Mean Analysis Research Articles

The angular correlation function as measured by the GLEAM-X survey

Abstract The angular correlation is a method for measuring the distribution of structure in the Universe, through the statistical properties of the angular distribution of galaxies on the sky. We measure the angular correlation of galaxies from the second data release of the GaLactic and Extragalactic All-sky Murchison Widefield Array eXtended survey (GLEAM-X) survey, a low-frequency radio survey covering declinations below $+30^\circ$ . We find an angular distribution consistent with the $\Lambda$ CDM cosmological model assuming the best fitting cosmological parameters from Planck Collaboration et al. (2020, A&A, 641, A6). We fit a bias function to the discrete tracers of the underlying matter distribution, finding a bias that evolves with redshift in either a linear or exponential fashion to be a better fit to the data than a constant bias. We perform a covariance analysis to obtain an estimation of the properties of the errors, by analytic, jackknife, and sample variance means. Our results are consistent with previous studies on the topic, and also the predictions of the $\Lambda$ CDM cosmological model.

Universal scaling relations for growth phenomena

The Family–Vicsek (FV) relation is a seminal universal relation obtained for the global roughness at the interface of two media in the growth process. In this work, we revisit the scaling analysis and, through both analytical and computational means, show that the FV relation can be generalized to a new scaling independent of the size, substrate dimension d, and scaling exponents. We use the properties of lattice growth models in the Kardar–Parisi–Zhang and Villain–Lai–Das Sarma universality classes for 1⩽d⩽3 to support our claims.

Enhancing Adrenaline Sensing with Lanthanum Cuprate: A Promising Approach for a Novel Sensor

Electrochemical sensing is an excellent analytical means with multiple applications. Most of the electrochemical developments are based on the novel electrochemically active substrates. A lot of them have been adopted from other fields of material science. Every new class of functional materials has impacted on the development of electroanalytical chemistry. In this view, lanthanide cuprates, a well recognized high temperature superconducting materials were considered for adrenaline sensing. Obtained from corresponding oxides, a highly crystalline La2CuO4 found to be a suitable substrate for analytical method development. It inherited sponge-like morphology common for the most of the previously reported lanthanum cuprates with narrow pore size distribution at 400–700 nm. Excellent charge mobility and low resistivity provided a carbon paste electrode with improved linearity range and low limit of detection. Repeatability and stability of the developed sensor were at satisfactory level. Sensor functionality was verified on the real samples with excellent recovery rates. With this work we suggest further development of electroanalytical methods with application of lanthanide cuprates.

Analytical and Finite-Element-Method-Based Analyses of Pile Shaft Capacity Subjected to Rainfall Infiltration

The presence of unsaturated soil is critical in geotechnical engineering since the matric suction may aid in accommodating the pile shaft capacity. The design of piles can be optimized by incorporating unsaturated soil mechanics principles. Hence, the amount of waste materials can be reduced, the duration of pile installation can be expedited, and the amount of energy used for casting the pile can be optimized, resulting in more sustainable design and construction of piles. Conventional α, β, and λ methods and modified α, β, and λ methods are the common models that are used for calculating the shaft capacity by incorporating soil–water characteristic curves (SWCCs). However, in our opinion, we feel that the investigation of the influence of seepage infiltration due to rainfall on the shaft capacity of piles, calculated using both analytical means and numerical analysis, has been dealt with inadequately in past studies. The objective of this study is to investigate changes in the shaft pile capacity according to suction changes due to rainwater infiltration for the greater reliability of the pile design, using both analytical and numerical studies with the finite element method (FEM). Sand and kaolin, which are typical components of coarse-grained and fine-grained soil, are used in this study. The laboratory results were incorporated into PLAXIS 3D (Version 22), and a coupled analysis was carried out, utilizing the meteorological conditions in Astana. The results showed that the decreases in matric suction in sand and kaolin are similar after their subjection to rainfall, yet sand produces a higher shaft capacity compared to kaolin. The modified β method offers a higher shaft capacity compared to the other methods due to the effective stress factors being taken into account. The modified α and λ methods are recommended for short piles because they are more sustainable, whilst the modified β method is preferable for long piles. Overall, unsaturated soil conditions should be applied to optimize the foundation design since they generate a higher shaft capacity.

Periodic and aperiodic Ti lattices additively manufactured under reactive atmosphere: Mechanical and biological properties

Porous Ti materials can reduce the so-called stress shielding effect and are widely regarded as superior biomedical materials. In this study, commercially-pure Ti (CP-Ti) with five different porous structures, namely Dodecahedron, Diamond, Gyroid, Fischer Koch S, and Voronoi, have been printed using Powder Bed Fusion Laser Beam (PBF-LM) and under a reactive atmosphere (97%Ar+3%N2). Compressive tests and in vitro bio tests including osteogenic mineralization and cytotoxicity have been conducted to evaluate their mechanical and biomedical performances. Analytical means such as laser confocal microscopy have been used and radar properties maps have been built to compare overall properties of these different lattices. Results show that some of the porous CP-Ti lattices (with Gyroid and Fischer Koch S arrangement) can achieve engineering fracture strength above 1000 MPa, while maintaining their low Young’s modulus (<20 GPa) and good biocompatibility. For the lattices, relationships between Young’s modulus and relative density are discussed under the scheme of the Gibson and Ashby model (G-A model) with C constant as 0.1–4. Energy absorption data for the lattices are provided, showing high values due to the excellent combination of strength and ductility. The study implies that a good selection of the lattice type together with the approach of printing under reactive atmosphere can lead to advanced Ti lattices for biomedical applications.

Coexistence of Competing Microbial Strains under Twofold Environmental Variability and Demographic Fluctuations

Microbial populations generally evolve in volatile environments, under conditions fluctuating between harsh and mild, e.g. as the result of sudden changes in toxin concentration or nutrient abundance. Environmental variability (EV) thus shapes the long-time population dynamics, notably by influencing the ability of different strains of microorganisms to coexist. Inspired by the evolution of antimicrobial resistance, we study the dynamics of a community consisting of two competing strains subject to twofold EV. The level of toxin varies in time, favouring the growth of one strain under low drug concentration and the other strain when the toxin level is high. We also model time-changing resource abundance by a randomly switching carrying capacity that drives the fluctuating size of the community. While one strain dominates in a static environment, we show that species coexistence is possible in the presence of EV. By computational and analytical means, we determine the environmental conditions under which long-lived coexistence is possible and when it is almost certain. Notably, we study the circumstances under which environmental and demographic fluctuations promote, or hinder, the strains coexistence. We also determine how the make-up of the coexistence phase and the average abundance of each strain depend on the EV.

Dynamical Analysis of a 3D Fractional-Order Chaotic System for High-Security Communication and its Electronic Circuit Implementation

This article, a 3D fractional-order chaotic system (FOCS) is designed; system holds Equilibria can take on various shapes and forms by introducing a nonlinear function and the value of its parameters. To comprehend the system’s behavior under diverse conditions and parameter values, a dynamical analysis is conducted through analytical and numerical means. This analysis employs techniques like phase portraits, Lyapunov exponents (LEs), bifurcation analysis, and Lyapunov spectra. The system demonstrates attractors that are more intricate compared to a regular chaotic system with an integer value, specifically if we set the fractional order q to 0.97. This characteristic makes it highly appropriate for developing secure communication systems. Moreover, a practical implementation has been developed using an electronic circuit to showcase its feasibility of the system. A secure communication system was built using two levels of encryption techniques. The propose sound encryption algorithm is verified through tests like histogram, correlation, and spectrogram investigation. The encryption correlation coefficient between the original signal and the encrypted one is 0.0010, this result shows a strong defences against pirate attacks.

Output-only modal identification with recursive dynamic mode decomposition for time-varying systems

The time-varying modal identification is a prominent analytical means but challenging task to grasp the dynamic characteristics of time-varying systems, which is important guarantee for real-time control and on-line health monitoring of many engineering structures. In this paper, a data-driven approach for time-varying modal identification without requiring parametric modeling about underlying system is presented. Firstly, the fundamental theory for Dynamic Mode Decomposition (DMD) is reviewed combining the Koopman operator. Subsequently, the DMD incorporated into operational modal identification is examined from the perspective of linear system, and the inner connection between modal parameters of vibration system and eigenvalues and eigenmodes of DMD is established. On the basis of that, a weighted recursive strategy integrated with DMD is employed to investigate the capability of time-varying modal identification, and the main identification procedures are elaborated. Finally, the effectiveness and practicability of the proposed method in extracting modal properties of time-varying systems are investigated by a numerical and experiment example. The results demonstrate that, compared to other methods, the proposed approach has high computational efficiency while ensuring the identification accuracy, exhibiting a good engineering application prospect.

Cybersecurity in AI-Driven Casual Network Formation

The paper describes a methodology for forming thematic causal networks using artificial intelligence and automating the processes of their visualization. The presented methodology is considered on the example of ChatGPT, as an artificial intelligence for analyzing the space of texts and building concepts of causal relationships, and their further visualization is demonstrated on the example of Gephi and CSV2Graph programs. The effectiveness of the disaggregated method in relation to traditional methods for solving such problems is shown by integrating the means of intelligent text analytics and graphical network analysis on the example of the problem of data leakage in information systems and a selection of news clippings on the selected topic.

Experimental investigation and multi-performance optimization of the leachate recirculation based sustainable landfills using Taguchi approach and an integrated MCDM method

Landfill leachates contain harmful substances viz. chemicals, heavy metals, and pathogens, that pose a threat to human health and the environment. Unattended leachate can also cause ground water contamination, soil pollution and air pollution. This study focuses on management of leachate, by recirculating the rich, nutrient-filled fluid back into the landfills, turning it to a bioreactor, thereby maximising the performance parameters of landfills favourable for electricity production by the waste to energy plants. This study demonstrates a sustainable alternative method for utilising the fluid, rather than treating it using an extremely expensive treatment process. Further, it also experimentally investigates the effect of varying levels of five input parameters of the landfill including waste particle size, waste addition, inorganic content in waste, leachate recirculation rate, and landfill age, each at five levels, on the multiple performance of the landfill using Taguchi’s L25 standard orthogonal array. Experimental results are analysed using an integrated MCDM approach i.e. MEREC-PIV method and statistical techniques such as analysis of mean (ANOM) and analysis of variance (ANOVA). The results indicate that the optimal setting of the input parameters is waste particle size at 9 ppm, waste addition at 80 Ktoe, inorganic content in waste at 2%, leachate recirculation rate at 250 l/day and landfill age at 3 years. Further, inorganic content waste is found to be the most significant parameter for the multiple performance of the landfill. This study presents a novel approach to produce input parameters for power plants which may enhance their profitability and sustainability.

Physical informed neural network for thermo-hydral analysis of fire-loaded concrete

In the event of a fire within a tunnel, the rapid and substantial increase in temperature can prompt swift fractures within the concrete lining. This situation can severely compromise the structural load-bearing capacity and overall safety of the tunnel. The intricate interplay of factors within the tunnel, including temperature, humidity, and pore pressure, necessitates the adoption of a thermo-hydro coupled model for comprehensive analysis. However, these highly coupled models exhibit profoundly nonlinear characteristics that cannot be readily solved through analytical means. In this study, a Physics-informed neural network (PINN) is harnessed to address this intricate multi-field coupled issue. Neural networks possess a significant advantage in their capacity to autonomously differentiate and directly capture variations within the spatiotemporal domain. Through a comparison with experimental results, the proposed method’s reliability is effectively demonstrated. The research findings hold the potential to significantly aid engineering professionals in swiftly conducting fire resistance assessments and risk evaluations for tunnels, whether they are under construction or already in operation.

Coupled environmental and demographic fluctuations shape the evolution of cooperative antimicrobial resistance.

There is a pressing need to better understand how microbial populations respond to antimicrobial drugs, and to find mechanisms to possibly eradicate antimicrobial-resistant cells. The inactivation of antimicrobials by resistant microbes can often be viewed as a cooperative behaviour leading to the coexistence of resistant and sensitive cells in large populations and static environments. This picture is, however, greatly altered by the fluctuations arising in volatile environments, in which microbial communities commonly evolve. Here, we study the eco-evolutionary dynamics of a population consisting of an antimicrobial-resistant strain and microbes sensitive to antimicrobial drugs in a time-fluctuating environment, modelled by a carrying capacity randomly switching between states of abundance and scarcity. We assume that antimicrobial resistance (AMR) is a shared public good when the number of resistant cells exceeds a certain threshold. Eco-evolutionary dynamics is thus characterised by demographic noise (birth and death events) coupled to environmental fluctuations which can cause population bottlenecks. By combining analytical and computational means, we determine the environmental conditions for the long-lived coexistence and fixation of both strains, and characterise a fluctuation-driven AMR eradication mechanism, where resistant microbes experience bottlenecks leading to extinction. We also discuss the possible applications of our findings to laboratory-controlled experiments.

SYNTACTIC-SEMANTIC REALIZATION OF CAUSATIVE STRUCTURES IN ENGLISH AND KARAKALPAK LANGUAGES

The article deals with the syntactic-semantic expression of causative structures in English and Karakalpak languages. Causative structures in comparatively studied languages have their own characteristics, and the semantics of causative verbs in English and Karakalpak languages are presented in a certain classification. In the Karakalpak language, causation is formed under the influence of the lexical meaning of the verb, certain morphological forms and context while in English it is formed by analytical and lexical means. Moreover, it is represented that if the causative structure is used together with the main predicate, it acts as a complex participle.

NOMINATIVE SPACE OF THE CONCEPT PARENTS IN HUTSUL LINGUISTIC WORLD-IMAGE

It has been acknowledged that against the background of unquenchable interest to the problem of speakers’ spiritual universe reconstruction the study of concepts remains one of the necessary and urgent tasks of modern linguistic research. The purpose of this study is to analyze the ways of linguistic implementation of the concept of parents in the local Hutsul world-image. The subject area is the concept of parents. The specific topic is the verbal ways of the analyzed concept representing in the Hutsul dialects. Achieving the set goal determined the choice of appropriate research methodology, especially: the continuous sampling method, which is aimed at isolating the linguistic representatives of the concept from dialect dictionaries and texts for the formation of actual research material; contextual analysis, which made it possible to find out the semantics of the conceptual verbalizers; modeling method that helped to establish the field structure of the nominative concept space. Advances in research is determined by the fact that it is the first time attempts to analyze the ways of linguistic objectification of the concept of parents in the dialectal scope. A recognized theoretical value and practical value is represented in drawing the scientists’ attention to dialect conceptology as an interesting and promising direction. The conclusion and potential future directions of research. Nominative space of the concept of parents is heterogeneous in the Hutsul world-image. It is formed by simple (synthetic) linguistic means, expressed by separate words, and complex (analytical) means represented by a whole compound. The potential future directions of research is connected with the analysis of the nominative space of the concept of parents in the Middle-upper-Dnieper dialects.

What Do We Call an ‘MP’? On Categories of Thought in the Anthropology of Parliaments

Abstract This article questions what social scientists mean by the term ‘Member of Parliament’ (‘MP’) and explains that they tend to associate it with a universal conception of parliamentary representation. The author starts to present the rationale behind this presupposition, which lies in the resistance of legislative studies to interpretive approaches, and shows its limits. He then defends the necessity of extensive scientific knowledge of deliberative assemblies to distinguish the vernacular meanings of the notion of ‘MP’ from its analytical meaning. Finally, he takes the example of his own research in France to show the concrete difficulties of a singular conception of parliamentary representation and to expose the virtues of using empirical-based ideal types to overcome them.

Analytic odd mean labeling of union and identification of some graphs

A graph G is analytic odd mean if there exist an injective function f : V → {0, 1, 3, . . . , 2q − 1} with an induced edge labeling f∗ : E → Z such that for each edge uv with f(u) &lt; f(v), is injective. Clearly the values of f∗ are odd. We say that f is an analytic odd mean labeling of G. In this paper, we show that the union and identification of some graphs admit analytic odd mean labeling by using the operation of joining of two graphs by an edge.

Hourglass, a rapid analysis framework for heterogeneous bioimaging data, identifies sex disparity in IL-6/STAT3-associated immune phenotypes in pancreatic cancer.

Integration and mining of bioimaging data remains a challenge and lags behind the rapidly expanding digital pathology field. We introduce Hourglass, an open-access analytical framework that streamlines biology-driven visualization, interrogation, and statistical assessment of multiparametric datasets. Cognizant of tissue and clinical heterogeneity, Hourglass systematically organizes observations across spatial and global levels and within patient subgroups. Applied to an extensive bioimaging dataset, Hourglass promptly consolidated a breadth of known interleukin-6 (IL-6) functions via its downstream effector STAT3 and uncovered a so-far unknown sexual dimorphism in the IL-6/STAT3-linked intratumoral T-cell response in human pancreatic cancer. As an R package and cross-platform application, Hourglass facilitates knowledge extraction from multi-layered bioimaging datasets for users with or without computational proficiency and provides unique and widely accessible analytical means to harness insights hidden within heterogeneous tissues at the sample and patient level. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

The application of blockchain and robo-advisors in wealth management literature review

This paper aims to study the blockchain in the field of financial ecology as the carrier, optimize the consensus mechanism, and use intelligent consulting as an analytical means to provide investors with an objective, low-cost asset allocation portfolio. This article begins with an introduction to the features of blockchain decentralization and tamper-proof execution of algorithms, how proof-of-work works, and how tokens can improve welfare and reduce user base volatility. The paper then introduces how robo-advisors work and how they develop. Finally, this paper reviews existing research models on robo-advisors, from the traditional mean-variance model based on Markowitz to the jump-diffusion, regime-switching model, and the Pi portfolio management model that does not require quantifying risk preference coefficients, which this paper discusses and seeks to explore the advantages and limitations between the different models. Based on the existing research gaps, the directions that digital finance can expand in the future are discussed.

Exact Dirac-Bogoliubov-de Gennes Dynamics for Inhomogeneous Quantum Liquids.

We study inhomogeneous 1+1-dimensional quantum many-body systems described by Tomonaga-Luttinger-liquid theory with general propagation velocity and Luttinger parameter varying smoothly in space, equivalent to an inhomogeneous compactification radius for free boson conformal field theory. This model appears prominently in low-energy descriptions, including for trapped ultracold atoms, while here we present an application to quantum Hall edges with inhomogeneous interactions. The dynamics is shown to be governed by a pair of coupled continuity equations identical to inhomogeneous Dirac-Bogoliubov-de Gennes equations with a local gap and solved by analytical means. We obtain their exact Green's functions and scattering matrix using a Magnus expansion, which generalize previous results for conformal interfaces and quantum wires coupled to leads. Our results explicitly describe the late-time evolution following quantum quenches, including inhomogeneous interaction quenches, and Andreev reflections between coupled quantum Hall edges, revealing remarkably universal dependence on details at stationarity or at late times out of equilibrium.

Lp-Sampling recovery for non-compact subclasses of L∞

In this article, we study the sampling recovery problem for certain relevant multivariate function classes on the cube [0, 1]d, which are not compactly embedded into L∞([0,1]d). Recent tools relating the sampling widths to the Kolmogorov or best m-term trigonometric widths in the uniform norm are therefore not applicable. In a sense, we continue the research on the small smoothness problem by considering limiting smoothness in the context of Besov and Triebel-Lizorkin spaces with dominating mixed regularity such that the sampling recovery problem is still relevant. There is not much information available on the recovery of such functions except for a previous result by Oswald in the univariate case and Dinh Dũng in the multivariate case. As a first step, we prove the uniform boundedness of the ℓp-norm of the Faber coefficients at a fixed level by Fourier analytic means. Using this, we can control the error made by a (Smolyak) truncated Faber series in Lq([0,1]d) with q &amp;lt;∞. It turns out that the main rate of convergence is sharp. Thus, we obtain results also for S1,∞1F([0,1]d), a space “close” to S11W([0,1]d), which is important in numerical analysis, especially numerical integration, but has rather poor Fourier analytical properties.