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The heat transfer in power‐law fluids across three corrugated circular cylinders placed in a triangular pitch arrangement is studied computationally in a confined channel. Continuity, momentum, and energy balance equations were solved using ANSYS FLUENT (Version 18.0). The flow is assumed to be steady, incompressible, two‐dimensional, and laminar. A square domain of side 300Dh is selected after a detailed domain study. An optimized grid with 98,187 cells is used in the study. The convergence criteria of 10−7 for the continuity, x‐momentum, and y‐momentum balances and 10−12 for the energy equation were used. Constant density and non‐Newtonian power‐law viscosity modules were used. The diffusive term is discretized using a central difference scheme. Convective terms are discretized using the Second‐Order Upwind scheme. Pressure–velocity coupling between continuity and momentum equations was implemented using the semi‐implicit method for pressure‐linked equation scheme. Streamlines show wake development behind the cylinders, which is very dominant at large ReN and n. Isotherm contours are cramped at higher values of ReN and PrN, implying higher heat transfer. Global parameters, like, Cd and Nu, are computed for the wide ranges of controlling dimensionless parameters, such as power‐law index (0.3 ≤ n ≤ 1.5), Reynolds (0.1 ≤ ReN ≤ 40), and Prandtl (0.72 ≤ PrN ≤ 500) numbers. The NuLocal plot attains a pitch near the corrugation of the surface due to abrupt changes in velocity and temperature gradients. Nu increases with ReN and/or PrN and decreases with n under ot herwise identical situations. Nu is correlated with pertinent parameters, namely, ReN, PrN, and n. [ABSTRACT FROM AUTHOR]

In this work, Gum ghatti-cl-poly(acrylic acid)/CoFe2O4 (GGAACF) hydrogels were synthesized using a free radical polymerization technique, with CoFe2O4 nanoparticles incorporated via a co-precipitation method using nitrates as precursors. Thermal gravimetric analysis (TGA) revealed that the inclusion of CoFe2O4 nanoparticles enhanced the thermal stability of the hydrogels. Swelling studies indicated that the addition of 30 mg of CoFe2O4 nanoparticles maximized water retention. Rheological assessments demonstrated non-Newtonian behavior, with flow curves fitted best by the Power Law model. The incorporation of CoFe2O4 nanoparticles significantly improved the hydrogel's elasticity and viscosity, as evidenced by a higher storage modulus (G′) compared to the loss modulus (G″) across all frequencies, indicating the elastic nature of the hydrogels. The decrease in complex viscosity with increasing frequency confirmed the pseudoplastic properties of the hydrogels, attributed to the random alignment of CoFe2O4 nanoparticles within the matrix. Tan δ values were below unity at all tested frequencies, underscoring the hydrogels' strong elastic properties. These findings highlight the effectiveness of rheological analysis in characterizing the viscoelastic behavior of polymer hydrogels, which can be tailored for various applications. [ABSTRACT FROM AUTHOR]

Many mathematical models have been proposed to estimate vertical tidal current profiles. However, as previous studies have shown that tidal current energy sites have different characteristics in their vertical tidal current profiles, it is necessary to estimate the profiles from field-measured data for practical purposes. In this study, we measured layered tidal currents over two months using an acoustic Doppler current profiler (ADCP) to analyze the characteristics of vertical tidal current profiles at the Jangjuk Strait, a candidate site for tidal current energy. As a result, the power law parameter α and bed roughness β were estimated as 4.51–12.41 and 0.38–0.42, respectively. Additionally, the maximum roughness length representing seabed roughness in the logarithmic profile was estimated as 0.221 m, and the estimated friction velocity was 0.038–0.194 m/s. Furthermore, a high correlation was observed between the depth-averaged tidal current velocity and friction velocities at all sites during flood and ebb tide conditions. A high correlation was also found between the bed roughness, roughness length, and power law exponent at relatively deeper sites. Tidal current energy sites display distinct characteristics compared to other sea areas. Therefore, it is essential to account for field conditions when conducting numerical modeling and design. [ABSTRACT FROM AUTHOR]

This investigation describes the computational characteristics of two-directional functionally graded beams under the hygrothermal effect. A prismatic two-directional functionally graded beam (FGM) is used in this work. Temperature and moisture are applied to this FGM beam. Stainless steel (SUS304) and silicon nitride (Si3N4) are the materials used in their construction. Finite element method (FEM) software calculates the eigenfrequencies of two-directional FGM (COMSOL Multiphysics). The power law governs the material properties. The Euler–Bernoulli beam theory (EBT) is used in this study. The mechanical parameters are varied in longitudinal and transverse directions. The eigenfrequencies of two-directional FGM beams are affected by the material gradient parameter, the boundary condition, and the aspect ratio under the influence of the hygrothermal effect. The results obtained for reduced cases are used to validate the results computed with the FEM. The natural frequencies decrease with increased moisture concentration and temperature variation under clamped–clamped (C–C) boundary conditions and increase under clamped-free (C–F) boundary conditions. It is also observed that natural frequencies increase with the increase in material gradient parameters in the z-direction (nz) and decrease with the increase in material gradient parameters in the x-direction (nx) under C–C and C–F end conditions. With the increase in aspect ratio, the natural frequency increases under C–C and C–F boundary conditions. [ABSTRACT FROM AUTHOR]

The common assumption is that genetic information is built on a four-symbol alphabet, i.e., DNA nucleotides, the smallest meaningful blocks of genomes are codon triplets, and the record of genetic information does not contain any asserted symbols playing the role of the space. It is, however, well known that some nucleotides in some codons are redundant. Our study, therefore, tests the alternative scenario. As the same nucleotide may play various semiotic roles, the genomic alphabet actually contains seven semiotic symbols. Consequently, the meaningful fragments of genomes (words) can be of different sizes, and there are asserted symbols in the record of genomic information. If this is true, then, similarly to natural languages, the frequency-range of these genomic words should follow the power-law distribution. The presented hypothesis was tested, in comparison to competitive (codon-based and n-tuple) forms of tokenization, on a wide range of genomic texts. [ABSTRACT FROM AUTHOR]

This work deals with applying the iteration method for the numerical solutions of a one-dimensional thermoelastic material. The governing equations have been constructed in the context of generalized thermoelasticity with one relaxation time model of Lord and Shulman (L-S). Some properties have been considered functional graded based on power law, and the lattice design parameter has been assumed. Adomian's decomposition method (ADM) and iteration techniques have been applied to solve the governing differential equations with an algorithm method. The numerical results have been represented in figures. The numerical results affirm that Adomian's decomposition method is a successful iteration method for solving and modeling thermoelastic problems. The lattice design parameter, empirical parameter, and ratio of the material properties significantly impact the temperature increment, strain, stress, displacement distributions, where they play vital roles in transferring the thermo-mechanical waves through the thermoelastic materials. [ABSTRACT FROM AUTHOR]

In this article a technique is described and the optical microstructure of the multiphase alloy BrAZh-9-4 is analyzed based on structurally ground copper. Features of the microstructure of this alloy are studied by assessing the spatial homogeneity of materials based on the geometric characteristics of phases and the structural components. To analyze the structural characteristics of the material, the following quantitative image characteristics were used: statistical separation of phases using clustering, phase identification based on the level of coloration of gray image pixels, determination of histograms of grain size distributions, and the total areas (lengths) of grain boundaries and phases. Based on the results of this study, a conclusion was made about the degree of refinement of the α and β phases, the enlargement of the γ phase due to the decomposition of the α and β phases during cooling of the alloy after ECAP treatment, the increase in the length of the grain boundaries of the material, and the uniformity of the distribution of phase grains over the microsection area. [ABSTRACT FROM AUTHOR]

Extrusion of viscous lava produces domes and spines, collapses of which pose a significant local hazard. Understanding extrusion processes and probabilistic hazard estimation require comprehensive datasets of geometric parameters. We introduce Morphology of Viscous Extrusions (MoVE), a collation of 323 observations of height and width from 80 extrusions at 46 volcanoes globally with compositions spanning basaltic through rhyolitic. Filtering this dataset for sample size, age, and time series overrepresentation reduces the composition range to basaltic-andesitic through dacitic, for which we do not identify a statistically significant effect of composition on extrusion geometry. Young (

Abstract Dispersion of Basil seed gum has high viscosity and exhibits shear-thinning behavior. This study aimed to analyze the influence of microwave treatment (MT) at various time intervals (0, 1, 2, and 3 min) on the viscosity and rheological behavior of Basil seed gum dispersion (0.5%, w/v). The finding of this study revealed that the apparent viscosity of Basil seed gum dispersion (non-treated dispersion) reduced from 0.330 Pa.s to 0.068 Pa.s as the shear rate (SR) increased from 12.2 s−1 to 171.2 s−1. Additionally, the apparent viscosity of the Basil seed gum dispersion reduced from 0.173 Pa.s to 0.100 Pa.s as the MT time increased from 0 to 3 min (SR = 61 s−1). The rheological properties of gum dispersion were successfully modeled using Power law (PL), Bingham, Herschel–Bulkley (HB), and Casson models, and the PL model was the best one for describing the behavior of Basil seed gum dispersion. The PL model showed an excellent performance with the maximum r-value (mean r-value = 0.942) and the minimum sum of squared error (SSE) values (mean SSE value = 5.265) and root mean square error (RMSE) values (mean RMSE value = 0.624) for all gum dispersion. MT had a considerable effect on the changes in the consistency coefficient (k-value) and flow behavior index (n-value) of Basil seed gum dispersion (p

Crude oil prices crashed and dropped into negative territory at the onset of the COVID-19 pandemic. This extreme event triggered a series of great-magnitude aftershocks. We seek to investigate the cascading dynamics and the characteristics of the series immediately following the oil market crash. Utilizing a robust method named the Omori law, we quantify the correlations of these events. This research presents empirical regularity concerning the number of times that the absolute value of the percentage change in the oil index exceeds a given threshold value. During the COVID-19 crisis, the West Texas Intermediate (WTI) oil prices exhibit greater volatility compared to the Brent oil prices, with higher relaxation values at all threshold levels. This indicates that larger aftershocks decay more rapidly, and the period of turbulence for the WTI is shorter than that of Brent and the stock market indices. We also demonstrate that the power law's exponent value increases with the threshold value's magnitude. By proposing this alternative method of modeling extreme events, we add to the current body of literature, and the findings demonstrate its practical use for decision-making authorities—particularly financial traders who model high-volatility products like derivatives. [ABSTRACT FROM AUTHOR]

Doping and morphology tailoring in metal sulfides up to nanoscale plays a vital role for better performance in various applications. Herein hydrothermally synthesized Co-doped nickel sulfide (NiS 2) nanostructures have been transformed from nano-rods to nano-sheets up to optimal doping concentration of 3 %. The phase changes, morphological modifications and elemental investigations have been analyzed by SEM, TEM, HRTEM, SAED, EDS, XRD and FTIR. The morphology transitions revealed that NiCo 3 S exhibited sophisticated morphology of flower like structures formed by nano-sheet patterns. NiCo 3 S electrode possesses an auspicious capacitance of 1242 Fg_1 at 1 Ag_1 within potential window range of 0.1–0.8 V. In accordance with GCD study, CV and EIS study confirmed that NiCo 3 S is more electroactive and less resistive showing excellent stability up to 92 % after 5000 cycles. The power law (b = 0.71) showed the capacitive nature of the NiCo 3 S. The capacitive and diffusive behavior calculated by Dunn's relationship confirmed the pseudocapacitive charge storage behavior of NiCo 3 S electrode making this a potential candidate for electrochemical applications. [ABSTRACT FROM AUTHOR]

The distinction between the shear behavior of tensile- and shear-induced fractures is critical to understanding the deformation and failure of geologic discontinuities at different scales. To investigate these differences, a series of direct shear tests were performed on sandstone specimens with a continuous fracture created by either splitting or shearing. The acoustic emission (AE) technique was used to examine variations in grain-size cracking behavior between specimens with tensile- and shear-induced fractures. An increase in normal stress for both fracture types correlates with increased microcrack density and energy release. However, there are notable differences: during the shear process, tensile-induced fractures produce AE sequences similar to the seismic patterns observed along natural tectonic faults, with foreshocks, mainshocks, and aftershocks. In contrast, the AE sequence for shear-induced fractures during the shear process lacks prominent mainshocks and deviates progressively from the power-law function with time as normal stress increases. In addition, the AE b-value for tension-induced fractures initially shows a gradual decrease as the mainshock approaches and then slowly increases during the aftershock period. In contrast, the b-value remains nearly constant for shear-induced fractures due to the low roughness and heterogeneity of the fracture surface. These differences highlight the strong correlation between AE responses and fault heterogeneity, paving the way for fault characterization and risk assessment in subsurface energy extraction. Highlights: The cracking behavior of both tensile- and shear-induced fractures in direct shear tests is investigated using the AE technique. In direct shear tests, the AE sequences of tensile fractures follow a power law, while a significant deviation from the power law is observed in the AE sequence of shear fractures. The power-law evolution of the AE sequence before and after the mainshock, together with anomalous b-values, can be used as indicators to distinguish young faults from mature faults. [ABSTRACT FROM AUTHOR]

Cancer, a disease intimately linked to cellular mutations, is commonly believed to exhibit a positive association with the cell count and lifespan of a species. Despite this assumption, the observed uniformity in cancer rates across species, referred to as the Peto’s paradox, presents a conundrum. Recognizing that tumour progression is not solely dependent on cancer cells but involves intricate interactions among various cell types, this study employed a Lotka-Volterra (LV) ordinary differential equation model to analyze the evolution of cancerous cells and the cancer incidence in an immune environment. As a result, this study uncovered the sufficient conditions underlying the absence of correlation in Peto’s paradox and provide insights into the reasons for the equitable distribution of cancer incidence across diverse species by applying nondimensionalization and drawing an analogy between the characteristic time interval for the variation of cell populations in the ODE model and that of cell cycles of a species. [ABSTRACT FROM AUTHOR]

Functional traits are important in understanding how plants respond and adapt to their immediate environment. Parrotia subaequalis is a highly endangered arbor species found throughout eastern China, primarily inhabiting hillsides and valleys, yet, little is known about the variation in leaf traits across these environments. In the present study, we tested this by comparing leaf surface area, leaf weight, leaf length, leaf symmetry and leaf mass per unit area, as well as the relationship between leaf traits and environmental factors and the scaling relationship between leaf surface area versus leaf dry mass. We observed significant differences in leaf surface area, weight, and length among the population sites, and these variables were strongly affected by environmental factors, especially high mean annual temperatures in hillside habitats and high mean annual precipitation in valley habitats. The scaling exponents remained numerically variant among the 10 populations, with different slopes greater than 1.0, and the scaling exponents increased significantly with hillside habitats. These metrics correlated with soil thickness associated with different habitat types. The areal ratio (AR) values in all populations deviated from 1, indicating that the two lamina sides were asymmetrical. The standardized symmetry index (SI) values displayed significant variation, especially in leaves from hillside habitats with a high degree of asymmetry. Collectively, our findings demonstrated that leaf functional traits exhibit considerable variability in response to different environmental contexts and provide valuable reference data that could be useful for conserving this endangered species. [ABSTRACT FROM AUTHOR]

Low-carbon cities aim to minimize greenhouse gas emissions in the context of climate change in the process of urbanization. Maintaining these cities at an appropriate physical scale has been proven to contribute to carbon reduction. Therefore, this study extended the definition of the city scale to an integrated framework with three dimensions: the construction land area, population, and economy. The urban construction land of 258 cities in China during 2012 to 2019 was divided into commercial, industrial, residential, and traffic sectors, and carbon emissions were calculated for each. The regression relationship between carbon emissions and the urban scale revealed by panel data analysis showed the following conclusions: (1) carbon emissions were concentrated in north China, provincial capital cities, and municipalities directly under the central government during the research period, and the industrial sector was the main emission resource, accounting for more than 85% of the total emissions. (2) Carbon emissions per unit of land decreased with the increasing land scale, regardless of the land-use type. The growth rate of carbon emissions was slower than that of the population, and cities also became more efficient as their economic scale expanded. (3) Compared with small cities, the large ones benefited more from increasing commercial and traffic land areas, whereas industrial emissions for production needs exhibited significant agglomeration characteristics. Overall, low-carbon planning should focus on the driving role of provincial capital cities as large cities tend to be more efficient, and develop the emission reduction potential of major industrial cities as well. [ABSTRACT FROM AUTHOR]

A practical supercapattery electrode with photocatalytic efficiency is created using tunnel-structured manganese dioxide nanostructure intercalated with K+ ions ∝ - K 0.18 MnO 2 through a single-step hydrothermal method. The tunnel structure and the high surface area of the material facilitate the interaction of ions, increasing the number of active sites and thereby enhancing the electrochemical and photocatalytic performance. The nanostructure exhibits a tetragonal hollandite structure with well-defined nanorod morphology. The prepared sample shows intriguing results in Raman spectra across various laser wavelength inputs. The examination of pore characteristics of the material revealed mainly mesopores throughout the sample. Optical examinations conducted using UV visible spectra revealed an energy band gap of 1.08 eV. The multiple oxidation states of the manganese were determined through X-ray photoelectron spectroscopy analysis, along with the existence of various oxygen components. The electrochemical performance of the optimized sample was assessed using a three-electrode assembly in 1 M KOH, which showed a high specific capacity of 577.33 C g−1 at 0.5 A g−1, confirmed using cyclic voltammetry (CV) and galvanostatic charge–discharge (GCD) analysis. The cyclic stability of the electrode was tested, showing 79.6% capacitance retention with 99.84% coulombic efficiency over 5000 charge–discharge cycles at 15 A g−1. The capacitive and diffusive contributions of the electrode material were evaluated using the power law and Dunn's method. The dye degradation efficiency of the nanocatalyst, tested over crystal violet dye, showed 98.7% degradation even after sunlight irradiation. These multifunctional applications of MnO2 pave the way to develop hybrid nanocomposites for extended applications. [ABSTRACT FROM AUTHOR]

Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforwardmeans of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed themost variation across differentmodes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders. [ABSTRACT FROM AUTHOR]

Abstract.Empirical studies of natural language have demonstrated that word frequencies follow power law distributions. However, standard statistical models often fail to capture this property. The Pitman-Yor process (PYP), a Bayesian non parametric model capable of generating power law distributions, has been widely used in probabilistic topic models to handle data with an infinite number of components. However, existing PYP topic models rarely account for the relationships between topics. Hidden Markov models (HMMs) are popular models for modeling topic relationships. To address this limitation, we propose a probabilistic topic model that combines HMM with Pitman-Yor priors. The posterior inference was performed by using variational Bayes methods. We applied our method to text categorization and compared it with two related topic models: the hidden Markov topic model and hierarchical PYP topic model. [ABSTRACT FROM AUTHOR]

Dispersion of Basil seed gum has high viscosity and exhibits shear-thinning behavior. This study aimed to analyze the influence of microwave treatment (MT) at various time intervals (0, 1, 2, and 3 min) on the viscosity and rheological behavior of Basil seed gum dispersion (0.5%, w/v). The finding of this study revealed that the apparent viscosity of Basil seed gum dispersion (non-treated dispersion) reduced from 0.330 Pa.s to 0.068 Pa.s as the shear rate (SR) increased from 12.2 s−1 to 171.2 s−1. Additionally, the apparent viscosity of the Basil seed gum dispersion reduced from 0.173 Pa.s to 0.100 Pa.s as the MT time increased from 0 to 3 min (SR = 61 s−1). The rheological properties of gum dispersion were successfully modeled using Power law (PL), Bingham, Herschel–Bulkley (HB), and Casson models, and the PL model was the best one for describing the behavior of Basil seed gum dispersion. The PL model showed an excellent performance with the maximum r-value (mean r-value = 0.942) and the minimum sum of squared error (SSE) values (mean SSE value = 5.265) and root mean square error (RMSE) values (mean RMSE value = 0.624) for all gum dispersion. MT had a considerable effect on the changes in the consistency coefficient (k-value) and flow behavior index (n-value) of Basil seed gum dispersion (p < 0.05). The k-value of Basil seed gum dispersion decreased significantly from 3.149 Pa.sn to 1.153 Pa.sn (p < 0.05) with increasing MT time from 0 to 3 min. The n-value of Basil seed gum dispersion increased significantly from 0.25 to 0.42 (p < 0.05) as the MT time increased. The Bingham plastic viscosity of Basil seed gum dispersion increased significantly from 0.029 Pa.s to 0.039 Pa.s (p < 0.05) while the duration of MT increased. The Casson yield stress of Basil seed gum dispersion notably reduced from 5.010 Pa to 2.165 Pa (p < 0.05) with increasing MT time from 0 to 3 min. [ABSTRACT FROM AUTHOR]

With the availability of efficient and sophisticated finite element analysis (FEA) and computational fluid dynamics (CFD) tools, engineering designs are becoming more software-driven and simulation-based. However, the insights relevant to engineering designs tend to be hidden within massive temporal and spatial data produced with full-fledged three-dimensional simulations. In this paper, we present a preliminary study of the controlled intermittent dispensing of a typical non-Newtonian glue employed in the manufacturing of electric vehicles (EVs). The focus of the study is on the scaling issues derived from different computational and analytical models of interest and importance to the precision control of this non-Newtonian fluid, the lowest dynamic viscosity of which at extremely high shear rates is nearly four million times that of water. More specifically, the abrupt change of the inlet pressure with a constant outlet or ambient pressure and various modeling strategies for transient viscous internal flow with both Newtonian and non-Newtonian fluids are modeled and compared. The analytical and computational results of the developing Newtonian fluid, i.e., water, are derived and computed for validation and verification purposes before the actual applications to the developing non-Newtonian fluid. The concept of a well-established relaxation time before the onset of the steady solution for Newtonian fluids has been validated with both analytical and computational approaches before its expansion and adoption to non-Newtonian fluids with complex rheological behaviors. Other issues attributed to transient operations and precision controls of non-Newtonian fluid delivery involve the pressure pulse and pressure wave propagation within the flexible pipe with compressible or almost incompressible non-Newtonian fluids with a constant pressure at the outlet and a constant mass flow rate or average axial velocity at the inlet, which will be addressed in a separate paper. [ABSTRACT FROM AUTHOR]

In this study, the prediction of n-value (index-value) surfaces—a key indicator of the field and temperature dependence of critical current density in superconductors—across various high-temperature superconducting materials is addressed using a deep learning modeling approach. As superconductors play a crucial role in advanced technological applications in aerospace and fusion energy sectors, improving their performance model is essential for both practical and academic research purposes. The feed-forward deep learning network technique is employed for the predictive modeling of n-value surfaces, utilizing a comprehensive dataset that includes experimental data on material properties and operational conditions affecting superconductors' behavior. The model demonstrates enhanced accuracy in predicting n-value surfaces when compared to traditional regression methods by a 99.62% goodness of fit to the experimental data for unseen data points. In this paper, we have demonstrated both the interpolation and extrapolation capabilities of our proposed DFFNN technique. This research advances intelligent modeling in the field of superconductivity and provides a foundation for further exploration into deep learning predictive models for different superconducting devices. [ABSTRACT FROM AUTHOR]

This study investigates the ephemeral and common features of public administration, reviews the development of public administration, identifies current research concerns, and explores future research directions. We gathered 89,905 documents from the SSCI database from the 1920s to 2020 and used co-word analysis to assess the evolution of the discipline of public administration over the past century. The popular concerns in the field of public administration are increasingly evident, particularly as they relate to performance evaluation, sector development, governance reform, governance equity, and strategic management. The future development of public administration will likely be more closely integrated with political science, and the related research will have a stronger preference for democratization.

Exact knowledge of wind energy potential is a fundamental issue in wind energy utilization. The vertical changes in wind speeds, that is, the wind profile, have a predominant impact on the wind energy available at a location because the kinetic energy of moving air is proportional to the square of the wind speed. Roughness describes the resistance of a 3D surface to moving air. The exponent α of the power law of Hellmann and the roughness length (z0) are two parameters that describe the effects of the roughness of the surface on the wind profile. They can be used for the vertical extrapolation of wind speeds. The exponent α can be determined using multiple height level wind speed measurement data, whereas a reliable technique for the calculation of the roughness length requires detailed knowledge of the 3D geometry of the measurement site. In the present study, the exponent α was calculated based on SODAR wind speed measurements, while (z0) was determined using a combination of GIS and UAS-based aerial survey methods. Wind speeds measured at 50 m were extrapolated for height levels of 80, 90, 100, 110, and 120 m using dynamic power law exponent values. Wind power was determined using the power law (method V1), roughness length (method V2), frequency distribution (method W-RF), and gamma distribution (method W-G), and Windographer software was compared to the values calculated from the empirical (measured) wind speeds. A comparative statistical analysis of the datasets of the power law and roughness length methods on monthly/diurnal, annual/diurnal, and month/direction contexts showed no significant differences at all height levels. Differences can be detected in the distribution of the signs of the differences at heights of 80 and 120 m for the entire dataset. Underestimation was dominant with a significant frequency (over 70 %) in the case of both methods and heights. There were no significant differences between the wind power estimations provided by the different methods, and all the methods involved in the study underestimated the wind speeds and wind energy potential for each height level. Methods V1 and V2 can be used alternatively, depending on the input data available for analysis. The major advantage of method V2 is that it provides the same accuracy as V1, which requires a UAS-based aerial survey at the beginning, but continuous wind measurements must be performed at a lower height only. This means that there is no need for a high measurement tower, which makes the measurements simpler, more cost-effective, and causes much less disturbance to the environment. Another important advantage of the methods presented here is that they use a dynamic approach of power law exponent values that provide a more realistic estimation of wind speed and energy on a diurnal scale.

The natural gas pipeline system (NGPS) is complex with a diverse network topology. This study elucidates the network characteristics of the NGPS, revealing that it shares commonalities with small-world networks, but with differences caused by its structural and operational characteristics. The combination of the low aggregation effect caused by branching structure and the offsetting effect caused by economic and geographical constraints results in a network with small world characteristics. NGPS has longer mean path than common small world network, but the aggregation factor is close to that of a standard small world network. With respect to its small-world characteristics and correlation with self-organized criticality (SOC), an in-depth study of the SOC characteristics of the NGPS was conducted utilizing a combination of traditional logarithm analysis and R/S analysis. The findings establish that there are atypical SOC characteristics, characterized by a weakened power law within a specific functional range, thereby encouraging the advancement of SCADA and other safety technologies. Because of the line-pack, which induces time lags in the NGPS, NGPS can prevent the cascade failures. The reasons for the periodicity and centrality of NGPS data are analyzed by the affecting components, with the R/S analysis verifying a weak positive correlation of the function data across different time series. The interlinking of the SOC and R/S analysis illustrates the connection between critical status, accident type, and operation index. The results prove the reasonability and importance of insurance measures, such as regular internal detection, higher grade steel pipes change and staff training et al., in a novel view of SOC. The future works in building accidents database and human factors study are proposed.

Abstract A fundamental pattern in ecology is that smaller organisms are more abundant than larger organisms. This pattern is known as the individual size distribution (ISD), which is the frequency distribution of all individual body sizes in an ecosystem. The ISD is described by a power law and a major goal of size spectra analyses is to estimate the exponent of the power law, λ. However, while numerous methods have been developed to do this, they have focused almost exclusively on estimating λ from single samples. Here, we develop an extension of the truncated Pareto distribution within the probabilistic modelling language Stan. We use it to estimate multiple λs simultaneously in a hierarchical modelling approach. The most important result is the ability to examine hypotheses related to size spectra, including the assessment of fixed and random effects, within a single Bayesian generalized mixed model. While the example here uses size spectra, the technique can also be generalized to any data that follow a power law distribution.

AbstractFunctionally graded materials (FGMs) are advanced composite materials with spatially varying properties, and their porosity distribution further enhances their complexity. The distribution pattern of porosity within a porous material plays a crucial role in determining the mechanical response of these structures. Therefore, the main objective of this study is to analyze the bending, free vibration, and buckling characteristics of porous FG plates by considering different porosity distributions and their effects on the overall behavior. To achieve this goal, a new finite element model is developed in the framework of an improved first-order shear deformation theory (IFSDT). In contrast to the conventional Mindlin–Reissner theory, the present IFSDT incorporates an improved mathematical formulation and provides a more realistic parabolic depiction of shear strain throughout the plate’s thickness without using any shear correction factors. In the present study, five types of porosity distribution functions are considered for the analysis. The material characteristics of the FGM porous plate change gradually in the thickness direction based on a power-law function. The governing equations are derived here using Hamilton’s principle, and a finite element method is employed for numerical analysis. Comparative analyses with previously published literature underscore the precision and simplicity of our developed finite element model. Moreover, the effects of various types of loads, porosity parameters, power-law index, side-to-thickness ratio, aspect ratio, porosity distributions and boundary conditions on the deflections, natural frequencies, and critical buckling loads are thoroughly analyzed in detail. Finally, the findings of this research contribute to the understanding of the mechanical behavior of FGMs and pave the way for designing and optimizing novel porous functionally graded structures. [ABSTRACT FROM AUTHOR]

Network embedding is a technique used to generate low-dimensional vectors representing each node in a network while maintaining the original topology and properties of the network. This technology enables a wide range of learning tasks, including node classification and link prediction. However, the current landscape of network embedding approaches predominantly revolves around static networks, neglecting the dynamic nature that characterizes real social networks. Dynamics at both the micro- and macrolevels are fundamental drivers of network evolution. Microlevel dynamics provide a detailed account of the network topology formation process, while macrolevel dynamics reveal the evolutionary trends of the network. Despite recent dynamic network embedding efforts, a few approaches accurately capture the evolution patterns of nodes at the microlevel or effectively preserve the crucial dynamics of both layers. Our study introduces a novel method for embedding networks, i.e., bilayer evolutionary pattern-preserving embedding for dynamic networks (Bi-DNE), that preserves the evolutionary patterns at both the micro- and macrolevels. The model utilizes strengthened triadic closure to represent the network structure formation process at the microlevel, while a dynamic equation constrains the network structure to adhere to the densification power-law evolution pattern at the macrolevel. The proposed Bi-DNE model exhibits significant performance improvements across a range of tasks, including link prediction, reconstruction, and temporal link analysis. These improvements are demonstrated through comprehensive experiments carried out on both simulated and real-world dynamic network datasets. The consistently superior results to those of the state-of-the-art methods provide empirical evidence for the effectiveness of Bi-DNE in capturing complex evolutionary patterns and learning high-quality node representations. These findings validate the methodological innovations presented in this work and mark valuable progress in the emerging field of dynamic network representation learning. Further exploration demonstrates that Bi-DNE is sensitive to the analysis task parameters, leading to a more accurate representation of the natural evolution process during dynamic network embedding. [ABSTRACT FROM AUTHOR]

Network modeling has been explored extensively by means of theoretical analysis as well as numerical simulations for Network Reconstruction (NR). The network reconstruction problem requires the estimation of the power-law exponent (γ) of a given input network. Thus, the effectiveness of the NR solution depends on the accuracy of the calculation of γ. In this article, we re-examine the degree distribution-based estimation of γ, which is not very accurate due to approximations. We propose X-distribution, which is more accurate than degree distribution. Various state-of-the-art network models, including CPM, NRM, RefOrCite2, BA, CDPAM, and DMS, are considered for simulation purposes, and simulated results support the proposed claim. Further, we apply X-distribution over several real-world networks to calculate their power-law exponents, which differ from those calculated using respective degree distributions. It is observed that X-distributions exhibit more linearity (straight line) on the log-log scale than degree distributions. Thus, X-distribution is more suitable for the evaluation of power-law exponent using linear fitting (on the log-log scale). The MATLAB implementation of power-law exponent (γ) calculation using X-distribution for different network models and the real-world datasets used in our experiments are available at https://github.com/Aikta-Arya/X-distribution-Retraceable-Power-Law-Exponent-of-Complex-Networks.git. [ABSTRACT FROM AUTHOR]

Real networks often exhibit clustering, the tendency to form relatively small groups of nodes with high edge densities. This clustering property can cause large numbers of small and dense subgraphs to emerge in otherwise sparse networks. Subgraph counts are an important and commonly used source of information about the network structure and function. We study probability distributions of subgraph counts in a community affiliation graph. This is a random graph generated as an overlay of m partly overlapping independent Bernoulli random graphs (layers) $G_1,\dots,G_m$ with variable sizes and densities. The model is parameterised by a joint distribution of layer sizes and densities. When m grows linearly in the number of nodes n , the model generates sparse random graphs with a rich statistical structure, admitting a nonvanishing clustering coefficient and a power-law limiting degree distribution. In this paper we establish the normal and $\alpha$ -stable approximations to the numbers of small cliques, cycles, and more general 2-connected subgraphs of a community affiliation graph. [ABSTRACT FROM AUTHOR]

Abnormal trading behavior in e-markets, also known as market manipulation, is done with the aim of deceiving traders in order to obtain unusual profits. These behaviors harm the proper functioning and integrity of the market and are, therefore, detrimental to trading activities. Although many studies have investigated this issue, their focus is on modeling the detection method, and they have neglected the processes that lead to manipulation. So that, to questions such as: What is the main reason for the success of these manipulations? What is the central idea behind manipulative strategies? Are manipulative schemes based on a specific theory? No answer has scientifically been given yet. Accordingly, there is a knowledge gap in the literature; this study aims to provide this complementary perspective and fill this gap. In this study, as a pioneer in this field, we develop the concept of the trading behavior's diffusion network. Then, we analyze and examine the diffusion process from the aspects of temporal dynamics of trading behaviors, diffusion network topology, diffusion patterns, and structural characteristics of the diffusion network. The results indicate significant differences in the diffusion process of manipulative behavior compared to regular trading behavior and random behavior. In addition, manipulators showed higher social influence (network centrality) in the diffusion network of manipulative behavior. Our findings bear significant implications for market supervisors and regular traders and provide a new dimension for researchers and supervisors in determining approaches and methods for detecting manipulation. [ABSTRACT FROM AUTHOR]

Aim: Species–area relationships (SAR) are widely utilized for estimating the species richness and its spatial turnover across various scales. Despite the prevalent characterization of SAR using the power law in many microbial community studies, its efficacy remains unvalidated. This study aims to characterize the microbial SAR and its mechanisms in alpine grassland soils on the Qinghai‐Tibet Plateau (QTP). Location: Qinghai‐Tibet Plateau, China. Time Period: August 2014. Major Taxa Studied: Soil bacteria. Methods: Soil samples were collected from five alpine grassland sites on the QTP. Employing a nested sampling strategy at each site, soil samples were collected in plot sizes ranging from 0.5 × 0.5 m2 to 2048 × 2048 m2. Soil bacterial communities were analysed by sequencing 16S ribosomal RNA gene amplicons using an Illumina MiSeq. Results: The bacterial SAR exhibited a logarithmic power law (R2: 0.952–0.999), outperforming the power law (R2: 0.701–0.852). Consequently, the most widely adopted power law led to an overestimation of species richness by up to 15.07% in areas >256 × 256 m2, and the regional maximum theoretical richness based on Chao1 by up to 9.88%. Mechanistically, the passive sampling hypothesis was refuted through the rarefied species richness analysis, and the disproportionate effect hypothesis was rejected based on analyses of the effective numbers of species number conversions for the probability of interspecific encounters (SPIE). Notably, Pearson and multiple linear regression analyses indicated that the spatial turnover of bacterial richness was determined by the environmental heterogeneity (R2: 0.855–0.999), rather or better than environmental variables themselves, supporting the 'environment heterogeneity hypothesis'. Main Conclusions: Soil bacterial SAR in alpine grasslands exhibited a logarithmic power relationship. Spatial turnover was primarily governed by the environmental heterogeneity. In contrast, the traditional power law leads to an overestimation of soil bacterial diversity at the regional scale. [ABSTRACT FROM AUTHOR]

A fundamental pattern in ecology is that smaller organisms are more abundant than larger organisms. This pattern is known as the individual size distribution (ISD), which is the frequency distribution of all individual body sizes in an ecosystem.The ISD is described by a power law and a major goal of size spectra analyses is to estimate the exponent of the power law, λ. However, while numerous methods have been developed to do this, they have focused almost exclusively on estimating λ from single samples.Here, we develop an extension of the truncated Pareto distribution within the probabilistic modelling language Stan. We use it to estimate multiple λs simultaneously in a hierarchical modelling approach.The most important result is the ability to examine hypotheses related to size spectra, including the assessment of fixed and random effects, within a single Bayesian generalized mixed model. While the example here uses size spectra, the technique can also be generalized to any data that follow a power law distribution. [ABSTRACT FROM AUTHOR]

Colloidal semiconductor quantum dots have many potential optical applications, including quantum dot light‐emitting diodes, single‐photon sources, or biological luminescent markers. The optical properties of colloidal quantum dots can be affected by their dielectric environment. This study investigated the photoluminescence (PL) decay of thick‐shell gradient‐alloyed colloidal semiconductor quantum dots as a function of solvent refractive index. These measurements were conducted in a wide range of delay times to account for both the initial spontaneous decay of excitons and the delayed emission of excitons that has the form of a power law. It is shown that whereas the initial spontaneous PL decay is very sensitive to the refractive index of the solvent, the power‐law delayed emission of excitons is not. Our results seem to exclude the possibility of carrier self‐trapping in the considered solvents and suggest the existence of trap states inside the quantum dots. Finally, our data show that the average exciton lifetime significantly decreases as a function of the solvent refractive index. The change in exciton lifetime is qualitatively modeled and discussed. [ABSTRACT FROM AUTHOR]

The gene content in a metagenomic pool defines the function potential of a microbial community. Natural selection, operating on the level of genomes or genes, shapes the evolution of community functions by enriching some genes while depriving the others. Despite the importance of microbiomes in the environment and health, a general metric to evaluate the community-wide fitness of microbial genes remains lacking. In this work, we adapt the classic neutral model of species and use it to predict how the abundances of different genes will be shaped by selection, regardless of at which level the selection acts. We establish a simple metric that quantitatively infers the average survival capability of each gene in a microbiome. We then experimentally validate the predictions using synthetic communities of barcoded Escherichia coli strains undergoing neutral assembly and competition. We further show that this approach can be applied to publicly available metagenomic datasets to gain insights into the environment-function interplay of natural microbiomes. [ABSTRACT FROM AUTHOR]

In the present study, a functionally graded cantilever beam has been analyzed to observe its deformation behavior and stress variations. While the material properties (modulus of elasticity, modulus of rigidity, and density) have been varied along the height of the beam, Poisson's ratio has been considered a constant. The governing equations have been derived using Hamilton's Principle in the framework of higher-order beam theory. The derived equations are then simplified to a single equation, which is similar to that of isotropic beams. However, the work is extended to include a few higher-order terms and to study the effect of the incorporation of these terms on the resulting FG beam behavior. The development of a single governing equation for studying the statics and dynamics of an FG beam with the incorporation of higher-order terms is a unique part of the report. The solution of the governing equation is approached using approximate methods; in this work, the B-spline collocation technique is used to arrive at the results. A sixth-order B-spline basis function is used as an approximating polynomial, and the Greville abscissa has been used to generate collocation points. The obtained results have been verified against standard literature to find a satisfactory match. The results include comparative plots for normalized bending and transverse shear stresses, with and without the inclusion of higher-order terms. The results are then extended to study the effect of material index on the deformation and stress behavior of FG beams. The effect of aspect ratio on results is also studied for comparison of various beam theories. [ABSTRACT FROM AUTHOR]