Almost two decades ago, Ernesto P. Borges and Bruce M. Boghosian embarked on the intricate task of composing a manuscript to honor the profound contributions of Constantino Tsallis to the realm of statistical physics, coupled with a concise exploration of q-Statistics. Fast forward to Constantino Tsallis’ illustrious 80th birthday celebration in 2023, where Deniz Eroglu and Ugur Tirnakli delved into Constantino’s collaborative network, injecting renewed vitality into the project. With hearts brimming with appreciation for Tsallis’ enduring inspiration, Eroglu, Boghosian, Borges and Tirnakli proudly present this meticulously crafted manuscript as a token of their gratitude.

Precipitation patterns are critical for understanding the hydrological and climatological dynamics of any region. Sicily, the largest island of the Mediterranean sea, with its diverse topography and climatic conditions, serves as an ideal case study for analyzing precipitation data to gain insights into regional water resources, agricultural productivity, and climate change impacts. This paper employs advanced statistical physics methods, particularly Tsallis q-statistics, to analyze sub-hourly precipitation data from 2002 to 2023, provided by the Sicilian Agrometeorological Informative System (SIAS). We investigate several critical variables related to rainfall events, including duration, depth, maximum record, and inter-event time. The study spans two decades (2002-2012 and 2013-2023), analyzing the distributions of relevant variables. Additionally, we examine the simple returns of these variables to identify significant temporal changes, fitting these returns with q-Gaussian distributions. Our findings reveal the scale-invariant nature of precipitation events, the presence of long-range interactions and memory effects, characteristic of complex environmental processes.

The statistics of grain displacements probability distribution function (pdf) during the shear of a granular medium displays an unusual dependence with the shear increment upscaling as recently evinced [Phys. Rev. Lett. 115 238301 2015]. Basically, the pdf of grain displacements has clear nonextensive ($q$-Gaussian) features at small scales but approaches to Gaussian characteristics at large shear window scales -- the granulence effect. Here, we extend this analysis studying a larger system (more grains considered in the experimental setup) which exhibits a severe shear band fault during the macroscopic straining. We calculate the pdf of grain displacements and the dependency of the $q$-statistics with the shear increment. This analysis have shown a singular behavior of $q$ at large scales, displaying a non-monotonic dependence with the shear increment. By means of an independent image analysis, we demonstrate that this singular non-monotonicity could be associated with the emergence of a shear band within the confined system. We show that the exact point where the $q$-value inverts its tendency coincides with the emergence of a giant percolation cluster along the system, caused by the shear band. We believe that this original approach using Statistical Mechanics tools to identify shear bands can be a very useful piece to solve the complex puzzle of the rheology of dense granular systems.

In this paper we study a class of impulsive delayed neural networks with parameter uncertainties and reaction-diffusion terms. Using the properties of M-matrices and some inequality techniques, some sufficient conditions are derived to guarantee the global exponential stability of the given system. The results obtained are presented by simple algebraic inequalities, which are certainly more concise than previous methods. Finally, the validity and usefulness of our results are illustrated by arithmetic examples.

Anaerobic digestion has emerged as an increasingly popular method for the effective management of organic waste and wastewater with the potential to generate renewable energy. However, efficient digestion depends heavily on the complex and diverse microbial communities mediating this process. This review paper delves into the complex microbiome that exists within anaerobic digestion digesters, playing a critical role in the efficient and sustainable conversion of organic waste into biogas. Its primary focus falls within the scope of how various operational and environmental factors, including temperature, pH, hydraulic retention time, substrate loading rates, and the presence of inhibitory compounds, can substantially impact the microbiome of these systems. Through an extensive analysis of the existing literature, the microbial ecology of anaerobic digestion, including the composition and function of the microbial community, their interactions, and responses to different stressors, are evaluated. By offering critical insights into the distribution patterns of active microbial populations upon alterations in various process parameters, a better understanding of the microbial dynamics within these systems is provided, enhancing their efficiency. Overall, this paper emphasizes the importance of unraveling the microbial evolution within AD reactors to optimize biogas production and sustainable waste management and proposes future perspectives in this direction.

In this paper, we study the semilinear pseudo-parabolic equations $\displaystyle u_{t} - \triangle_{\mathbb{B}}u - \triangle_{\mathbb{B}}u_{t} = \left|u\right|^{p-1}u$ on a manifold with conical singularity, where $\triangle _{\mathbb{B}}$ is Fuchsian type Laplace operator investigated with totally characteristic degeneracy on the boundary $x_{1} = 0$. Firstly, we discuss the invariant sets and the vacuum isolating behavior of solutions with the help of a family of potential wells. Then, we derive a threshold result of existence and nonexistence of global weak solution: for the low initial energy $J(u_{0})<d$, the solution is global in time with $I(u_{0}) >0$ or $\displaystyle\Vert \nabla_{\mathbb{B}}u_{0}\Vert_{L_{2}^{\frac{n}{2}}(\mathbb{B})} = 0$ and blows up in finite time with $I(u_{0}) < 0$; for the critical initial energy $J(u_{0}) = d$, the solution is global in time with $I(u_{0}) \geq0$ and blows up in finite time with $I(u_{0}) < 0$. The decay estimate of the energy functional for the global solution and the estimates of the lifespan of local solution are also given.

Biogas production from organic waste is a promising renewable energy source, but achieving optimal production and digester stability can be challenging. This study investigated the impact of the Evogen microbial additive on biogas production and digester quality through microbial abundance and physicochemical parameter analysis. Two biogas plants, BG01 and BG02, were examined using 16S rRNA profiling to assess microbial abundance. Simultaneously, physicochemical parameters, including FOS/TAC ratio, total solids, volatile solids, biogas production, and VFA profile, were measured to evaluate digester performance. Results revealed distinct microbial community shifts in Evogen-treated digesters. Increased abundance of methanogenic archaea and hydrolytic bacteria indicated improved anaerobic digestion. Evogen supplementation also positively affected digester performance, with higher FOS/TAC ratios indicating enhanced acidification and methanogenesis. Reductions in total solids and volatile solids demonstrated improved organic matter degradation. Significantly higher biogas production was observed in Evogen-treated digesters, highlighting its potential as a microbial additive. Furthermore, VFA profiling demonstrated improved process stability and reduced substrate inhibition in Evogen-treated digesters. In summary, Evogen microbial additive positively influenced microbial dynamics, improving biogas production and digester quality. These findings contribute to optimizing biogas production systems and understanding the complex microbial interactions within anaerobic digesters.

The SK model proposed by Sherrington and Kirkpatrick is an important mean field model for spin glasses. The dependence structure of Hamiltonians of the SK model is considered in this paper. We have investigated the covariance matrix of Hamiltonians of the SK model, and establish a relationship between the rank of the corresponding covariance matrix and the dimension of the space spanned by the Hamiltonians. Finally, we also give an upper bound for the rank of the covariance matrix, which might be the best.

In (Semigroup Forum 77: 325-338, 2008) Mahmoudi M. and Renshaw J. solved a study that covers of cyclic $S$-acts over monoids. This article is an attempt to initiate the covers of finitely generated $S$-acts. We give a necessary and sufficient condition for a monoid to have the properties that $n$-generated $S$-acts have strongly flat covers, Condition $(P)$ covers and projective covers. The main conclusions extend some known results. We show also that Condition $(P)$ covers of finitely generated $S$-acts are not unique, unlike the situation for strongly flat covers. Additionally, we demonstrate that the property of Enochs' $\mathcal{X}$-precover of $S$-act $A$, where $\mathcal{X}$ denotes a class of $S$-acts that are closed under isomorphisms.

In this paper, we study the global existence and exponential decay for a dynamic contact problem between a Timoshenko beam with second sound and two rigid obstacles, of which the heat flux is given by Cattaneo's law instead of the usual Fourier's law. The main difficulties arise from the irregular boundary terms, from the low regularity of the weak solution and from the weaker dissipative effects of heat conduction induced by Cattaneo's law. By considering related penalized problems, proving some a priori estimates and passing to the limit, we prove the global existence of the solutions. By considering the approximate framework, constructing some new functionals and applying the perturbed energy method, we obtain the exponential decay result for the approximate solution, and then prove the exponential decay rate to the original problem by utilizing the weak lower semicontinuity arguments.

The main contribution of this paper is to develop a graphical tool to evaluate the suitability of a candidate parametric model to fit a data set. The practical motivation is to check the adequacy of the so called SAINT model proposed in Jarner and Kryger (2011). This model has been implemented in practice by an important European pension fund concerned with setting annuity reserves for all current or former employees of Denmark. So, one particular relevant question is whether this mortality model is actually fitting old-age. Our graphical test can be described as follows. First we transform the data by means of the parametric model which is being evaluated. Should the model be correct, the transformed data will be in accordance with an Exponential distribution with rate 1. Then we construct a family of local linear hazard estimates based on the data on the transformed scale. Finally we use the statistical tool SiZer to assess the goodness-of-fit of the Exponential distribution to the data on the transformed scale. If the model is correct the SiZer map should not reveal any significant feature in the family of kernel estimates. Our method allow us to establish a diagnostic regarding the validity of the SAINT model when describing mortality patterns in four different countries.

In this paper we present experimental results concerning Acoustic Emission (AE) recorded during cyclic compression tests on two different kinds of brittle building materials, namely concrete and basalt. The AE inter-event times were investigated through a non-extensive statistical mechanics analysis which shows that their decumulative probability distributions follow q-exponential laws. The entropic index q and the relaxation parameter q  1=Tq, obtained by fitting the experimental data, exhibit systematic changes during the various stages of the failure process, namely (q; Tq) linearly align. The Tq = 0 point corresponds to the macroscopic breakdown of the material. The slope, including its sign, of the linear alignment appears to depend on the chemical and mechanical properties of the sample. These results provide an insight on the warning signs of the incipient failure of building materials and could therefore be used in monitoring the health of existing structures such as buildings and bridges.

In this paper, we study the well-posedness and asymptotics of a one-dimensional thermoelastic laminated beam system either with or without structural damping, where the heat conduction is given by Fourier's law effective in the rotation angle displacements. We show that the system is well-posed by using Lumer-Philips theorem, and prove that the system is exponentially stable if and only if the wave speeds are equal, by using the perturbed energy method and Gearhart-Herbst-Prüss-Huang theorem. Furthermore, we show that the system with structural damping is polynomially stable provided that the wave speeds are not equal, by using the second-order energy method.

In this work, we consider a one-dimensional laminated beam in the case of non-equal wave speeds with only one infinite memory on the effective rotation angle. In this case, we establish the general decay result for the energy of solution without any kind of internal or boundary control. The main result is obtained by applying the method used in Guesmia et al. (Electron. J. Differential Equations 193: 1-45, 2012) and the second-order energy.

In this paper, we study a one-dimensional Bresse-Cattaneo system with infinite memories and time-dependent delay term (the coefficient of which is not necessarily positive) in the internal feedbacks. First, it is proved that the system is well-posed by means of the Hille-Yosida theorem under suitable assumptions on the relaxation functions. Then, without any restriction on the speeds of wave propagations, we establish the exponential or general decay result by introducing suitable energy and Lyapunov functionals.

In this study, based on a new quantum integral identity, the authors establish some quantum estimates of Hermite-Hadamard type inequalities for convex functions. These results generalize and improve some known results given in literatures.

In this paper, several classes of rough Marcinkiewicz integral operators along surfaces of revolution on product spaces are investigated. We prove the Lp boundedness of these operators when their kernels functions belong to a class of functions related to a class of functions introduced by Grafakos-Stefanov. The results in this paper extend and improve several known results on Marcinkiewicz integrals.

In this paper, we consider a fourth-order suspension bridge equation with nonlinear damping term |u_t|^m-2u_t and source term |u|^p-2u. We give necessary and sufficient condition for global existence and energy decay results without considering the relation between m and p. Moreover, when p>m, we give sufficient condition for finite time blow-up of solutions. The lower bound of the blow-up time T_max is also established. It worth to mention that our obtained results extend the recent results of Wang (J. Math. Anal. Appl., 2014) to the nonlinear damping case.

Additives can improve the efficiency of anaerobic digestion by increasing biogas production, reducing air pollution, and preventing total ammoniacal nitrogen (TAN) inhibition. Biological or chemical supplementation can also improve the economic efficiency of anaerobic digestion. However, the effects of specific additives on biogas production can vary depending on the type of supplement used. This research utilizes the additives on an industrial scale and monitors the optimization of the anaerobic digestion operating parameters after their addition. The various AD additives were examined in a sufficient cycle of operation for three biogas plants located in northern Greece. In this manner, the effectiveness was investigated in multiple initial feeds and unstable operating situations caused by the seasonality of specific feedstocks. The existing operation state in the three biogas plants was recorded before and after adding the supplements. In all three cases, lower concentration levels were observed in many of the examined trace elements. Introducing additives proved to be a stabilization factor in AD performance and inhibition mediator.

In his best-selling book An Introduction to Probability Theory and its Applications, W. Feller established a way of ending the St. Petersburg Paradox by the introduction of an entrance fee, and provided it for the case in which the game is played with a fair coin. A natural generalization of his method is to establish the entrance fee for the case in which the probability of head is θ (0 < θ < 1/2). The deduction of those fees is the main result of Section 2. We then propose a Bayesian approach to the problem. When the probability of head is θ (1/2 < θ < 1) the expected gain of the St. Petersburg Game is finite, therefore there is no paradox. However, if one takes θ as a random variable assuming values in (1/2,1) the paradox may hold, what is counter-intuitive. On Section 3 we determine a necessary and sufficient condition for the absence of paradox on the Bayesian approach and on Section 4 we establish the entrance fee for the case in which θ is uniformly distributed in (1/2,1), for in this case there is paradox.

This paper investigates five different GARCH models (GARCH, EGARCH, IGARCH, Component GARCH (CGARCH) and GJR-GARCH) along with six distributions (Normal, Student’s t, GED and their skewed forms), which are used to forecast the volatility for the Bulgarian Stock index SOFIX. We use a GARCH model to predict how much time it will take, after the latest crisis, for the SOFIX index to reach its historical peak once again. The empirical data covers the period between the years 2000 and 2024, including the 2008 financial crisis and the COVID-19 pandemic. The purpose is to answer which of the five models is the best at forecasting the volatility of SOFIX and which distribution is most appropriate. The results, based on BIC and AIC information criteria, show that the ARMA(1,1)-CGARCH(1,1) specification with the Student t-distribution is preferred for forecasting. With the help of 5000 simulations, it is estimated that the chances of Index SOFIX reaching its historical peak value of 1976.73 (08.10.2007) are higher than 90% at 13.08.2087.

We propose a pricing formula for a defaultable zero-coupon bond with imperfect information under a regime-switching model using a structural form of credit risk modelling. While the value of the firm’s equity is observed continuously, we assume that the total value of the firm is only observed at discrete times, such as the dates of the release of the firm’s annual reports, or quarterly reports. This uncertainty about the true value of the firm results in credit spreads that do not approach zero as the debt approaches maturity, which is a problem with many structural models. The firm’s value is typically defined as its equity and debt; however, we consider the asset-to-equity ratio, an accounting ratio used to examine a firm’s financial well-being. A dependent regime-switching process models the components of this ratio, and the Markov chain represents the states of the economy. The main contribution is to study this problem when the dynamics of a firm’s assets have different parameters with regime changes.

For this research, a comparative evaluation was performed in which the addition of 3D-printed biocarriers fabricated with 13X and Halloysite (13Χ-Η) and commercial Kaldnes K1 biocarriers were evaluated as opposed to the non addition of biocarriers in a semi-pilot scale MBBR-MBR unit. For the evaluation of the MBBR-MBR efficiency, various physicochemical parameters were measured while static light scattering and optical microscopy observations were additionally used. Biofilm extracted from the biocarriers was also evaluated. It was observed that in the MBBR-MBR K1 unit, the membrane filtration improved while in the MBBR-MBR 13X-H unit the membrane fouling rate was the same as in the control MBBR-MBR unit. This is due to the production of large amount of SMP which resulted from the large amount of biofilm created in the 13X-H biocarriers. An optimal biodegradation of the organic load was concluded for all three MBBR-MBR units. The nitrification and denitrification processes were improved at the MBBR-MBR units using 13X-H and K1. The dry mass produced on the 13X-H biocarriers was three orders of magnitude larger than that produced on the K1. Finally, it was observed that mostly EPS was produced in the biofilm of K1 biocarriers while in the biofilm of 13X-H mostly SMP.

The physiological characteristics of liquid digestate retrieved from various biogas plants based in northern Greece are presented. Preliminary photocatalysis experiments on inoculated liquid di-gestate sampled showed that disinfection is possible when pre-treated digestate with a combination of centrifuge-flocculation-μFiltration after 5.5 hours of 0.7g/L suspended TiO2 under UVA illumination for the experimental conditions is used. A novel design photocatalytic nanofiltration reactor was implemented for disinfection experiments on pre-treated liquid digestate, giving promising results. This work sets the basis for the efficient operation and engineering application of technology collaboration with photocatalysis as the final step for liquid digestate sanitation and reusable water recovery.

Few studies have examined the dietary protein intake and sources in combination with the longitudinal changes in brain structure markers. Our study aimed to examine the association between dietary protein intake and different sources of dietary protein with the longitudinal rate of change in brain structural markers. A total of 2723 and 2679 participants from the UK Biobank were included in the analysis. The relative and absolute amounts of dietary protein intake were calculated through a 24-hour dietary recall questionnaire. The longitudinal change rates of brain structural biomarkers were computed using two waves of brain imaging data. We utilized multiple linear regression to examine the association between dietary protein and different sources and the longitudinal changes in brain structural biomarkers. Restrictive cubic splines were used to explore nonlinear relationships, and stratified and sensitivity analyses were conducted. Increasing the proportion of animal protein in dietary protein intake is associated with a slower reduction in the total hippocampus volume (THV, β: -0.02524, P&lt;0.05), left hippocampus volume (LHV, β: -0.02435, P&lt;0.01) and right hippocampus volume (RHV, β: -0.02544, P&lt;0.05).Higher intake of animal protein relative to plant protein is linked to a lower atrophy rate in THV (β: -0.01249, P&lt;0.05) and LHV (β: -0.01173, P&lt;0.05) and RHV (β: -0.01193, P&lt;0.05). Individuals with higher intake of seafood exhibit lower longitudinal rate of change in HV compared to those with no consume (THV, β: -0.004514; P&lt;0.05; RHV, β: -0.005527, P&lt;0.05). In subgroup and sensitivity analyses, there was not a significantly alteration. A moderate increase in the intake and proportion of animal protein in the diet, especially from seafood, is associated with a lower longitudinal change rate in hippocampus volume.

This research focuses on the Hopf bifurcation occurring at a singular point with high-order degeneracy in a class of three-dimensional systems. Based on center manifold theorem, by expanding the technique for determining singular point values of degenerate singularities in two-dimensional systems, the formal series method to determine the singular point values at the high-order degenerate critical point is discussed. Furthermore, a class of specific three-dimensional differential systems with high-order degenerate singularities is explored. By computing the singular point values and determining center conditions, we proved that a minimum of 5 small-amplitude limit cycles in the vicinity of the origin. This research provides new perspectives on the center problem and limit cycle bifurcation in high-dimensional systems with high-order degenerate singular points.

The extent of correlation or co-movement among the returns of developed and/or emerging stock markets remains pivotal for efficiently diversifying global portfolios. This correlation is prone to variation over time as a consequence of escalating economic interdependence fostered by international trade and financial markets. In this study, we analyzed the time-varying correlation and co-movement between the JSE.JO stock market of South Africa and its developed and developing stock market partners. The Dynamic Conditional Correlation - Exponential Generalized Autoregressive Conditional Heteroscedasticity (DCC-EGARCH) methodology is employed with different multivariate distributions to explore the time-varying correlation and volatilities between the JSE.JO stock market and its partners. Based on the conditional correlation results, the JSE.JO stock market is integrated and co-moves with its partners, and the conditional correlation for all markets exhibit time-variant behavior. The conditional volatility results show that the JSE.JO stock market behaves differently from other markets, especially after 2015, indicating a positive sign for investors to diversify between JSE.JO and its partners. The highest value of conditional volatility for markets was in 2020 during the COVID-19 pandemic, representing the riskiest period that investors should avoid due to the lack of diversification opportunities during crises.

Organic wastes and by-products from several activities, including food industries, farming, and animal husbandry, are a hygiene threat when aerobically decomposed. Therefore, their manage-ment is crucial for public health. In this direction, anaerobic digestion (AD) systems may be the solution by transforming waste into energy, which may decrease the environmental impact. However, their efficacy should be carefully examined. In this innovative study, we evaluated the physicochemical and microbial characteristics of liquid digestate (LD) retrieved from organic animal wastes in northern Greece using nanofiltration. Using treatment technologies including physical (solid-liquid separation, microfiltration and nanofiltration) and biological (anaerobic digestion), heavy metals and microbial (i.e., Salmonella spp., Escherichia coli, Enterococcus faecalis) concentrations reduced and nutrients were recovered. This work sets the basis for the efficient management of liquid digestate. Our method may enable the use of treated liquid digestate for unlimited irrigation water and other industrial applications of water. Apart from the sanitation process, the recovery of nutrients for soil fertilization seems to be a more sustainable way for fu-ture agricultural practices.

Recent analyses of cosmic microwave background surveys have revealed hints that there may be a non-trivial running of the running of the spectral index. If future experiments were to confirm these hints, it would prove a powerful discriminator of inflationary models, ruling out simple single field models. We discuss how isocurvature perturbations in multi-field models can be invoked to generate large runnings in a non-standard hierarchy, and find that a minimal model capable of practically realising this would be a two-field model with a non-canonical kinetic structure. We also consider alternative scenarios such as variable speed of light models and canonical quantum gravity effects and their implications for runnings of the spectral index.

We introduce the notion of generalized contraction on dualistic partial metric spaces. A fixed point theorem for mappings satisfying above mentioned contraction is obtained. Some consequences of our result are obtained. We construct an example to demonstrate the effectiveness of our result among the corresponding results in partial metric spaces. Our results provide substantial generalizations and improvements of several well known results existing in the comparable literature. We discuss an application of our fixed point results to show the existence of solution of functional equations.

Motivated by a common Mathematical Finance topic, this paper surveys several results concerning windings of 2-dimensional processes, including planar Brownian motion, complex-valued Ornstein-Uhlenbeck processes and planar stable processes. In particular, we present Spitzer's asymptotic Theorem for each case. We also relate this study to the pricing of Asian options.

In this data deposit, I describe a dataset that is the result of content mining 167,318 published articles for statistical test results. As a result of this content mining, 688,112 results from 50,845 articles were extracted. In order to provide a comprehensive set of data, the statistical results are supplemented with metadata from the article they originate from. The dataset is provided in a comma separated file (CSV) in long-format. For each of the 688,112 results, 20 variables are included, of which seven are article metadata and 13 pertain to the individual statistical results (e.g., reported and recalculated p-value). A five-pronged approach was taken to generate the dataset: (i) collect journal lists, (ii) spider journal pages for articles, (iii) download articles, (iv) add article metadata, and (v) mine articles for statistical results.

Many corporations have now revised their value statements to showcase their commitment to sustainable development and green business due to the pressing issues of climate and environmental damage. Despite the importance of value statements that can direct companies to achieve sustainable goals, what kind of values can be perceived as organizational orientations to sustainability remains unknown. This study explores how the general public views the inclusion of sustainable components in value statements from the listed companies in Hong Kong by using Q-sort methodology (n = 30). Analyzing the final sample of 40 value statements extracted from 27 listed companies, we identified three factors related to sustainability, namely, Empowering business process management, Empowering customer-centric excellence, and Empowering sustainable progress. This study provides an additional component to the traditional triple bottom line by suggesting that internal operation of a company is an emerging idea to understand corporate sustainability.

We study L_2-approximation problems in the weighted Hilbert spaces in the worst case setting. Three interesting weighted Hilbert spaces appear in this paper, whose weights are equipped with two positive parameters $\ga_j$ and $\az_j$ for $j=1,\,2,\dots,\,d$. We consider the worst case error of algorithms that use finitely many arbitrary continuous linear functionals. We discuss the exponential convergence-(t,s)-weak tractability (EC-(t,s)-WT) of these L_2-approximation problems under the absolute or normalized error criterion. In particular, we obtain the sufficient and necessary conditions for EC-(1,1)-WT and EC-(t,1)-WT with t

As modern systems are becoming more complex their control strategy cannot longer relay only on measurement information that usually comes from probes but also from mathematical models. Those systems models can lead to unbearable computation times due to their own complexity, turning the control process non-viable, which leads to the implementation of surrogate models that enable to achieve estimates within acceptable time to take decisions. A control trained with Deep Reinforcement Learning algorithm, using a Physics-Informed Neural Network to obtain the temperature map on the following time step, replaces the need of running Direct Numerical Simulations. On this work we considered an 1D heat conduction problem, which temperature distribution feeds a control system to activate a heat source aiming to obtain a constant, previously defined, temperature value. With this approach, control training becomes much faster without the need of performing numerical simulations or laboratory measurements, as well the control is taken based on Neural Network enabling its implementation on simple processors to edge computing.

In this paper, we prove the existence of three solutions for a $p$-Kirchhoff problem with $\psi$-Hilfer fractional derivative. To be more precise, we use the variational method and we prove that the associated functional energy admits a critical point in each of the three constructed sets, these critical points are weak solutions for a studied problem. Moreover, by definition of these sets, one of these solutions is positive, the second is negative, and the third one change sign. At the end of this work, we present an example to validate our main results.

Pneumatic nebulizers (as variations based on the Collison nebulizer) have been widely used for producing fine aerosol droplets from a liquid material. The basic working principle of those nebulizers has been qualitatively described as utilization of the negative pressure associated with an expanding gas jet to syphon liquid into the jet stream, then to blow and shear into liquid sheets, filaments, and eventually droplets. Detailed quantitative analysis based on fluid mechanics theory is desirable, to gain in-depth understanding of the liquid aspiration mechanism among other aspects of the Collison nebulizer behaviour. The purpose of present work is to investigate the nature of negative pressure distribution associated with compressible gas jet flow in the Collison nebulizer by a computational fluid dynamics (CFD) analysis, using an OpenFOAM® compressible flow solver. The value of the negative pressure associated with a gas jet flow is examined by varying geometric parameters of the jet expansion channel adjacent to the outlet of jet orifice. Such an analysis can provide valuable insights into fundamental mechanisms for liquid aspiration, helpful for designing improved pneumatic atomizer in the Aerosol Jet® direct-write system for micro-feature, high-aspect-ratio material deposition

The Affordable Care Act (ACA) is at the crossroads. It is important to evaluate the effectiveness of the ACA in order to make rational decisions about the ongoing healthcare reform, but existing research into its effect on health insurance status in the United States is insufficient and descriptive. Using data from the National Health Interview Surveys from 2009 to 2015, this study examines changes in health insurance status and its determinants before the ACA in 2009, during its partial implementation in 2010–2013, and after its full implementation in 2014 and 2015. The results of trend analysis indicate a significant increase in national health insurance rate from 82.2% in 2009 to 89.4% in 2015. Logistic regression analyses confirm the similar impact of age, gender, race, marital status, nativity, citizenship, education, and poverty on health insurance status before and after the ACA. Despite similar effects across years, controlling for other variables, youth aged 26 or below, the foreign-born, Asians, and other races had a greater probability of gaining health insurance after the ACA than before the ACA; however, the odds of obtaining health insurance for Hispanics and the impoverished rose slightly during the partial implementation of the ACA but somewhat declined after the full implementation of the ACA starting in 2014. These findings should be taken into account by the U.S. government in deciding the fate of the ACA.

In this study, a cholera infection model with the bilinear infection rate is developed by considering the perturbation of the infection rate by the mean-reverting process. First of all, we give the existence of a globally unique positive solution for a stochastic system at arbitrary initial value. On this basis, the sufficient condition for the model to have an ergodic stationary distribution is given by constructing proper Lyapunov functions and tight sets. This indicates in a biological sense the long-term persistence of cholera infection. Furthermore, after transforming the stochastic model to an relevant linearised system, an accurate expression for the probability density function of the stochastic model around a quasi-endemic equilibrium is derived. Subsequently, the sufficient condition to make the disease extinct is also derived. Eventually, the theoretical findings are shown by numerical simulations. Numerical simulations show that the impact of regression speed and fluctuation intensity on stochastic systems.

The role played by non extensive thermodynamics in physical systems has been under intense debate for the last decades. With many applications in several areas, the Tsallis statistics has been discussed in details in many works and triggered an interesting discussion on the most deep meaning of entropy and its role in complex systems. Some possible mechanisms that could give rise to non extensive statistics have been formulated along the last several years, in particular a fractal structure in thermodynamics functions was recently proposed as a possible origin for non extensive statistics in physical systems. In the present work we investigate the properties of such fractal thermodynamical system and propose a diagrammatic method for calculations of relevant quantities related to such system. It is shown that a system with the fractal structure described here presents temperature fluctuation following an Euler Gamma Function, in accordance with previous works that evidenced the connections between those fluctuations and Tsallis statistics. Finally, the fractal scale invariance is discussed in terms of the Callan-Symanzik Equation.

Counter-current spontaneous imbibition of water is a critical oil-recovery mechanism. In the laboratory, the Amott test is a widely used technique to assess efficacy of brine imbibition into oil-saturated core plugs. The classic Amott-cell methodology was designed to estimate ultimate oil recovery, but not the recovery dynamics that provides essential information about the imbibition mechanisms. Retention of oil droplets at the external core surface and delayed onset of oil production are the two key artifacts of the classic Amott experiment. This retention, referred to here as the ``external-surface oil holdup effect'', often results in stepwise recovery curves that obscure true dynamics of spontaneous imbibition. To address these holdup drawbacks of the classic Amott method, we modified the Amott cell and experimental procedure. For the first time, using water-wet Indiana limestone cores saturated with brine and mineral oil, we showed that our improvements of the Amott method enabled accurate and reproducible measurements of oil-recovery dynamics. We used the Generalized Extreme Value (GEV) statistics to describe oil-production histories from water-wet heterogeneous limestone cores with finite initial water saturations, for the first time. We demonstrated that our four-parameter GEV model accurately described the recovery dynamics, and that optimal GEV parameter values systematically reflected the key characteristics of the oil-rock system, such as oil viscosity and rock permeability. These findings gave us a more fundamental understanding of spontaneous, counter-current imbibition mechanisms and insights into what constitutes a predictive model of counter-current water imbibition into oil-saturated rocks with finite initial water saturation.

We explored the statistics of filtering of simple patterns on a number of deterministic and random graphs as a tractable simple example of information processing in complex systems. In this problem, multiple inputs map to the same output, and the statistics of filtering is represented by the distribution of this degeneracy. For a few simple filter patterns on a ring we obtained an exact solution of the problem and described numerically more difficult filter setups. For each of the filter patterns and networks we found a few numbers essentially describing the statistics of filtering and compared them for different networks. Our results for networks with diverse architectures appear to be essentially determined by two factors: whether the graphs structure is deterministic or random, and the vertex degree. We find that filtering in random graphs produces a much richer statistics than in deterministic graphs. This statistical richness is reduced by increasing the graph’s degree.

The systematic sampling is used as a method to get the quantitative results from the tissues and the radiological images. Systematic sampling on real line (R) is a very attractive method within which the biomedical imaging is consulted by the practitioners. For the systematic sampling on R, the measurement function (MF) is occurred by slicing the three-dimensional object equidistant systematically. The currently used covariogram model in variance approximation proposed by [28,29] is tested for the different measurement functions in a class to see the performance on the variance estimation of systematically sampled R. This study is an extension of [17], and an exact calculation method is proposed to calculate the constant λ(q,N) of confidence interval in the systematic sampling. The exact value of constant λ(q,N) is examined for the different measurement functions as well. As a result, it is observed from the simulation that the proposed MF should be used to check the performances of the variance approximation and the constant λ(q,N). Synthetic data can support the results of real data.

Biomechanics is a branch of biophysics that deals with mechanics applied to biology. The biomechanics of the cornea plays a significant role in managing patients with glaucoma. While evidence suggests a higher risk of glaucoma in patients with thin and stiffer corneas, it also affects the measurement of intraocular pressure (IOP). We reviewed the pertinent literature to help the understanding of corneal biomechanics and how it can help optimize clinical and surgical treatments and a better approach to diagnosing and managing patients with glaucoma.

In the present work, a comparative evaluation of an MBBR unit performance was carried out for the following cases: when adding 3D-printed biocarriers fabricated with 13X and bentonite, when using K1 commercial biocarriers and when not adding biocarriers at all. For the evaluation of the MBBR efficiency, various physicochemical parameters were measured, while static light scattering and optical microscopy observations were additionally used. Finally, biofilm extracted from the biocarriers was evaluated. The findings suggest that there is an optimal biodegradation of the organic load in all MBBR units. The nitrification and denitrification process were improved at the 3D MBBR as compared to the control MBBR and MBBR K1. The dry mass of the biofilm in the 3D-printed biocarriers was two orders of magnitude larger than the one in the K1 biocarriers. What is more, in the K1 biocarriers the mass of the biofilm varied in relation to time, due to the fact that it could not be kept inside the holes, something that was not observed to happen with the 3D-printed biocarriers. Finally, it was observed, mostly in the 3D MBBR and less in the K1 MBBR, that the growth of nitrifying bacteria and heterotrophs inside the units increased the biomass production in the form of SMP, which in turn favored the adhesion of biomass on the surface of biocarriers.

The Solvency II regulatory framework specifies procedures and parameters for determining solvency capital requirements (SCRs) for insurance companies. The proposed standard SCR calculations involve two steps. The Value–at–Risk (VaR) of each risk driver is measured and, in a second step, all components are aggregated to the company’s overall SCR, using the Standard Formula. This formula has two inputs: the VaRs of the individual risk drivers and their correlations. The appropriate calibration of these input parameters has been the purpose of various Quantitative Impact Studies that have been conducted during recent years. This paper demonstrates that the parameter calibration for the equity–risk module—overall, with about 25%, the most significant risk component—is seriously flawed, giving rise to spurious and highly erratic parameter values. As a consequence, an implementation of the Standard Formula with the currently proposed calibration settings for equity–risk is likely to produce inaccurate, biased and, over time, highly erratic capital requirements.

This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset.

This paper presents a quantitative approach to poetry, based on the use of several statistical measures (entropy, informational energy, N-gram, etc.) applied to a few characteristic English writings. We found that English language changes its entropy as time passes, and that entropy depends on the language used and on the author. In order to compare two similar texts, we were able to introduce a statistical method to asses the information entropy between two texts. We also introduced a method of computing the average information conveyed by a group of letters about the next letter in the text. We found a formula for computing the Shannon language entropy and we introduced the concept of N-gram informational energy of a poetry. We also constructed a neural network, which is able to generate Byron-type poetry and to analyze the information proximity to the genuine Byron poetry.

The addition of biocarriers can improve the biological processes appearing in a bioreactor since their surface allows the immobilization, attachment, protection, and growth of microorganisms. In addition, the development of a biofilm layer allows the colonization of microorganisms in the biocarriers. The structure, composition, and roughness of the biocarriers' surface are crucial factors that affect the development of the biofilm. In the current work, the aluminosilicate zeolites 13X and ZSM-5 were examined as the main building components of the biocarrier scaffolds, using bentonite, montmorillonite, and halloysite nanotubes as inorganic binders in various combinations. 3D printing was utilized to form pastes into monoliths that underwent heat treatment. The 3d-printed biocarriers were subjected to a mechanical analysis, including density, compression, and nanoindentation tests. Furthermore, the 3d-printed biocarriers were morphologically and structurally characterized using nitrogen adsorption at 77 K (LN2), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The stress-strain response of the materials being studied was obtained through nanoindentation tests combined with finite element analysis (FEA). These tests were also utilized to simulate the lattice geometries under compression loading conditions to investigate their deformation and stress distribution in relation to experimental compression testing. The results indicated that the 3d-printed biocarrier 13X/halloysite nanotubes was endowed with high specific surface area and extended mesoporous structure. Due to these assets, its bulk density was one of the lowest observed amongst the biocarriers derived from the various combinations of materials. The biocarriers based on the 13X zeolite exhibited the highest mechanical stability and appropriate morphological features. The 13X/halloysite nanotubes scaffold exhibits a moderate hardness value compared to the rest, while it presents the highest value of modulus of elasticity. In conclusion, Aluminosilicate zeolites and their combination with clays and inorganic nanotubes afford 3d-printed biocarriers of various textural and structural properties, which can be utilized to improve biological processes.

Cardiomyopathies are diseases of heart muscle, a significant percentage of which are genetic in origin. Cardiomyopathies can be classified as dilated, hypertrophic, restrictive, arrhythmogenic right ventricular or left ventricular non-compaction, although mixed morphologies are possible. A subset of neuromuscular disorders, notably Duchenne and Becker muscular dystrophies, are also characterized by cardiomyopathy aside from skeletal myopathy. The global burden of cardiomyopathies is certainly high, necessitating further research and novel therapies. Genome editing tools, which include zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) and clustered regularly interspaced short palindromic repeats (CRISPR) systems have emerged as increasingly important technologies in studying this group of cardiovascular disorders. In this review, we discuss the applications of genome editing in the understanding and treatment of cardiomyopathy. We also describe recent advances in genome editing that may help improve these applications, and some future prospects for genome editing in cardiomyopathy treatment.

Local polynomial smoother (LPS) is a weighted local least-squares nonparametric method. It provides a local Taylor series fit of the data at any location and can be directly used in a differential equation to provide a numerical scheme. In this article, we introduce this new nonparametric idea based on local polynomial smoother, for acquiring the numerical solution of the Bagley-Torvik fractional-order differential equations. Furthermore, this paper will present a numerical comparison with some methods, such as legendre operational matrix and pseudo-spectral method. The efficiency and accuracy of the LPS method are demonstrated by two numerical examples.

The impacts of inflation on international finance are generally reflected in the decline of buying power, risks of investment and deposit loss from currency devaluation. For the purpose of optimized allocation and management of asset values, i.e., annual income or research grants, a framework of a three-level model for static financial management is presented to maximize banking interests, bonuses, and the remaining balance after spending necessary expenditures. Meanwhile, in retrospect to higher-level financial inflation, strategies of dynamic investment, i.e., posing regular tests of flipping coins, exploiting variations of the currency exchange rate, as well as applying the theory of broad money, are presented to increase the appreciation rate of net assets in and post the era of COVID-19. Quantitative evaluations in case studies verify the feasibility of improving the financial situation, indicating broader applications for budgeting and investing when US inflation rate is below 3.0%. Besides, combining approaches of static and dynamic strategies, the expected annual growth rate of individual zero-risk asset investment is predicted as within the range of 8.30~9.62% according to budgeted appreciation on net assets in 12 months, which is capable of overcoming negative impacts from higher level annual inflation, i.e., those as highest as 8.01% in the Year 2022.

A popular approach to learning about admixture from population genetic data is by computing the allele-sharing summary statistics known as f-statistics. Compared to some methods in population genetics, f-statistics are relatively simple, but interpreting them can still be complicated at times. In addition, f-statistics can be used to build admixture graphs (multi-population trees allowing for admixture events), which provide more explicit and thorough modeling capabilities but are correspondingly more complex to work with. Here, I discuss some of these issues to provide users of these tools with a basic guide for protocols and procedures. My focus is on the kinds of conclusions that can or cannot be drawn from the results of f₄-statistics and admixture graphs, illustrated with real-world examples involving human populations.

In this paper we consider type II bivariate generalized power series Poisson distribution as a compound Poisson distribution with bivariate generalized power series compounding distribution. We obtain some properties, p.m.f. and conditional distributions. In addition we also give a brief discussion about the multivariate extension of this case. Then we introduce type II bivariate generalized power series Poisson process and consider a bivariate risk model with type II bivariate generalized power series Poisson model as the counting process. For this model we derive distribution of the time to ruin and bounds for the probability of ruin. We obtain partial integro-differential equation for the ruin probabilities and express its bivariate transform through two univariate boundary transforms,where one of the initial capitals is fi xed at zero.

Categorical variables are common in spatial data analysis. Traditional analytical methods for deriving probabilities of class occurrence, such as kriging-family algorithms, have been hindered by the discrete characteristics of categorical fields. This study introduces the theoretical backgrounds of linear Bayesian updating (LBU) approach for spatial classification through expert system. Transition probabilities are interpreted as expert opinions for updating the prior marginal probabilities of categorical response variables. The main objective of this paper is to present the solid theoretical foundations of LBU and provide a categorical random field prediction method which yields relatively higher classification accuracy compared with conventional Markov chain random field (MCRF) approach. A real-world case study has also been carried out to demonstrate the superiority of our method. Since the LBU idea is originated from aggregating expert opinions and not restricted to conditional independent assumption (CIA), it may prove to be reasonably adequate for analyzing complex geospatial data sets, like remote sensing images or area-class maps.

This article introduces a new fractional approach to the concept of information dimension of complex networks, based on a (q,q′)-entropy proposed in the literature. The q parameter measures how far is the number of subsystems (for a given size ε) from the mean number of overall sizes. The q′ (interaction index) measures when the interactions between subsystems are greater (q′&gt;1), lesser (q′&lt;1) or equal to the interactions into these subsystems. The computations of the proposed information dimension are carried out on several real-world and synthetic complex networks. The results from the proposed information dimension are compared with those from the information dimension based on the Shannon entropy.

Leaf temperature (T_leaf) influences photosynthesis and respiration. Currently, there is a growing interest on including lianas in productivity models due to their increasing abundance, and their detrimental effects on net primary productivity in tropical environments. Therefore, understanding the differences of T_leaf between lianas and trees is important for future of forest on whole ecosystem productivity. Here we determined the displayed leaf temperature (T_d= T_leaf – ambient temperature) of several species of lianas and their host trees during ENSO and non-ENSO years to evaluate if the presence of lianas affects the T_d of their host trees, and if leaves of lianas and their host trees exhibit differences in T_d. Our results suggest that close to midday, the presence of lianas does not affect the T_d of their host trees; however, lianas tend to have higher values of T_d than their hosts across seasons, in both ENSO and non-ENSO years. Although lianas and trees tend to have similar physiological-temperature responses, differences in T_d could lead to significant differences in rates of photosynthesis and respiration based temperature response curves. Future models should thus consider differences in leaf temperature between these life forms to achieve robust predictions of productivity.

The fitting and modeling of skewed, complex, symmetric, and asymmetric datasets is an exciting research topic in many fields of applied sciences, notably lifetime, medical, and financial sciences. The Heavy tailed Nadarajah Haghighi model is introduced by compounding the heavy tailed family and Nadarajah Haghighi distribution in this paper. The so obtained model has three parameters accounting for the scale and shape of the distribution. The proposed distribution's fundamental characteristics such as probability density, cumulative distribution, hazard rate, and survival functions are provided, and several key statistical properties are established, and several entropy information measures are proposed. The estimation of model parameter is performed via the ML procedure. Further, different simulation experiments are conducted to demonstrate the performance of proposed estimator’s using some measure, like the average estimate, the average bias, and associated mean square error. Finally, we are applied our proposed model to analyze three different real datasets. In our illustration, we are compared the practicality of the recommended model with several well-known competing models.

Leishmaniasis is the third most common vector-borne disease and a very important protozoan infection. Cutaneous leishmaniasis is one of the most common types of leishmaniasis infectious diseases with up to 2 million occurrences of new cases each year worldwide. A dynamic transmission multivariate time series model was applied to the data to account for overdispersion and evaluate the effects of three environmental layers as well as seasonality in the data. Furthermore, ecological niche modeling was used to investigate the geographical suitable conditions for cutaneous leishmaniasis using temperature, precipitation and altitude as environmental layers, together with the leishmaniasis presence data. A retrospective analysis of the cutaneous leishmaniasis spatial data in Afghanistan between 2003 and 2009 indicates a steady increase from 2003 to 2007, a small decrease in 2008, then another increase in 2009. An upward trend and regularly repeating patterns of highs and lows was observed related to the months of the year which suggests seasonality effect in the data. Two peaks were observed in the disease occurrence-- January to March and September to December -- which coincide with the cold period. Ecological niche modelling indicates that precipitation has the greatest contribution to the potential distribution of leishmaniasis.

Let G be a finite group. The prime graph Γ(G) of G is defined as follows: The set of vertices of Γ(G) is the set of prime divisors of |G| and two distinct vertices p and p' are connected in Γ(G), whenever G has an element of order pp'. A non-abelian simple group P is called recognizable by prime graph if for any finite group G with Γ(G)=Γ(P), G has a composition factor isomorphic to P. In [4] proved finite simple groups ²D_n(q), where n ≠ 4k are quasirecognizable by prime graph. Now in this paper we discuss the quasirecognizability by prime graph of the simple groups ²D_2k(q), where k ≥ 9 and q is a prime power less than 10⁵.

To estimate the degree of quantum entanglement, it is important to understand the statistical behavior of functions of spectrum of density matrices such as von Neumann entropy, quantum purity, and entanglement capacity. These entangle- ment metrics over different generic state ensembles have been studied intensively in the literature. As an alternative metric, in this work we study sum of square root spectrum of density matrices, which is relevant to negativity and fidelity in quantum information processing. In particular, we derive the exact mean and vari- ance of sum of square root spectrum over the Bures-Hall generic state ensemble extending known results obtained recently over the Hilbert-Schmidt ensemble.

The Thermodynamic Formalism provides a rigorous mathematical framework to study quantitative and qualitative aspects of dynamical systems. At its core there is a variational principle and corresponding, in its simplest form, to the Maximum Entropy principle, used as a statistical inference procedure to represent, by specific probability measures (Gibbs measures), the collective behaviour of complex systems. This framework has found applications in different domains of scienThe Thermodynamic Formalism provides a rigorous mathematical framework to study quantitative and qualitative aspects of dynamical systems. At its core there is a variational principle and corresponding, in its simplest form, to the Maximum Entropy principle, used as a statistical inference procedure to represent, by specific probability measures (Gibbs measures), the collective behaviour of complex systems. This framework has found applications in different domains of science, in particular, has been fruitful and influential in neurosciences. In this article, we review how the Thermodynamic Formalism can be exploited in the field of theoretical neuroscience, as a conceptual and operational tool, to link the dynamics of interacting neurons and the statistics of action potentials from either experimental data or mathematical models. We comment on perspectives and open problems in theoretical neuroscience that could be addressed within this formalism.ce, in particular, has been fruitful and influential in neurosciences. In this article, we review how the Thermodynamic Formalism can be exploited in the field of theoretical neuroscience, as a conceptual and operational tool, to link the dynamics of interacting neurons and the statistics of action potentials from either experimental data or mathematical models. We comment on perspectives and open problems in theoretical neuroscience that could be addressed within this formalism.

The application of graph theory in chemical and molecular structure research far exceeds people's expectations, and it has recently grown exponentially. In the molecular graph, atoms are represented by vertices and bonded by edges. In this report, we study the silicon-carbon Si₂C₃-I[p,q] and Si₂C₃-II[p,q]. We compute several Zagreb polynomials and Redefined Zagreb indices of these silicon-carbons.

Hybrid forecasting methods have emerged as a solution surpassing the limitations of both statistical and deep learning approaches. While the first emphasize the significance of variables, they often produce worse forecasting results when compared to newer techniques. In contrast, deep learning models remain enigmatic "black boxes" in terms of interpretability, although achieving better results in forecasting. This article introduces the Comprehensive Cross-Correlation and Lagged Linear Regression Deep Learning (CCLR-DL) framework, designed to harness the best of both approaches, enhancing forecasting accuracy while retaining model interpretability through a feature selection process. CCLR-DL blens cross-correlation analysis, lagged multiple linear regression and granger's causality procedures with deep learning architectures based on LSTM. In a practical demonstration, CCLR-DL was applied to a real database of clinical visits associated to diagnoses in Catalonia, Spain (tracking a population of 6.3 million patients during 10 years). Predicting visits enables the healthcare managers to be ready for future demand shifts. Results demonstrate a consistent and substantial improvement over standalone statistical and deep learning methods when predicting healthcare demand. This hybrid approach not only showcases its efficacy but also offers a promising solution to the challenge of balancing predictive accuracy with model explicability. In this context, this work aims to design and validate a method for feature selection and forecasting of multivariate high dimensional time series datasets not only to improve prediction accuracy but also to model transparency by identifying a subset of variables that improve predictions and G-cause the target variable.

This study considers an application of the first-order Grey Markov Model to foresee the values of Italian power generation in relation to the available energy sources. The model is used to fit data from the Italian energy system from 2000 to 2022. The integration of Markovian error introduces a random element to the model, which is able now to capture inherent uncertainties and misalignments between the Grey model predictions and the real data. This application provides valuable insights for strategic planning in the energy sector and future developments. The results show good accuracy of the predictions, which provides powerful information for the effective implementation of energy policies concerning the evolution of energy demand in the country. Despite advancements, Italy’s 2032 energy mix will still significantly rely on fossil fuels, emphasizing the need for sustained efforts beyond 2032 to enhance sustainability.

Panel data of our interest consist of a moderate number of panels, while the panels contain a small number of observations. An estimator of common breaks in panel means without a boundary issue for this kind of scenario is proposed. In particular, the novel estimator is able to detect a common break point even when the change happens immediately after the first time point or just before the last observation period. Another advantage of the elaborated change point estimator is that it results in the last observation in situations with no structural breaks. The consistency of the change point estimator in panel data is established. The results are illustrated through a simulation study. As a by-product of the developed estimation technique, a theoretical utilization for correlation structure estimation, hypothesis testing, and bootstrapping in panel data is demonstrated. A practical application to non-life insurance is presented as well.

This article aims to identify the generating function of modiÂ…ed Apostol type q-Bernoulli polynomials. With the aid of this generating function, some properties of modiÂ…ed Apostol type q-Bernoulli polynomials are given. It is shown that aforementioned polynomials are q-Appell. Hence, we make use of these polynomials to have applications on q-Umbral calculus. From those applications, we derive some theorems in order to get Apostol type modiÂ…ed q-Bernoulli polynomials as a linear combination of some known polynomials which we stated in the paper.

We consider the financial planning problem of a retiree wishing to enter a retirement village at a future uncertain date. The date of entry is determined by the retiree's utility and bequest maximisation problem within the context of uncertain future health states. In addition, the retiree must choose optimal consumption, investment, bequest and purchase of insurance products prior to her full annuitisation on entry to the retirement village. A hyperbolic absolute risk-aversion (HARA) utility function is used to allow necessary consumption for basic living and medical costs. The retirement village will typically require an initial deposit upon entry. This threshold wealth requirement leads to exercising the replication of an American put option at the uncertain stopping time. From our numerical results, active insurance and annuity markets are shown to be a critical aspect in retirement planning.

The late universe contains a wealth of information about fundamental physics and gravity, wrapped up in non-Gaussian fields. To make use of as much information as possible it is necessary to go beyond two-point statistics. Rather than going to higher order N-point correlation functions, we demonstrate that the probability distribution function (PDF) of spheres in the matter field (a one-point function) already contains a significant amount of this non-Gaussian information. The matter PDF dissects different density environments which are lumped together in two-point statistics, making it particularly useful for probing modifications of gravity or expansion history. Our approach in Cataneo et. al. 2021 extends the success of Large Deviation Theory for predicting the matter PDF in ΛCDM in these “extended” cosmologies. A Fisher forecast demonstrates the information content in the matter PDF via constraints for a Euclid-like survey volume combining the 3D matter PDF with the 3D matter power spectrum. Adding the matter PDF halves the uncertainties on parameters in an evolving dark energy model, relative to the power spectrum alone. Additionally, the matter PDF contains enough non-linear information to substantially increase the detection significance of departures from General Relativity, with improvements up to six times the power spectrum alone. This analysis demonstrates that the matter PDF is a promising non-Gaussian statistic for extracting cosmological information, particularly for beyond ΛCDM models.

Background: In the present study, cisplatin, artemisinin and oleanolic acid were evaluated alone and in combination, on human ovarian A2780, A2780ZD0473R and A2780cisR cancer cell lines with aim of overcoming cisplatin resistance and side effects. Methods: Cytotoxicity was assessed by MTT reduction assay. CI values were used as a measure of combined drug effect. MALDI TOF/TOF MS/MS and 2-DE gel electrophoresis were used to identify protein biomarkers in ovarian cancer and to evaluate combination effects. Results: Synergism from combinations was dependent on concentration and sequence of administration. Generally, bolus was most synergistic. 49 proteins differently expressed by 2 ≥ fold were: CYPA, EIF5A1, Op18, p18, LDHB, P4HB, HSP7C, GRP94, ERp57, mortalin, IMMT, CLIC1, NM23, PSA3,1433Z, and HSP90B were down-regulated, whereas hnRNPA1, hnRNPA2/B1, EF2, GOT1, EF1A1, VIME, BIP, ATP5H, APG2, VINC, KPYM, RAN, PSA7, TPI, PGK1, ACTG and VDAC1 were up-regulated, while TCPA, TCPH, TCPB, PRDX6, EF1G, ATPA, ENOA, PRDX1, MCM7, GBLP, PSAT, Hop, EFTU, PGAM1, SERA and CAH2 were not-expressed in A2780cisR cells. The proteins were found to play critical roles in cell cycle regulation, metabolism and biosynthetic processes and drug resistance and detoxification. Conclusion: Results indicate that appropriately sequenced combinations of cisplatin with ART and OA may provide a means to reduce side effects and circumvent platinum resistance.

The asymptotic behavior of the analytic solutions of a family of singularly perturbed q-difference-differential equations in the complex domain is studied. Different asymptotic expansions with respect to the perturbation parameter and to the time variable are provided: one of Gevrey nature, and another of mixed type Gevrey and q-Gevrey. This asymptotic phenomena is observed due to the modification of the norm established on the space of coefficients of the formal solution. The techniques used are based on the adequate path deformation of the difference of two analytic solutions, and the application of several versions of Ramis-Sibuya theorem.

Electronic designers nowadays are facing two challenging demands for various applications: miniaturization and increased functionality. To satisfy these seemingly opposed requirements, reducing the number of mounted components—and thus solder joints—in PCB designs becomes an attractive approach by directly printing passive components such as embedded resistors into the circuit. This approach can also potentially increase the reliability, such as “mean time between failures” (MTBF), while reducing the circuit board size. With its unique capabilities for non-contact precision material deposition, the Aerosol Jet® direct-write technology has been enabling additive manufacturing of fine-feature electronics conformally onto flexible substrates of complicated shapes. The CAD/CAM controlled relative motions between substrate and print head allows convenient adjustment of the pattern and pile height of deposited material at a given ink volumetric deposition rate. To date in the printed electronics industry, additively printing embedded polymer-thick-film (PTF) resistors has mostly been done with screen printing using carbon-based paste inks. Here we demonstrate results of Aerosol Jet® printed PTF resistors of resistance values ranging from ~50 W to > 1 kW, adjustable (among several variable parameters) by the number of stacked layers (or print passes with each pass depositing a fixed amount of ink) between contact pads of around 1 mm apart with footprint line typically < 0.3 mm. In principle, any ink material that can be atomized into fine droplets of 1 to 5 microns can be printed with the Aerosol Jet® system. However, the print quality such as line edge cleanliness can significantly influenced by ink rheology which involves solvent volatility, solids loading, and so on. Our atomizable carbon ink was made by simply diluting a screen printing paste with a compatible solvent of reasonable volatility, which can be cured at temperatures below 200 ^oC. We show that Aerosol Jet® printed overlapping lines can be stacked to large pile height (to reduce the resistance value) without significant increase of line width, which enables fabricating embedded resistors with adjustable resistance values in a limited footprint space.

Duchenne muscular dystrophy (DMD) is a fatal X-linked recessive neuromuscular disease prevalent in 1 in 3500 to 5000 males worldwide. As a result of mutations that interrupt the reading frame of the dystrophin gene (DMD), DMD is characterized by a loss of dystrophin protein which leads to decreased muscle membrane integrity, which increases susceptibility to degeneration. CRISPR/Cas9 technology has garnered interest as an avenue for DMD therapy due to its potential for permanent exon skipping, which can restore the disrupted DMD reading frame in DMD and lead to dystrophin restoration. An RNA-guided DNA endonuclease system, CRISPR/Cas9 allows for the targeted editing of specific sequences in the genome. The efficacy and safety of CRISPR/Cas9 as a therapy for DMD has been evaluated by numerous studies in vitro and in vivo, with varying rates of success. Despite the potential of CRISPR/Cas9-mediated gene editing for the long-term treatment of DMD, its translation into the clinic is currently challenged by issues such as off-targeting, immune response activation, and sub-optimal in vivo delivery. Its nature as being mostly a personalized form of therapy also limits applicability to DMD patients, who exhibit a wide spectrum of mutations. This review summarizes the various CRISPR/Cas9 strategies that have been tested in vitro and in vivo for the treatment of DMD. Perspectives on the approach will be provided, and the challenges faced by CRISPR/Cas9 in its road to the clinic will be briefly discussed.

The application of graph theory in chemical and molecular structure research far exceeds people's expectations, and it has recently grown exponentially. In the molecular graph, atoms are represented by vertices and bonded by edges. Closed forms of multiplicative degree-based topological indices which are numerical parameters of the structure and determine physico-chemical properties of the concerned molecular compound. In this article, we compute and analyze many multiplicative degree-based topological indices of silicon-carbon Si₂C₃-I[p,q] and Si₂C₃-II[p,q].

The goal of this work is to develop a sustainable value chain of carbonaceous adsorbents that can be produced from the solid fibrous digestate (SFD) of biogas plants and further applied in integrated desulphurisation-upgrading (CO2/CH4 separation) processes of biogas to yield high purity biomethane. On this purpose, physical and chemical activation of the SFD derived biochar was optimised to afford micro-mesoporous activated carbons (ACs) of high BET surface area (590-2300 m2g−1) and enhanced pore volume (0.57-1.0 cm3g−1). Gas breakthrough experiments from fixed bed columns of the obtained ACs, using real biogas mixture as feedstock, unveiled that the physical and chemical activation conclude to different types of ACs which are sufficient for biogas upgrade and biogas desulphurisation respectively. Performing breakthrough experiments at three temperatures close to ambient it was possible to define the optimum conditions for enhanced H2S/CO2 separation. It was also concluded that the H2S adsorption capacity is significantly affected by restriction to gas diffusion. Hence, the best performance was obtained at 50 oC and the maximum observed in the H2S adsorption capacity vs the temperature is attributed to the counterbalance between adsorption and diffusion processes.

With the increasing use of data-driven modeling methods new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. Literature shows the advantages of data-driven methods reduce computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies’ aptitude for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation data sets, with Gaussian Process Regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine.

A family of singularly perturbed q-difference-differential equations under the action of a small complex perturbation parameter is studied. The action of the formal monodromy around the origin is present in the equation, which suggests the construction of holomorphic solutions holding logarithmic terms in both, the formal and the analytic level. We provide both solutions and describe the asymptotic behavior relating them by means of q-gevrey asymptotic expansions of some positive order, with respect to the perturbation parameter. On the way, the development of a space product of Banach spaces in the Borel plane is needed to provide a fixed point for a coupled system of equations.

Deciding when to replace dairy bulls presents a complex challenge for artificial insemination (AI) companies. These decisions encompass multiple factors, including a bull's age, predicted semen production, and estimated genetic merit. This study's purpose was to provide a practical, objective tool to assist in these decisions. We utilized a Markov Chain model to calculate the economic valuation of dairy bulls, incorporating key factors such as housing costs, collection and marketing expenses, and the bull's probable tenure in the herd. Data from a leading AI company were used to establish baseline values. The model further compared a bull's net present value to that of a potential young replacement, establishing a relative valuation (BullVal$). The range of BullVal$ observed spanned from -$316,748 to $497,710. Interestingly, the model recommended culling for 49% of the bulls based on negative BullVal$. It was found that a bull's net present value was primarily influenced by market allocation and pricing, coupled with the interaction of semen production and genetic merit. This study offers a robust, data-driven model to guide bull replacement decisions in AI companies. Key determinants of a bull's valuation included market dynamics, semen production rates, and genetic merit.

SStatistical characteristics of the wind speed in Samaria region of Israel have been analyzed by processing 11 years of wind data provided by the Israeli Meteorological Service, recorded at 10 m height above the ground. The cumulative mean wind speed at measurement height was shown to be 4.53 m/s with standard deviation of 2.32 m/s. Prevailing wind direction is shown to be char-acterized by cumulative mean azimuth of 226° with standard deviation of 79.76°. The results were extrapolated to 70-meter height in order to estimate wind characteristics at hub height of a me-dium-scale wind turbine. Moreover, Weibull distribution parameters were calculated annually, monthly and seasonally, demonstrating a good match with histogram-based statistical repre-sentations. Shape parameter of the Weibull distribution was shown to reside within a narrow range of 1.93 to 2.15, allowing us to assume a Rayleigh distribution, thus simplifying wind tur-bines energy yield calculations. The novelty of the current paper is related to gathering wind statistics for a certain area (Samaria) we are not aware of any published statistics regarding wind velocity and direction in this area. The data may be interesting for potential regional wind energy development in which the obtained Weibull distribution can be used in calculations of expected power generation of particular turbines with known power dependence on velocity. We also point out that the fact that realistic wind velocity statistics is well described by an analytic formula (Weibull distribution) is not trivial, and in fact the fit may have been poor.

Over the last few years, Earth Observation (EO) data has shifted towards increased use to produce official statistics, particularly in the agriculture sector. National statistics offices worldwide, including in Asia and the Pacific, are expanding their use of EO data to produce agricultural statistics such as crop classification, yield estimation, irrigation mapping, and crop loss estimation. The advances in image classification, such as pixel-based and phenology-based classifications, and machine learning create new opportunities for researchers to analyze EO data applied to agriculture statistics. However, it requires the ground truth (GT) data because classification result mainly depends on the quality of GT. Therefore, in this study, we introduced a random sampling approach to design and collect GT data using EO imagery and ancillary data. As a result of data collection, GT data improve the algorithms and validates classification results. Nevertheless, despite the importance of GT data, they are rarely disseminated as a data product in themselves. Thus, this results in an untapped opportunity to share GT data as a global public good, and improved use of survey and census data as a source of GT data.

The interest in random fiber lasers (RFLs), first demonstrated one decade ago, is still growing and their basic characteristics have been studied by several authors. RFLs are open systems that present instabilities in the intensity fluctuations due to the energy exchange among their non-orthogonal quasi-modes. In this work, we present a review of the recent investigations on the output characteristics of a continuous-wave erbium-doped RFL, with emphasis on the statistical behavior of the emitted intensity fluctuations. A progression from the Gaussian to Lévy and back to the Gaussian statistical regime was observed by increasing the excitation laser power from below to above the RFL threshold. By analyzing the RFL output intensity fluctuations, the probability density function of emission intensities was determined, and its correspondence with the experimental results was identified, enabling a clear demonstration of the analogy between the RFL phenomenon and the spin-glass phase transition. A replica-symmetry-breaking phase above the RFL threshold was characterized and the glassy behavior of the emitted light was established. We also discuss perspectives for future investigations on RFL systems.

The COVID-19 pandemics has had an unprecedented global impact, and the COVID-19 mass vaccination campaign has been commonly regarded as crucial to overcome the pandemics. Since all-cause mortality is the best way to measure the consequences of a health intervention, the present study was devised to analyze the all-cause mortality data of the United Kingdom (UK), which are made publicly available broken down by vaccination status. Data from April 2021 to May 2023 were retrospectively collected and analyzed according to age groups and vaccination status and the Standardized Mortality Ratio (SMR) for all-cause mortality and for non-COVID-19 were calculated in comparison to the corresponding unvaccinated groups. Results show that, since an age group dependent date, the all-cause mortality SMRs were increasing in any of the age groups considered. Initially, the all-causes death SMRs were less than 1 for all age groups, but due to their growth, for the age groups 18-39, 80-89 and 90+ years by a certain date they exceeded the reference value. For the other age groups, as long as the trend is maintained, it is possible to predict the date in which the SMR would reach the value 1. Non-COVID-19 SMR values show a very similar trend. Their initial values much lower than 1 suggest the presence of significant biases in the ONS dataset which lead to an underestimation of the risks for those vaccinated, as it is not plausible that the vaccine protects from causes other than COVID-19. The finding that all-cause mortality SMRs in vaccinated in comparison to unvaccinated people increases over time requires careful examination to understand the underlying factors. Meanwhile, the other major countries should undertake a systematic collection of all-causes mortality broken down by vaccination status, and should be implemented a moratorium of the promotion of mass vaccination campaigns.

In this paper, we study the p-adic integral representation on Z_p of q-Bernoulli numbers arising from two variable q-Bernstein polynomials and investigate some properties for the q-Bernoulli numbers. In addition, we give some new identities of q-Bernoulli numbers.

This article seeks to provide an improved and more comprehensive understanding of the concept and theories on wasteland. It achieves this by focusing on the Indian context allowing us to unpack the importance of including multiple perspectives of wasteland narratives; this means including more positive narratives of the potential of wasteland to inform and improve prospects for land policies in the global south. Wasteland is commonly recognized as an underutilized category of land that may transform into a valuable resource base with proper management measures. The term waste has multiple angles that carry different notions ranging from fallow to agroforestry land in the global south and Brownfield to green space in the global north. We conduct a narra-tive review approach to qualitatively analyze the concept wastelands studied in pre-existing lit-erature from 1970 to present. The unsystematic literature review approach incorporates multiple elements of wasteland discourse, like understanding the meaning of the term on a global scale, setting out the meaning of the term waste into multiple perspectives explicitly in the Indian con-text, along with different classes and management approaches of wasteland from a national per-spective. The multiple perspectives of wasteland not only generate misconception on land resource but spawn difficulties in land use policy particularly for Indian scenario. For sustainable land use policy, reclaiming wasteland would be the best possible way for India and other countries in Global South, which requires a comprehensive methodological overview on wasteland narrative.

Asymptotic theories for fractional cointegrations have been extensively studied in the context of time series data, with numerous empirical studies and tests developed. However, most of the previously developed testing procedures for fractional cointegration were primarily designed for time series data. This paper proposes a generalized residual-based test for fractionally cointegrated panels with fixed effects. The test development is based on bivariate panel series with the regressor assumed to be fixed across cross-sectional units. The proposed test procedure accommodates any integration order between [0,1], and it is asymptotically normal under the null hypothesis. Monte Carlo experiments demonstrate that the test exhibits better size and power compared to a similar residual-based test across varying sample sizes.

Background: Autism spectrum disorder (ASD) is a persistent neurodevelopment disorder frequently co-occurring with Attention-Deficit/Hyperactivity Disorder (ADHD) and behavior-related disorders. While behavioral therapy is the first-line option to manage ASD core symptoms, pharmacological therapy is sometimes needed to treat acute problems, such as agitation and aggressive behaviors. Recent guidelines recommend neuroleptics to reduce psychomotor agitation in people with ASD. However, since children with ASD are often drug-resistant, alternative treatments are often justified. Reports from literature showed that intra-venous valproate (IV-VPA) can be effective in reducing agitation in psychiatric patients, with lower frequency of adverse events compared to convention-al treatments. However, since findings are occasionally inconsistent, IV-VPA is not an approved option in clinical psychiatry. We aim at improving the knowledge on the IV-VPA treatment option in emergency psychiatry in the pediatric age. Methods: We report the case of a 11-year-old boy suffering from a complex neurodevelopmental condition, who experienced a psychotic episode with severe aggressive and disruptive behaviors, successfully treated with IV-VPA. Besides, we conducted an up-dated literature review on this topic. Conclusion: In our case, first-line therapies proved to be ineffective. On the contrary, IV-VPA led to a safe and prompt clinical success, in line with other reports. Based on our literature review, IV-VPA can be highly effective and can reduce the risk of adverse events that frequently occur with the use of high-dose standard medications in emergency psychiatry.

The main purpose of this paper is to introduce a new class of $q$-Hermite-Fubini numbers and polynomials by combining the $q$-Hermite polynomials and $q$-Fubini polynomials. By using generating functions for these numbers and polynomials, we derive some alternative summation formulas including powers of consecutive $q$-integers. Also, we establish some relationships for $q$-Hermite-Fubini polynomials associated with $q$-Bernoulli polynomials, $q$-Euler polynomials and $q$-Genocchi polynomials and $q$-Stirling numbers of the second kind.

In this paper, we investigate a new $q$-analogue of the higher order degenerate Changhee polynomials and numbers, which are called the Witt-type formula for the $q$-analogue of degenerate Changhee polynomials of order $r$. We can derive some new interesting identities related to the degenerate $(h,q)$-Changhee polynomials and numbers.

In this paper, we investigate a new $q$-analogue of the higher order degenerate Changhee polynomials and numbers, which are called the Witt-type formula for the $q$-analogue of degenerate Changhee polynomials of order $r$. We can derive some new interesting identities related to the degenerate $(h,q)$-Changhee polynomials and numbers.

In the present paper, we introduce (p, q)-extension of Apostol type Frobenius-Euler polynomials and numbers and investigate some basic identities and properties for these polynomials and numbers, including addition theorems, difference equations, derivative properties, recurrence relations and so on. Then, we provide integral representations, explicit formulas and relations for these polynomials and numbers. Moreover, we discover (p, q)-extensions of Carlitz's result [L. Carlitz, Mat. Mag., 32 (1959), 247–260] and Srivastava and Pintér addition theorems in [H. M. Srivastava, A. Pinter, Appl. Math. Lett., 17 (2004), 375–380].

Here, we describe the bicomplex (p,q)- Fibonacci numbers and the bicomplex (p,q)- Fibonacci quaternions that are based on these numbers and give some of their equations, including the Binet formula, generating function, Catalan, Cassini, d’Ocagne’s identities, and some summation formulas for both of them. Finally, we create a matrix for bicomplex (p,q)- Fibonacci quaternions, and we obtain a determinant of a special matrix that gives the terms of that quaternion.

We propose a notion of $(q,\sigma,\tau)$-differential graded algebra, which generalizes the notions of $(\sigma,\tau)$-differential graded algebra and $q$-differential graded algebra. We construct two examples of $(q,\sigma,\tau)$-differential graded algebra, where the first one is constructed by means of generalized Clifford algebra with two generators (reduced quantum plane), where we use a $(\sigma,\tau)$-twisted graded $q$-commutator. In order to construct the second example, we introduce a notion of $(\sigma,\tau)$-pre-cosimplicial algebra.

In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator, and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

This paper is motivated by spatio-temporal pattern in the occurrence of Leishmaniasis in Afghanistan and the relatively high number of zero counts. We hold the view that correlations that arise from spatial and temporal sources are inherently distinct. Our method decouples these two sources of correlations, there are at least two advantages in taking this approach. First, it circumvents the need to inverting a large correlation matrix, which is a commonly encountered problem in spatio-temporal analyses. Second, it simplifies the modelling of complex relationships such as anisotropy, which would have been extremely difficult or impossible if spatio-temporal correlations were simultaneously considered. We identify three challenges in the modelling of a spatio-temporal process: (1) accommodation of covariances that arise from spatial and temporal sources; (2) choosing the correct covariance structure and (3) extending to situations where a covariance is not the natural measure of association. Moreover, because the data covers a period that overlaps with the US invasion of Afghanistan, the high number of zero counts may be the result of no disease incidence or lapse of data collection. To resolve this issue, a model truncated at zero built on a foundation of the generalized estimating equations was proposed.

Porous α-Al2O3 ceramics is a highly demanded material for multitude applications such as filters, substrates, biomedicine materials, etc. Despite the availability of the raw materials, a challenge of their technology is in a high energy budget caused by the sintering above 1500 °C. For a cold sintering processing (CSP) of ceramics, the lowering of α-Al2O3 sintering temperature is one of the most urgent challenges at the background of its rapid development. The current paper for the first time demonstrates the solution of the mentioned problem by the CSP of α-alumina ceramics in a presence of pure water as a transient liquid. The manufactured materials were examined by XRD analysis; the evolution of their microstructures during the CSP was revealed by SEM, and the porosity evaluated by the Archimedes method. The ceramics with an open porosity up to 36 % was produced at 380-450 °C, 220 MPa in 30 min. An increase in the pressure was found to impede α-Al2O3 formation from γ-AlOOH. The development of the microstructure was discussed within the frameworks of the dissolution-precipitation model and the homogenous nucleation. The results of the SEM study pointed to the coalescence of γ-AlOOH grains during the CSP.

This paper presents a statistical approach to identify the underlying roughness characteristics in synthetic aperture radar (SAR) intensity data. The physical modeling of this kind of data allows the use of the Gamma distribution in the presence of fully-developed speckle, i.e., when there are infinitely many independent backscatterers per resolution cell, and none dominates the return. Such areas are often called ``homogeneous'' or ``textureless'' regions. The GI0 distribution is also a widely accepted law for heterogeneous and extremely heterogeneous regions, i.e., areas where the fully-developed speckle hypotheses do not hold. We propose three test statistics to distinguish between homogeneous and inhomogeneous regions, i.e., between gamma and GI0 distributed data, both with a known number of looks. The first test statistic uses a bootstrapped non-parametric estimator of Shannon entropy, providing a robust assessment in uncertain distributional assumptions. The second test uses the classical coefficient of variation (CV). The third test uses an alternative form of estimating the CV based on the ratio of the mean absolute deviation from the median to the median. We apply our test statistic to create maps of \(p\)-values for the homogeneity hypothesis. Finally, we show that our proposal, the entropy-based test, outperforms existing methods, such as the classical CV and its alternative variant, in identifying heterogeneity when applied to both simulated and actual data.

Smallsats are becoming the predominant electro-optical Earth observation (EO) imaging platforms. While atmospheric correction of smallsat data enhances its utility, an established pathway to do so may constrain accuracy and utility. The alternative, Closed-form Method for Atmospheric Correction (CMAC), developed for smallsat application provides surface reflectance derived solely from scene statistics. In a prior paper, CMAC closely agreed with Land Surface Reflectance Code (LaSRC) software for correction of the four VNIR bands of Landsat-8/9 images for conditions of low to moderate atmospheric effect over quasi-invariant warehouse-industrial targets. Those results were accepted as surrogate surface reflectance to support analysis of CMAC and LaSRC reliability for surface reflectance retrieval in two contrasting environments: shortgrass prairie and barren desert. Reliability was defined and tested through a null hypothesis: the same top-of-atmosphere reflectance under the same atmospheric condition will provide the same estimate of surface reflectance. Evaluated against the prior surrogate surface reflectance, the results found decreasing error with increasing wavelength for both methods. From 58 comparisons across the four bands, LaSRC average absolute error ranged from 0.59% (NIR) to 50.30% (blue). CMAC error was well constrained from 0.01% (NIR) to 0.98% (blue) sustaining the null hypothesis for reliability.

People often make mistakes, so we try to automate every aspect of our lives. Sports is no exception. While just over a decade ago humans were analyzing games, today this is being done by artificial intelligence. Due to rapid development over the past decade, neural networks are now faster, more accurate, and in some areas even better than their human counterparts. In this paper, we present an algorithm that can detect player statistics during an NBA broadcast. It also helps users better understand the game and the use of augmented reality. The algorithm detects players on the court, tracks their movements, and assigns them to their respective teams. Using homography estimation, we transform the players’ positions from a three-dimensional space in the video to a two-dimensional space on the playing field plane. We define a new algorithm that predicts the players’ actions and their statistics. The results show that the proposed method can effectively identify the players, their respective teams, and their positions. It can also analyze their actions with high accuracy.

This study contended that learning statistics and its rudiments were perceived as complicated compared to some other courses offered at the university level. Further, this investigation contested the existence of statistics anxiety among postgraduate students in an in-site university setting. Relationships and differences were determined in this study utilizing the constructs of antecedents of statistics anxiety namely academic procrastination, perfectionism, and gender. The objectives were (1) to establish the relationship between academic procrastination and statistics anxiety; (2) to find out the relationship between perfectionism and statistics anxiety; and (3) to investigate the differences between gender and statistics anxiety. The data from randomly selected 136 postgrad students (Kampala International University, Uganda) referring to dispositional (procrastination and perfectionism) and environmental antecedents (gender) and statistics anxiety were scientifically elicited, processed and analyzed utilizing the quantitative- post positivist’s research paradigm model. The findings revealed a positive but insignificant relationship between academic procrastination and statistics anxiety; a significant adverse correlation between perfectionism and statistics anxiety; an insignificant positive correlation existed between gender and statistics anxiety, and differences in statistics anxiety between the female and male students existed. Notably from the results then, academic procrastination did not significantly affect the students’ statistics anxiety; the students with higher levels of perfectionism tended to have lower levels of statistics anxiety while the levels of statistics anxiety among the female students were slightly greater than that of the male students.

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the corporate distribution image, and further reduce corporate operating costs and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identified the locations of primary and secondary logistics nodes but also provided a new perspective for constructing the urban last-mile logistics system, enriching the academic research related to logistics node construction. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.