Miracle-dz/UniFAR-FTUs huggingface dataset:
Spinodal decomposition (phase transitions via unstable states)

Considerable deviations from equilibrium conditions are observed for nonstatic, first-order phase transitions. In some cases, unstable (labile) phase states may precede the onset of the phase transition. The relaxation of the system is then accompanied by an enhancement of random inhomogeneities and the appearance of a modulated intermediate structure. This mechanism of the initial stage of a phase transition is called spinodal decomposition (SD). Theoretical and experimental studies of SD in two-component systems are reviewed in this paper. Thermodynamic stability and the possibility of SD in one-component liquid-vapor systems and an alternative nucleation mechanism are discussed. The phenomenological theory of SD is based on the Ginzburg-Landau expression for the free energy of an inhomogeneous system. A linearized diffusion equation is derived, for which thermodynamically unstable states have exponentially increasing solutions for the Fourier components of composition. Subsequent refinements of SD theory take into account thermal fluctuations and involve the derivation of the kinetic equation for the distribution functional. Diffraction methods are the most effective in the experimental study of SD in alloys, glasses, and binary liquid mixtures. So far, the agreement between theory and experiment must be regarded as only qualitative.
Functional Modelling and Classification of Longitudinal Data *

Abstract. We review and extend some statistical tools that have proved useful for analysing functional data. Functional data analysis primarily is designed for the analysis of random trajectories and infinite‐dimensional data, and there exists a need for the development of adequate statistical estimation and inference techniques. While this field is in flux, some methods have proven useful. These include warping methods, functional principal component analysis, and conditioning under Gaussian assumptions for the case of sparse data. The latter is a recent development that may provide a bridge between functional and more classical longitudinal data analysis. Besides presenting a brief review of functional principal components and functional regression, we develop some concepts for estimating functional principal component scores in the sparse situation. An extension of the so‐called generalized functional linear model to the case of sparse longitudinal predictors is proposed. This extension includes functional binary regression models for longitudinal data and is illustrated with data on primary biliary cirrhosis.
Dynamic connectedness among Pakistani stock markets and its major trading partners

PurposeInternationalization and financial deregulation have caused market participants and policymakers to consider the significance of financial connectedness and the spillover effects of shocks. In this context, this research is a pioneering effort to investigate the direction and magnitude of return volatility spillovers between Pakistan’s financial markets and those of its key trade partners. This paper examines the relationship between return and volatility spillover in the financial markets of Pakistan and its major trading partners.Design/methodology/approachTen countries are selected for empirical examination of dynamic connectedness among Pakistan and its major trading partner’s stock markets. This study utilizes a spillover index approach model and considers daily, weekly and monthly datasets spanning 25 years from 1995 to 2019.FindingsThe results indicate that stock markets provide efficient channels for return and volatility spillovers. Moreover, it is found that the intensity of spillovers during the financial crisis is more intense as these crises are major determinants of contagion; consequently, investors, speculators and policymakers use these events for their respective purposes.Originality/valueResearchers, practitioners, policymakers and investors may all benefit from the findings in areas including risk management, portfolio diversification and trading methods.
Aptamers as Novel Binding Molecules on an Antimicrobial Peptide-Armored Composite Hydrogel Wound Dressing for Specific Removal and Efficient Eradication of Pseudomonas aeruginosa

Here we present for the first time a potential wound dressing material implementing aptamers as binding entities to remove pathogenic cells from newly contaminated surfaces of wound matrix-mimicking collagen gels. The model pathogen in this study was the Gram-negative opportunistic bacterium Pseudomonas aeruginosa, which represents a considerable health threat in hospital environments as a cause of severe infections of burn or post-surgery wounds. A two-layered hydrogel composite material was constructed based on an established eight-membered focused anti-P. aeruginosa polyclonal aptamer library, which was chemically crosslinked to the material surface to form a trapping zone for efficient binding of the pathogen. A drug-loaded zone of the composite released the C14R antimicrobial peptide to deliver it directly to the bound pathogenic cells. We demonstrate that this material combining aptamer-mediated affinity and peptide-dependent pathogen eradication can quantitatively remove bacterial cells from the “wound” surface, and we show that the surface-trapped bacteria are completely killed. The drug delivery function of the composite thus represents an extra safeguarding property and thus probably one of the most important additional advances of a next-generation or smart wound dressing ensuring the complete removal and/or eradication of the pathogen of a freshly infected wound.
Kerr–Newman solution in modified teleparallel theory of gravity

A nondiagonal tetrad field having six unknown functions plus an angle Φ, which is a function of the radial coordinate r, azimuthal angle θ and the polar angle ϕ, is applied to the charged field equations of modified teleparallel theory of gravity. A special nonvacuum solution is derived with three constants of integration. The tetrad field of this solution is axially symmetric and its scalar torsion is constant. The associated metric of the derived solution gives Kerr–Newman spacetime. We have shown that the derived solution can be described by a local Lorentz transformations plus a diagonal tetrad field that is the square root of the Kerr–Newman metric. We show that any solution of general relativity (GR) can be a solution in f(T) under certain conditions.
A C18O Survey of Dense Cloud Cores in Taurus: Star Formation

This paper discusses star formation in the 40 C18O molecular cloud cores in Taurus by studying relations between the cores and young stellar objects (YSOs). These C18O cores constitute a complete sample of dense gas in Taurus as described in detail by Onishi and coworkers. The YSOs treated in the present work fall into the following four categories: (1) starless H13CO+ compact cores, (2) cold IRAS sources, (3) warm IRAS sources, and (4) T Tauri stars without far-infrared emission. Starless H13CO+ compact cores are assumed to be protostellar condensations that are in a phase just prior to star formation, although they are not yet considered to be YSOs in the usual sense. In the present study, we shall call the objects 1 and 2 cold objects and objects 3 and 4 warm objects. They constitute the most complete sample of YSOs, including compact dense molecular condensations, at present. It is found that C18O cores with cold objects have clearly higher molecular column densities than the rest. These C18O cores also tend to be massive and large in size. If we assume that the cold objects represent the protostellar condensations or protostars in the main accretion phase, the results imply that the actual process of star formation, i.e., collapse and significant mass accretion, takes place when a column density of the molecular cloud core becomes greater than a certain value. This threshold value for star formation is ~8.0 × 1021 cm-2 in average column density. On the other hand, the C18O cores without cold objects are characterized by a typical column density of 5.5 × 1021 cm-2, significantly smaller than the threshold value. The fact that all the C18O cores whose average column density is higher than 8.0 × 1021 cm-2 are associated with cold objects suggests that star formation takes place immediately and without exception when the column density satisfies the above value. Such C18O cores with cold objects typically have more than one YSO. The number of YSOs in a C18O core increases with core mass. The C18O core mass divided by the number of YSOs does not depend strongly on the C18O core mass and is nearly a constant mass of 11 M☉, i.e., the number of YSOs within a C18O core is proportional to the core mass. This suggests that formation of a protostellar condensation requires ~11 M☉ molecular gas of n(H)2 ~ 104 cm-3 indicating a rather uniform star formation efficiency (Mstar/Mgas) of ~6% (the mass of a YSO is taken to be 0.7 M☉) at a density of ~104 cm-3. The mean projected separation of the cold objects in a C18O core is estimated to be ~0.3 pc. This separation is roughly in a range explicable by existing theoretical studies of gravitational instability, while the core morphology is not yet exactly taken into account in these studies. The C18O data suggest that the amount of the surrounding gas decreases on a timescale of, ~5 × 105 yr or larger. Dissipation of surrounding gas occurs not only in the vicinity of a star but also in the scale of C18O cores, ≳0.1 pc. This dissipation cannot be explained simply by an outflow or mass accretion onto a central star.
Deadline Miss Early Detection Method for Mixed Timer-Driven and Event-Driven DAG Tasks

Self-driving systems have a hard real-time nature, and the control commands of the vehicle must be output before the end-to-end deadline using sufficiently new data. It must also have the functionality to quickly shift to a safe state in the event of a deadline miss. However, the current self-driving system can only detect a deadline miss at the end of the process. To further improve safety, a method for detecting the possibility of a deadline miss in the middle of the process is required. Therefore, we represent such a real-time system as a mixed timer-driven and event-driven directed acyclic graph (DAG) and propose an early detection method for deadline misses by deriving a time constraint for each node. The experimental evaluation shows that the proposed method can detect deadline misses early for various scheduling algorithms. It also shows that the deadline miss ratio can be reduced by prioritizing the scheduling of jobs with a small margin to the time constraint determined using the proposed method for each job.
Born scattering of long-period body waves

The Born approximation is applied to the modelling of the propagation of deeply turning long-period body waves through heterogeneities in the lowermost mantle. We use an exact Green's function for a spherically symmetric earth model that also satisfies the appropriate boundary conditions at internal boundaries and the surface of the earth. The scattered displacement field is obtained by a numerical quadrature of the product of the Green's function, the exciting wavefield and structural perturbations. We study three examples: scattering of long-period P waves from a plume rising from the core–mantle boundary (CMB), generation of long-period precursors to PKIKP by strong, localized scatterers at the CMB, and propagation of core-diffracted P waves through large-scale heterogeneities in D″. The main results are as follows: (1) the signals scattered from a realistic plume are small with relative amplitudes of less than 2 per cent at a period of 20 s, rendering plume detection a fairly difficult task; (2) strong heterogeneities at the CMB of appropriate size may produce observable long-period precursors to PKIKP in spite of the presence of a diffraction from the PKP-B caustic; (3) core-diffracted P waves (Pdiff) are sensitive to structure in D″ far off the geometrical ray path and also far beyond the entry and exit points of the ray into and out of D″; sensitivity kernels exhibit ring-shaped patterns of alternating sign reminiscent of Fresnel zones; (4) Pdiff also shows a non-negligible sensitivity to shear wave velocity in D″; (5) down to periods of 40 s, the Born approximation is sufficiently accurate to allow waveform modelling of Pdiff through large-scale heterogeneities in D″ of up to 5 per cent.
Atomic and Molecular Spectroscopy: Basic Aspects and Practical Applications

A wide-ranging review of modern techniques in atomic and molecular spectroscopy. A brief description of atomic and molecular structure is followed by the relevant energy structure expressions. A discussion of radiative properties and the origin of spectra leads into coverage of X-ray and photoelectron spectroscopy, optical spectroscopy, and radiofrequency and microwave techniques. The treatment of laser spectroscopy investigates various tunable sources and a wide range of techniques characterized by high sensitivity and high resolution. Throughout this book, the relation between fundamental and applied aspects is shown, in particular by descriptions of applications to chemical analysis, photochemistry, surface characterisation, environmental and medical diagnostics, remote sensing and astrophysics.
Quantitative Precipitation Forecast Techniques for Use in Hydrologic Forecasting

Abstract Quantitative hydrologic forecasting usually requires knowledge of the spatial and temporal distribution of precipitation. First, it is important to accurately measure the precipitation falling over a particular watershed of interest. Second, especially for small watersheds and/or for longer forecast lead times, forecasts of precipitation are critical to the achievement of the greatest possible hydrologic forecast accuracy and longest possible lead time. This paper describes the current hydrologic forecasting program of the U.S. National Weather Service (NWS) and highlights the relevance of Quantitative Precipitation Forecasting (QPF) products to real-time hydrologic forecasting. Specific requirements for QPF products in support of hydrologic forecasting applications are defined and current operational QPF procedures are reviewed to determine to what extent they meet these requirements. It is concluded that no known QPF procedures capable of fulfilling all desired requirements are currently availa.
CSGNN: Contamination Warning and Control of Food Quality via Contrastive Self-Supervised Learning-Based Graph Neural Network

Effective contamination warning and control of food quality can significantly reduce the likelihood of food quality safety incidents. Existing food contamination warning models for food quality rely on supervised learning, do not model the complex feature associations between detection samples, and do not consider the unevenness of detection data categories. In this paper, To overcome these limitations, we propose a Contrastive Self-supervised learning-based Graph Neural Network framework (CSGNN) for contamination warning of food quality. Specifically, we structure the graph for detecting correlations between samples and then define the positive and negative instance pairs for contrastive learning based on attribute networks. Further, we use a self-supervised approach to capture the complex relationships between detection samples. Finally, we assessed each sample’s contamination level based on the absolute value of the subtraction of the prediction scores from multiple rounds of positive and negative instances obtained by the CSGNN. Moreover, we conducted a sample study on a batch of dairy product detection data in a Chinese province. The experimental results show that CSGNN outperforms other baseline models in contamination assessment of food quality, with AUC and recall of unqualified samples reaching 0.9188 and 1.0000, respectively. Meanwhile, our framework provides interpretable contamination classification for food detection. This study provides an efficient early warning method with precise and hierarchical contamination classification for contamination warning of food quality work.
Theory of optical scattering by carbon nanotubes

Here, a theory of optical scattering by single‐wall carbon nanotube using modal expansion of the scattered and transmitted fields is developed. The effective‐boundary condition, derived previously using accurate quantum‐mechanical one‐electron tight‐biding approach, is employed to obtain the unknown coefficients in the series expansion. The complete scattered field is then derived analytically and compared to Rayleigh spectroscopy experiments. Very good agreement with the empirical data is observed. The theory is shown to produce expressions for the dielectric function of carbon nanotubes that can be used for further study of the optical and electronic structure. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 2360–2364, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22768
Machine Learning Security in Industry: A Quantitative Survey

Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study with 139 industrial practitioners. We analyze attack occurrence and concern and evaluate statistical hypotheses on factors influencing threat perception and exposure. Our results shed light on real-world attacks on deployed machine learning. On the organizational level, while we find no predictors for threat exposure in our sample, the amount of implement defenses depends on exposure to threats or expected likelihood to become a target. We also provide a detailed analysis of practitioners’ replies on the relevance of individual machine learning attacks, unveiling complex concerns like unreliable decision making, business information leakage, and bias introduction into models. Finally, we find that on the individual level, prior knowledge about machine learning security influences threat perception. Our work paves the way for more research about adversarial machine learning in practice, but yields also insights for regulation and auditing.
Temporal dynamics of the cecal and litter microbiome of chickens raised in two separate broiler houses

In this study, we investigated the dynamics of the ceca and litter microbiome of chickens from post-hatch through pre-harvest. To achieve this, six hundred one-day old Cobb 500 broiler chicks were raised on floor pens for 49 days in two separate houses. We performed short-read and full-length sequencing of the bacterial 16S rRNA gene present in the meconium and in cecal and litter samples collected over the duration of the study. In addition, we determined the antimicrobial resistance (AMR) phenotype of Escherichia coli and Enterococcus spp. isolated from the meconium and the ceca of 49-day old chickens. We monitored the relative humidity, temperature, and ammonia in each house daily and the pH and moisture of litter samples weekly. The overall microbial community structure of the ceca and litter consistently changed throughout the course of the grow-out and correlated with some of the environmental parameters measured (p < 0.05). We found that the ceca and litter microbiome were similar in the two houses at the beginning of the experiment, but over time, the microbial community separated and differed between the houses. When we compared the environmental parameters in the two houses, we found no significant differences in the first half of the growth cycle (day 0–21), but morning temperature, morning humidity, and ammonia significantly differed (p < 0.05) between the two houses from day 22–49. Lastly, the prevalence of AMR in cecal E. coli isolates differed from meconium isolates (p < 0.001), while the AMR phenotype of cecal Enterococcus isolates differed between houses (p < 0.05).
Performing Calculation in Interactive Theorem Proving

An LCF style tactic can be used to verify that the conclusion is a logical consequence of the premises, however, in mathematical practice, we often construct a conclusion in a proof step. This paper proposes a calculational style of tactic, and illustrates the characteristic and the realization issues of the tactics on the top of a theorem prover.
BD +05°706: A New Member of the Class of “Cool Algols”

We report the serendipitous discovery of an Algol-type binary with an orbital period of 18.9 days, in which both components are cool giants (G8 III and K1–K2 III). Few systems with these characteristics are known and are referred to as "cool Algols," as opposed to the "classical Algols" in which the mass-gaining component resembles an early-type main-sequence star. The nature of this object, which is a strong X-ray source, was discovered in the course of a spectroscopic search for T Tauri stars among ROSAT All-Sky Survey sources. We derive a double-lined spectroscopic orbit leading to minimum masses of MA = 2.481 ± 0.023 M☉ and MB = 0.515 ± 0.011 M☉ and a corresponding mass ratio of q ∼ 0.21. We measure also projected rotational velocities of 22 and 31 km s-1, respectively, and a luminosity ratio of lB/lA = 1.06 ± 0.04 at ∼5200 Å. The system is probably semidetached, with the axial spin of the components synchronized with the orbital motion, and the probability of eclipses is high. The general properties of BD +05°706, most likely a result of case B mass exchange, are very similar to those of the other known members of this class. We discuss the correlation between the mass of the hotter, detached components and their effective temperature for all cool Algols, noted previously by D. M. Popper, as well as a trend with the orbital period. Chromospheric activity indicators in BD +05°706 are compared with those of other members of its class, as well as with those of the classical Algols and the RS CVn binaries.
Advances in Research on the Regulation of Floral Development by CYC-like Genes

CYCLOIDEA (CYC)-like genes belong to the TCP transcription factor family and play important roles associated with flower development. The CYC-like genes in the CYC1, CYC2, and CYC3 clades resulted from gene duplication events. The CYC2 clade includes the largest number of members that are crucial regulators of floral symmetry. To date, studies on CYC-like genes have mainly focused on plants with actinomorphic and zygomorphic flowers, including Fabaceae, Asteraceae, Scrophulariaceae, and Gesneriaceae species and the effects of CYC-like gene duplication events and diverse spatiotemporal expression patterns on flower development. The CYC-like genes generally affect petal morphological characteristics and stamen development, as well as stem and leaf growth, flower differentiation and development, and branching in most angiosperms. As the relevant research scope has expanded, studies have increasingly focused on the molecular mechanisms regulating CYC-like genes with different functions related to flower development and the phylogenetic relationships among these genes. We summarize the status of research on the CYC-like genes in angiosperms, such as the limited research conducted on CYC1 and CYC3 clade members, the necessity to functionally characterize the CYC-like genes in more plant groups, the need for investigation of the regulatory elements upstream of CYC-like genes, and exploration of the phylogenetic relationships and expression of CYC-like genes with new techniques and methods. This review provides theoretical guidance and ideas for future research on CYC-like genes.