Miracle-dz/UniFAR-FTUs huggingface dataset:
Holographic Optical Tweezers: Techniques and Biomedical Applications

Holographic optical tweezers (HOT) is a programmable technique used for manipulation of microsized samples. In combination with computer-generation holography (CGH), a spatial light modulator reshapes the light distribution within the focal area of the optical tweezers. HOT can be used to realize real-time multiple-point manipulation in fluid, and this is useful in biological research. In this article, we summarize the HOT technique, discuss its recent developments, and present an overview of its biological applications.
Non-Perturbative Qft Methods and Their Applications

Conformal boundary conditions - and what they teach us, V.B. Petkova and J.-B. Zuber a physical basis for the entropy of the AdS3 black hole, S. Fernando and F. Mansouri spinon formulation of the Kondo problem, A. Klumper and J.R. Reyes-Martinez boundary integrable quantum field theories, P. Dorey finite size effects in integrable quantum field theories, F. Ravanini nonperturbative analysis of the two-frequency Sine-Gordon model, Z. Bajnok et al screening in hot SU(2) gauge theory and propagators in 3D adjoint Higgs model, A. Cucchieri et al effective average action in statistical physics and quantum field theory, Ch. Wetterich phase transitions in non-Hermitean matrix models and the "single ring" theorem, J. Feinberg et al unravelling the mystery of flavor, A. Falk the Nahm transformation of "R2 X T2", C. Ford a 2D integrable axion model and target space duality, P. Forgacs supersymmetric ward identities and chiral symmetry breaking in SUSY QED, M.L. Walker. (Part contents).
Experimental analysis of particle deposition in fibrous depth filters during gas cleaning using X-ray microscopy

Abstract Microscopic data on airborne particle separation in depth filters are a key for understanding and predictive modeling of the evolution of filtration properties such as pressure drop and efficiency during the filtration process. Tomographic imaging techniques (e.g., MRI, CT) are excellent methods for 3D-resolved analysis of microscopic loading behavior, but these are often limited in terms of spatial resolution and because of the low contrast between filter material and particles. In this study, an X-ray microscope was used to analyze the separation of iodine-containing particles (d50,3 =1.5 µm) in a coarse dust filter (porosity: 0.98; fiber diameter: 24 µm). The use of iodine-containing particles produced sufficient contrast for segmentation and analysis of the particle deposits produced during filtration. The established method allowed the analysis of the deposits within the material in terms of mass, size distribution, and the shape of the formed deposits in time and space. The data presented in this work provide new insights and methods for an improved understanding of the dynamic behavior of filter materials. Graphical Abstract
A multi-wavelength study of the gravitational lens COSMOS J095930+023427

We present a multi-wavelength study of the gravitational lens COSMOS J095930+023427 (z1 = 0.892), together with the associated galaxy group along the line of sight located at z ∼ 0.7, and the lensed background galaxy. The source redshift is currently unknown, but estimated to be at zs ∼ 2. This analysis is based on publicly available HST, Subaru and Chandra imaging data, as well as VLT spectroscopy. The lensing system is an early-type galaxy showing a strong [OII] emission line, and produces four bright images of the distant background source. It has an Einstein radius of 0.79″, about four times larger than the effective radius. We perform a lensing analysis using both a singular isothermal ellipsoid and a peudo-isothermal elliptical mass distribution for the lensing galaxy, and find that the final results on the total mass, the dark matter (DM) fraction within the Einstein radius and the external shear due to a foreground galaxy group are robust with respect to the choice of the parametric model and the source redshift (yet unknown). We measure the luminous mass from the photometric data, and find the DM fraction within the Einstein radius fDM to be between 0.71 ± 0.13 and 0.79 ± 0.15, depending on the unknown source redshift. Meanwhile, the non-null external shear found in our lensing models supports the presence and structure of a galaxy group at z ∼ 0.7, and an independent measurement of the 0.5−2 keV X-ray luminosity within 20″ around the X-ray centroid provides a group mass of M = (3 − 10) × 1013 M⊙, in good agreement with the previous estimate derived through weak lensing analysis. Finally, by inverting the HST/ACS I814 image with the lensing equation, we obtain the reconstructed image of the magnified source galaxy, which has a scale of about 3.3 kpc at zs = 2 (2.7 kpc at zs = 4) and the typical disturbed disk-like appearance observed in low-mass star-forming galaxies at z ∼ 3. However, deep, spatially resolved spectroscopic data for similar lensed sources are still required to detect the first stage of galaxy evolution, since the available spectrum shows no clear features due to the background source.
Frequency Information-Based Observer Design for Takagi–Sugeno Fuzzy Affine Systems

This brief studies the problem of observer design for discrete-time uncertain Takagi-Sugeno (T-S) fuzzy affine systems subject to external disturbances and measurement noises with known frequency ranges. A class of novel observers with more design parameters is proposed. Based on the premise variable space partition information, piecewise Lyapunov functions are constructed together with the frequency range information of disturbances and noises, less conservative asymptotic stability with finite frequency performance analysis results are obtained. Then, based on Projection lemma, a novel observer design approach is proposed to T-S fuzzy affine systems, which tends to be less conservative than the non-frequency information and/or common Lyapunov function based observer design ones. Finally, simulation results on a circuit system are given to demonstrate the effectiveness and advantages of the proposed method.
Differences and Similarities in Spontaneous Activity Between Animal Models of Cancer-Induced Pain and Neuropathic Pain

Background Clinical data on cancer-induced pain (CIP) demonstrate widespread changes in sensory function. It is characterized in humans not only by stimulus-invoked pain, but also by spontaneous pain. In our previous studies in an animal model of CIP, we observed changes in intrinsic membrane properties and excitability of dorsal root ganglion (DRG) sensory neurons corresponding to mechanical allodynia and hyperalgesia, of which abnormal activities of Aβ-fiber sensory neurons are consistent in a rat model of peripheral neuropathic pain (NEP). Objective To investigate whether there are related peripheral neural mechanisms between the CIP and NEP models of spontaneous pain, we compared the electrophysiological properties of DRG sensory neurons at 2–3 weeks after CIP and NEP model induction. Methods CIP models were induced with metastasis tumour-1 rat breast cancer cells implanted into the distal epiphysis of the femur. NEP models were induced with a polyethylene cuff implanted around the sciatic nerve. Spontaneous pain in animals is measured by spontaneous foot lifting (SFL). After measurement of SFL, the animals were prepared for electrophysiological recordings of spontaneous activity (SA) in DRG neurons in vivo. Results Our data showed that SFL and SA occurred in both models. The proportion of SFL and SA of C-fiber sensory neurons in CIP was more significantly increased than in NEP models. There was no difference in duration of SFL and the rate of SA between the two models. The duration of SFL is related to the rate of SA in C-fiber in both models. Conclusion Thus, SFL may result from SA activity in C-fiber neurons in CIP and NEP rats. The differences and similarities in spontaneous pain between CIP and NEP rats is related to the proportion and rate of SA in C-fibers, respectively.
Linear response to long wavelength fluctuations using curvature simulations

We study the local response to long wavelength fluctuations in cosmological N-body simulations, focusing on the matter and halo power spectra, halo abundance and non-linear transformations of the density field. The long wavelength mode is implemented using an effective curved cosmology and a mapping of time and distances. The method provides an alternative, more direct, way to measure the isotropic halo biases. Limiting ourselves to the linear case, we find generally good agreement between the biases obtained from the curvature method and the traditional power spectrum method at the level of a few percent. We also study the response of halo counts to changes in the variance of the field and find that the slope of the relation between the responses to density and variance differs from the naïve derivation assuming a universal mass function by approximately 8–20%. This has implications for measurements of the amplitude of local non-Gaussianity using scale dependent bias. We also analyze the halo power spectrum and halo-dark matter cross-spectrum response to long wavelength fluctuations and derive second order halo bias from it, as well as the super-sample variance contribution to the galaxy power spectrum covariance matrix.
An Analysis of Alopecia Areata Classification Framework for Human Hair Loss Based on VGG-SVM Approach

Artificial intelligence approach is used in this article to make a diagnosis of hair loss. An autoimmune condition known as alopecia areata (AA) results in hair loss in the affected area. The most recent figures show that AA has an incidence of 2% and a frequency of 1 in 1000 people worldwide. For instance, classification is important in the field of medicine because one of the doctor's main objectives is to establish whether or not a patient has a condition. The identification of alopecia areata may be helpful for better prediction and diagnosis. Machine learning (ML) techniques have shown potential in a variety of domains, including dermatology. The dynamic character of illness symptoms is another important factor in the precise diagnosis of a particular disease. After then, as was already mentioned, linked work in the fields of ML and classification of healthy hair & hair disorders. The purpose of this research is to evaluate the efficacy of neural networks in recognising alopecia and non-humans. Healthy hair (HHs) and alopecia areata (AA) have been classified using a framework that will be susceptible to IP, including CLAHE enhancement and segmentation. Then, to increase the precision of the proposed framework, data augmentation (DA) was employed to generate further data. The VGG-19 pre-trained CNN model was then used to extract features. The Support Vector Machine (SVM) classification approach is then used to create an ML model utilising 70% of the images. The remaining images in the collection were used for testing. The suggested VGG-SVM was demonstrated to be 98.31% accurate in the simulation, which used 200 images of HH from the Figaro1k dataset and 68 images of AA from the Dermnet dataset. The proposed VGG-SVM outperformed the current Edge-SVM in terms of accuracy. The findings of our investigation point to the potential for enhanced dermatological prediction.
SEARCHING FOR GALACTIC SOURCES IN THE SWIFT GRB CATALOG

Context. Since the early 1990s, gamma ray bursts (GRB) have been accepted to be of extra-Galactic origin because of the isotropic distribution observed by BATSE and the redshifts observed via absorption line spectroscopy. Nevertheless, upon closer examination at least one case turned out to be of Galactic origin. This particular event presented a fast rise and exponential decay (FRED) structure, which leads us to believe that other FRED sources might also be Galactic. Aims. This study was set out to estimate the most probable degree of contamination by Galactic sources that certain samples of FREDs have. Methods. To quantify the degree of anisotropy, the average dipolar and quadripolar moments of each sample of GRBs with respect to the Galactic plane were calculated. This was then compared to the probability distribution of simulated samples comprising a combination of isotropically generated sources and Galactic sources. Results. We observe that the dipolar and quadripolar moments of the selected subsamples of FREDs are found more than two standard deviations outside those of random isotropically generated samples. The most probable degree of contamination by Galactic sources for the FRED GRBs of the Swift catalog detected until February 2011 that do not have a known redshift is about 21 out of 77 sources, which represents roughly 27%. Furthermore, we observe that by removing from this sample those bursts that have any type of indirect redshift indicator and multiple peaks, the most probable contamination increases to 34% (17 out of 49 sources). Conclusions. It is probable that a high degree of contamination by Galactic sources occurs among the single-peak FREDs observed by Swift. Accordingly, we encourage additional studies on these types of events to determine the nature of what could be an exotic type of Galactic source.
Improved Protocols for Distributed Secret Sharing

In Distributed Secret Sharing schemes, secrets are encoded with shares distributed over multiple nodes of a network. Each involved party has access to a subset of the nodes and thus to a subset of the shares and is able to reconstruct a specific secret. Usually, these schemes are evaluated by measuring the required storage overhead, as well as the encoding and decoding complexities. In this paper, we provide new Distributed (multi) Secret Sharing Protocols for <inline-formula><tex-math notation="LaTeX">$(k,n)$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>(</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="palmieri-ieq1-3213790.gif"/></alternatives></inline-formula>-threshold access structures that improve on previous results, characterized by nearly-optimal storage overhead, achieving both storage optimality and a better encoding/decoding complexity. The protocols are also simpler than previous ones and allow for easier encoding.
German financial state aid during Covid-19 pandemic: Higher impact among digitalized self-employed

ABSTRACT In response to strong revenue and income losses facing a large share of self-employed individuals during the COVID-19 pandemic, the German federal government introduced a €50bn emergency-aid program. Based on real-time online-survey data comprising more than 20,000 observations, we analyze the impact of this program on the confidence to survive the crisis. We investigate how the digitalization level of self-employed individuals influences the program’s effectiveness. Employing propensity score matching, we find that the emergency-aid program had only moderately positive effects on the confidence of self-employed to survive the crisis. However, self-employed whose businesses were highly digitalized, benefitted much more from the state aid than those whose businesses were less digitalized. This only holds true for those self-employed, who started the digitalization processes already before the crisis. Taking a regional perspective, we find suggestive evidence that the quality of the regional broadband infrastructure matters in the sense that it increases the effectiveness of the emergency-aid program. Our findings show the interplay between governmental support programs, the digitalization levels of entrepreneurs, and the regional digital infrastructure. The study helps public policy to improve the impact of crisis-related policy instruments, ultimately increasing the resilience of small firms in times of crises.
Impact of the Non-Uniform Catalyst Particle Size on Product Selectivities in Consecutive Reactions

The analysis of consecutive reactions A→B→C in porous catalyst particles, where the simultaneous processes of diffusion and chemical reactions take place and both reactant and products are subjected to diffusion limitations, was performed for catalyst particles with non-uniform sizes, a fact that has not been considered so far. The system comprises first-order consecutive irreversible reactions that proceed on spherical catalyst particles with a log-normal volume particle size distribution (PSD), which is typical in many catalytic applications. Regardless of the prevailing diffusion regime (chemical control, transition situation or intraparticle diffusion control), the yield of the intermediate product (B) reaches a maximum value as a function of the conversion of reactant (A), then decreases as a consequence of the prevalence of the secondary reaction that converts it into the secondary product (C). If intraparticle diffusion resistances affect the reactant species, given the relationship between the kinetic constants and the mean particle size, the selectivity to the intermediate product is negatively affected by the dispersion in PSD. The larger the dispersion in PSD, the stronger the negative impact.
Noise Theory and Application to Physics: From Fluctuations to Information

1. The Binomial No-Arbitrage Pricing Model 1.1. One-Period Binomial Model 1.2. Multiperiod Binomial Model 1.3. Computational Considerations 1.4. Summary 1.5. Notes 1.6. Exercises 2. Probability Theory on Coin Toss Space 2.1. Finite Probability Spaces 2.2. Random Variables, Distributions, and Expectations 2.3. Conditional Expectations 2.4. Martingales 2.5. Markov Processes 2.6. Summary 2.7. Notes 2.8. Exercises 3. State Prices 3.1. Change of Measure 3.2. Radon-Nikod\'ym Derivative Process 3.3. Capital Asset Pricing Model 3.4. Summary 3.5. Notes 3.6. Exercises 4. American Derivative Securities 4.1. Introduction 4.2. Non-Path-Dependent American Derivatives 4.3. Stopping Times 4.4. General American Derivatives 4.5. American Call Options 4.6. Summary 4.7. Notes 4.8. Exercises 5. Random Walk 5.1. Introduction 5.2. First Passage Times 5.3. Reflection Principle 5.4. Perpetual American Put: An Example 5.5. Summary 5.6. Notes 5.7. Exercises 6. Interest-Rate-Dependent Assets 6.1. Introduction 6.2. Binomial Model for Interest Rates 6.3. Fixed-Income Derivatives 6.4. Forward Measures 6.5. Futures 6.6. Summary 6.7. Notes 6.8. Exercises Proof of Fundamental Properties of Conditional Expectations References Index
OLD NEUTRON STARS AS PROBES OF ISOSPIN-VIOLATING DARK MATTER

Isospin-violating dark matter (IVDM), which couples differently with protons and neutrons, provides a promising mechanism to ameliorate the tension among recent direct detection experiments. Assuming dark matter (DM) is non-interacting bosonic asymmetric IVDM, we investigate how the existence of old neutron stars limits the DM–proton scattering cross-section &sgr; p ? > , especially the effects of the isospin-violating DM–nucleon interactions and the symmetry energy in the equation of state (EOS) of isospin asymmetric nuclear matter. Our calculations are completely based on general relativity and the structure of neutron stars is obtained by solving the Tolman–Oppenheimer–Volkoff equations with nuclear matter EOS constrained by terrestrial experiments. We find that, by considering the more realistic neutron star model rather than a simple uniform neutron sphere as usual, the &sgr; p ? > bounds from old neutron stars can be varied by more than an order of magnitude depending on the specific values of the DM neutron-to-proton coupling ratio f n / f p ? > , and they can be further varied by more than a factor of two depending on the density dependence of the symmetry energy. In particular, we demonstrate that the observed nearby isolated old neutron star PSR B1257+12 can set a very strong limit on &sgr; p ? > for low-mass DM particles ( ≤ 20 GeV ? > ) that reaches a sensitivity beyond the current best limits from direct detection experiments and disfavors the DM interpretation of previously reported positive experimental results, including the IVDM.
Results and Prospects from T2K

As measurements of the neutrino oscillation parameters improve it is becoming more interesting to study antineutrino oscillations, to investigate CP and CPT violation in the lepton sector and nonstandard matter effects. We present the most recent T2K antineutrino oscillation results, from data collected using a muon antineutrino-enhanced neutrino beam corresponding to 4.01E20 protons on target. The first analysis of electron antineutrino appearance at T2K is presented, as well as world-leading measurements of the dominant oscillation parameters for muon antineutrino disappearance. T2K measures $\sin^2(\bar{\theta}_{23}) = 0.46^{+0.14}_{-0.06}$ and $\Delta \bar{m}^2_{32} = 2.50^{+0.3}_{-0.2} \times 10^{-3} \mbox{eV}^2$, which is consistent with previous T2K measurements of the neutrino oscillation parameters and existing antineutrino measurements.
Social media influencers’ community and content strategy and follower engagement behavior in the presence of competition: an Instagram-based investigation

Purpose Drawing on insights from social influence theory, the MAIN model of digital media affordances and the literature on the attention economy, this study aims to investigate how social media influencers’ community and content strategy contribute to follower engagement behavior in the presence of competition. Design/methodology/approach This study used a web-scraped data set of Instagram influencers. It measured community strategy using influencers’ number of followers, number of following and breadth of interest; content strategy using modality type and number of posts; competition using number of influencers operating in the same primary domain of interest; and follower engagement behavior using number of likes and comments. A negative binomial regression model was estimated to test the proposed hypotheses. Findings The findings reveal that elements of influencers’ community and content strategies, such as number of followers, modality type and number of posts, influence follower engagement behavior. Similarly, competition significantly influences follower engagement behavior both independently and by interacting with influencers’ community and content strategy. Practical implications The findings offer insight for brands to identify suitable influencers for partnerships. For instance, brands can judge influencers’ suitability for partnership based on how many followers they have, how many posts they share and how many competitors they have. Further, the findings offer insight for influencers on how they can drive follower engagement behavior by managing their social media community and content. Originality/value This study develops an integrated model of factors that determine follower engagement behavior for social media influencers. The findings emphasize influencers’ strategy as the primary driver of follower engagement behavior. Extant studies focus on followers’ motivation and perception to explain follower engagement behavior while the role of influencers’ strategy is underplayed.