexpressed on the surface of tumor cells and induce tumor cell lysis. However, healthy cells can express these antigens at lower abundances and can get lysed by CAR-T cells. A wide variety of CAR-T cells have been designed that increase tumor cell elimination while decreasing destruction of healthy cells. However, given the cost and labor-intensive nature of such efforts, a systematic exploration of potential hypotheses becomes limited. To this end, we develop a framework (PASCAR) by combining multiscale population dynamic models and multi-objective optimization approaches with data obtained from published cytometry and cytotoxicity assays to systematically explore design space of constitutive and tunable CAR-T cells. We demonstrate PASCAR can quantitatively describe in vitro and in vivo results for constitutive and inducible CAR-T cells and can successfully predict experiments outside the training data. Our exploration of the CAR design space reveals that CAR affinities in an intermediate range of dissociation constants (KD) in constitutive and tunable CAR-T cells can dramatically decrease healthy cell lysis but sustain a high rate of tumor cell killing. In addition, our modeling provides guidance towards optimal tuning of CAR expressions in synNotch CAR T cells. The proposed framework can be extended for other CAR immune cells.
The ecology of viruses in urban rodents with a focus on SARS-CoV-2

Wild animals are naturally infected with a range of viruses, some of which may be zoonotic for humans. During the human COIVD pandemic there was also the possibility of rodents acquiring SARS-CoV-2 from people, so-called reverse zoonoses. To investigate this we have sampled rats (Rattus norvegicus) and mice (Apodemus sylvaticus) from urban environments in 2020 during the human COVID-19 pandemic. We metagenomically sequenced lung and gut tissue and faeces for viruses, PCR screened for SARS-CoV-2, and serologically surveyed for anti-SARS-CoV-2 Spike antibodies. We describe the range of viruses that we found in these two rodent species. We found no molecular evidence of SARS-CoV-2 infection, though in rats we found lung antibody responses and evidence of neutralisation ability, that are consistent with rats being exposed to SARS-CoV-2 and / or exposed to other viruses that result in cross-reactive antibodies.
Effective Boundary Conditions for a 2d iNhomogeneous Nonlinear Thin Layer Coated On a Metallic Surface - Abstract

Approximate boundary conditions for an inhomogeneous nonlinear thin layer coated on a perfectly conducting plane are derived through an asymptotic expansion of the field in power series of the thickness. Numerical results show that the second order approximate boundary condition gives a sufficient accuracy for all incident angles when the coating thickness is much smaller than the wavelength. The case when the nonlinear thin layer is coated on a curved metallic surface is also considered.
Optimal Design of Smart Skin Structures for Thermo-Mechanical Buckling and Vibration using a Genetic Algorithm

Smart skin was analyzed for buckling and vibration. Thermal effects were taken into consideration, because the temperature rise may induce buckling or resonance on the skin. The model was a multi-layer sandwich structure composed of carbon/epoxy, glass/epoxy and a dielectric polymer. A numerical model was used to find design data from various parameters such as the aspect ratio, thickness of core, ply-angle and area ratio. In addition, each variable's effect on the model was studied. The results were compared to a case in which the thermal effect was not considered. The formulation of the structural model was based on the first-order shear deformation plate theory (FSDT) and numerical results were obtained by finite element method. Finally, a genetic algorithm was used to maximize the critical temperature and natural frequency of the structure.
Sparsity constraint SAR speckle reduction based on redundant multiscale ridgelet dictionary

In this paper, we proposed a SAR speckle reduction method based on sparse and redundant representations over multiscale ridgelet dictionary. Firstly, the multiscale ridgelet function is proposed. And then based on it, the multiscale ridgelet dictionary is constructed, which can sparsely represent the SAR images. Finally, we propose a global image prior that forces sparsity over small patches in every location in the image. We define a maximum a-posteriori probability (MAP) estimator as the minimizer of a well-defined global penalty term. The speckle reduction leads to a simple iterated patch-by-patch sparse coding and averaging algorithm. The experimental results demonstrate that the proposed method performs better than several other existing methods in terms of quantitative performance as well as in term of visual quality of the images.
The basic equations for the large eddy simulation of turbulent flows in complex geometry

Abstract The equations for large eddy simulation (LES) of turbulent flows are derived by applying a "filtering" operation to the Navier-Stokes equations. LES of inhomogeneous turbulent flows requires the use of filters with variable filter width. The use of such filters invalidates the standard derivation of the basic equations for the filtered fields since the filtering operation in general does not commute with the operation of differentiation. In this paper an alternate definition of the filtering operation based on the mapping function of the nonuniform grid is introduced. It is shown that with this modified definition the filtering and differentiation operations commute up to an error which is second order in the filter width. It is also shown that the commutation error can be expressed in terms of the filtered field and its derivatives as an asymptotic series in the square of the filter width. These results are then applied to the Navier-Stokes equations to derive the basic equations satisfied by the filtered fields.
Comptonization of Diffuse Ambient Radiation by a Relativistic Jet: The Source of Gamma Rays from Blazars?

Recent Energy Gamma Ray Experiment Telescope (EGRET) observations of blazars have revealed strong, variable gamma-ray fluxes with no signatures of gamma-ray absorption by pair production. This radiation probably originates from the inner parts of relativistic jets which are aimed nearly toward us. On sub-parsec scales, the jet will be pervaded by radiation from the broad-line region, as well as by photons from the central continuum source (some of which will be scattered by thermal plasma). In a frame moving with the relativistic outflow, the energy of this ambient radiation would be enhanced. This radiation would be Comptonized by both cold and relativistic electrons in the jet, yielding (in the observer's frame) a collimated beam of X-rays and gamma rays. On the assumption that this process dominates self-Comptonization of synchrotron radiation, we develop a self-consistent model for variable gamma-ray emission, involving a single population of relativistic electrons accelerated by a disturbance in the jet. The spectral break between the X-ray and gamma-ray band, observed in 3C 279 and deduced for other blazars, results from inefficient radiative cooling of lower energy electrons. The existence of such a break strongly favors a model involving Comptonization of an external radiation field over a synchrotron self-Compton model. We derive constraints on such model parameters as the location and speed of the source, its dimensions and internal physical parameters, the maximum photon energies produced in the source, and the density and distribution of ambient radiation. Finally, we discuss how observations might discriminate between our model and alternative ones invoking Comptonization of ambient radiation.
Swot Analysis to Reform MSMEs

The role of Micro, Small and Medium Enterprises (MSMEs) in the industrial sector is growing very fast. This research paper aims at analysing strength, weakness, opportunities and threat of MSME sector for the increased employment and GDP. A questionnaire was developed and administered to 163 randomly chosen MSMEs in peenya industry, Bangalore. The primary data collected through personal visits, email, and little through telephone calls. This study will help to analyse the strength and weakness of current business practices pursued by MSMEs in peenya industry using the available resources. The study also reveals the opportunities and threats to overcome by using various technological innovations. The study result suggests and concludes that industries need to reform their strategies to manage and control the threats in order to eradicate the weakness. Also need to strengthen the opportunities through more affordable technologies is important
Introducing mathematical modelling skills to students and the use of posters in assessment

ABSTRACT A well balanced mathematics curriculum consists of a mixture of concepts, context, and skills. It is now recognized that one's ability to use mathematics in problem solving is equally important as developing mathematical skills. Developing good problem solving skills is not easy. In this paper we describe the outcomes of an intensive one month mathematical modelling course and the use of posters in peer assessment of the student work.
HIPPO

Hand-grip strength is widely used to estimate muscle strength and it serves as a general indicator of the overall health of a person, particularly in aging adults. Hand-grip strength is typically estimated using dynamometers or specialized force resistant pressure sensors embedded onto objects. Both of these solutions require the user to interact with a dedicated measurement device which unnecessarily restricts the contexts where estimates are acquired. We contribute HIPPO, a novel non-intrusive and opportunistic method for estimating hand-grip strength from everyday interactions with objects. HIPPO re-purposes light sensors available in wearables (e.g., rings or gloves) to capture changes in light reflectivity when people interact with objects. This allows HIPPO to non-intrusively piggyback everyday interactions for health information without affecting the user's everyday routines. We present two prototypes integrating HIPPO, an early smart glove proof-of-concept, and a further optimized solution that uses sensors integrated onto a ring. We validate HIPPO through extensive experiments and compare HIPPO against three baselines, including a clinical dynamometer. Our results show that HIPPO operates robustly across a wide range of everyday objects, and participants. The force strength estimates correlate with estimates produced by pressure-based devices, and can also determine the correct hand grip strength category with up to 86% accuracy. Our findings also suggest that users prefer our approach to existing solutions as HIPPO blends the estimation with everyday interactions.
Nanoplate elasticity under surface reconstruction

Using classical molecular statics simulations, we show that nanoplate elasticity strongly depends on surface reconstruction and alignment of bond chains. Because of its well-established surface reconstructions and the readily available interatomic potential, diamond-cubic silicon is the prototype of this study. We focus on silicon nanoplates of high-symmetry surfaces, {111} and {100}; with 7×7 and 2×1 reconstructions. Nanoplates with unreconstructed {111} surfaces are elastically stiffer than bulk. In contrast, the same nanoplates with 7×7 reconstructed {111} surfaces are elastically softer than bulk. On {100} surfaces, the 2×1 surface reconstruction has little impact. The bond chains are along one of the two ⟨110⟩ directions, making the two ⟨110⟩ directions nonequivalent. The alignment of the bond chains on the opposite surfaces of a nanoplate dictates its elastic anisotropy. The sensitivity of nanoplate elasticity on details of surface atomic arrangements may impact the application of nanoplates (or nan.
When and why people perform mindless math

In this paper, we show that the presence of numbers in a problem tempts people to perform mathematical operations even when the correct answer requires no math, which we term “mindless math”. In three pre-registered studies across two survey platforms (total N = 3,193), we investigate how mindless math relates to perceived problem difficulty, problem representation, and accuracy. In Study 1, we show that increasing the numeric demands of problems leads to more mindless math (and fewer correct answers). Study 2 shows that this effect is not caused by people being wary of problems that seem too easy. In Study 3, we show that this effect is robust over a wider range of numeric demands, and in the discussion we offer two possible mechanisms that would explain this effect, and the caveat that at even harder levels of numeric demands the effect may invert such that much harder math increases accuracy relative to moderately hard math.
A new parameterization of surface drag in the marginal sea ice zone

A parameterization of subgridscale surface fluxes over the marginal sea ice zone which has beenused earlier in several studies is modified and applied to a nonhydrostatic mesoscale model.The new scheme accounts for the form drag of ice floes and is combined with a so-called fluxaveraging method for the determination of surface fluxes over inhomogeneous terrain. Individualfluxes over ice and water are calculated as a function of the blending height. It is shown bycomparison with observations that the drag coefficients calculated with the new parameterizationagree well with data. The original scheme strongly overestimates the form drag effect.An improvement is mainly obtained by an inclusion of stratification and by use of a moreadequate coefficient of resistance for individual ice floes. The mesoscale model is applied to officeflows over the polar marginal sea ice zone. The model results show that under certainmeteorological conditions the form drag can have a strong influence on the near-surface windvelocity and on the turbulent fluxes of momentum. Four case studies are carried out. Themaximum influence of form drag occurs in the case with strong unstable stratification and withwind oblique to the ice edge. Under these conditions the wind stress on sea ice is modified byat least 100% for ice concentrations less than 50% if form drag is taken into account.
Abundances in red giant stars - Carbon and oxygen isotopes in carbon-rich molecular envelopes

Millimeter-wave observations have been made of isotopically substituted CO toward the envelopes of 11 carbon-rich stars. In every case, C-13O was detected and model calculations were used to estimate the C-12/C-13 abundance ratio. C-17O was detected toward three, and possibly four, envelopes, with sensitive upper limits for two others. The CO-18 variant was detected in two envelopes. New results include determinations of oxygen isotopic ratios in the two carbon-rich protoplanetary nebulae CRL 26688 and CRL 618. As with other classes of red giant stars, the carbon-rich giants seem to be significantly, though variably, enriched in O-17. These results, in combination with observations in interstellar molecular clouds, indicate that current knowledge of stellar production of the CNO nuclides is far from satisfactory.
End-to-End Deep Learning Proactive Content Caching Framework

Proactive content caching has been proposed as a promising solution to cope with the challenges caused by the rapid surge in content access using wireless and mobile devices and to prevent significant revenue loss for content providers. In this paper, we propose an end-to-end Deep Learning framework for proactive content caching that models the dynamic interaction between users and content items, particularly their features. The proposed model performs the caching task by building a probability distribution across different content items, per user, via a Deep Neural Network model and supports, both, centralized and distributed caching schemes. In addition, the paper addresses the key question: Do we need an explicit user-item pairs-based recommendation system in content caching? i.e., do we need to develop a recommendation system while tackling the content caching problem? To this end, an end-to-end Deep Learning framework is introduced. Finally, we validate our approach through extensive experiments on a real-world, public data set, coined MovieLens. Our experiments show consistent performance gains against its counterparts, where our proposed Deep Learning Caching module, dubbed as DLC, significantly outperforms state-of-the-art content caching schemes, serving as a baseline. Our code is available here: https://github.com/heshameraqi/Proactive-Content-Caching-with-Deep-Learning.
An Energy Function-Based and Norm-Free Event-Triggering Approach to Schedule Control Data Transmissions in State Feedback Control

In this paper, we propose a new event-triggering approach to schedule control data transmissions in state feedback control of linear time-invariant dynamical systems. Specifically, an energy function-based and norm-free event-triggering condition is presented, where the embedded processor broadcasts a sampled data of its control signal value through a zero-order-hold operator to the dynamical system when the left side of the event-triggering condition equals to its right side. Here, the energy function-based feature implies that the right side of this event-triggering condition involves an energy function as well as its time-derivative for making the selection of its right side user-adjustable. Moreover, the norm-free feature implies that the left side of this event-triggering condition does not depend on signal norms to yield better control data transmission reduction. We also present illustrative numerical examples in order to demonstrate the efficacy of the proposed event-triggering approach in scheduling state feedback control data transmissions.
A Mixed-Signal Neuron Circuit Implementation using 40nm Technology

This paper presents a mixed-signal neuron circuit implementation for an accelerated LIF array (ALIFA) NIC, designed using the TSMC 40nm PDK technology. The ALIFA operates in the continuous time mixed-signal mode and features largely configurable components with accelerated dynamics. The LIF neurons are interconnected via synapses, and integrate the net current from all the synapses, before emitting an output digital spike. Analogue capacitive memory is implemented to store the circuits’ biasing, which provides wide tuning ranges for synaptic and membrane time constants, as well as refractory periods. The simulation results of the ALIFA convey the same characteristics described by the mathematical model of the LIF. Compared to earlier work, the area consumption has been reduced, as well as power from 14. 4μW [1] to 11. 5μW. The synapse parameters of the synaptic resistance and time constant ranges are increased to (0.528MΩ-15.873MΩ) and (0.528μs-15.873μs) respectively.
The growth of a turbulent patch in a stratified fluid

The behaviour of a turbulent region generated within a linearly-stratified fluid by an external energy source has been studied experimentally. A monoplanar grid that generated small-amplitude oscillations was used as the energy source. The results show that the mixed layer initially grows rapidly, as in an unstratified fluid, but when its physical vertical size becomes rf ∼ (K1/N)½, at a time tf ≈ 4.0 N−1, where N is the buoyancy frequency and K1 is the ‘action’ of the grid, the buoyancy forces become dominant and drastically reduce further vertical growth of the patch. While the patch size remains at rf, a well-defined density interfacial layer is formed at the entrainment interface. An important feature of the interfacial layer is the presence of internal waves, excited by the mixed-layer turbulence. If the grid oscillations are continuously maintained, the interfacial waves break and cause turbulent mixing, thereby increasing the size of the patch beyond rf at a very slow rate. Theoretical estimates are made for the growth characteristics and are compared with the experimental results.