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The paper discusses the objective Bayesian estimation of the reliability of a multistate stress‐strength model (MSSM) based on different kernel functions. For the MSSM, we first derive the reliability function and Fisher information matrix. The Jeffreys prior, reference prior, and probability matching prior for the reliability function of the MSSM are constructed based on the objective Bayesian paradigm. Subsequently, we demonstrated that these priors are improper density, then evaluated the effects of these priors on Bayes estimates for MSSM's reliability based on a complete sample. The Bayesian estimates are calculated using random walk Metropolis–Hastings techniques. We employ Monte Carlo simulation to examine the effectiveness of Bayes estimates for MSSM's reliability in terms of average bias and mean squared error, meanwhile the highest posterior density credible intervals are investigated in terms of average length and coverage probability. Finally, two real datasets were examined, demonstrating the viability of the objective Bayes technique for small sample data. [ABSTRACT FROM AUTHOR]

We prove structure theorems for o-minimal definable subsets S ⊂ G of definable groups containing large multiplicative structures, and show definable groups do not have bounded torsion arbitrarily close to the identity. As an application, for certain models of n-step random walks X in G we show upper bounds P (X ∈ S) ≤ n - C and a structure theorem for the steps of X when P (X ∈ S) ≥ n - C ′ . [ABSTRACT FROM AUTHOR]

We study the limiting distribution of dense orbits of a lattice subgroup |$\Gamma \leq \textrm{SL}(m+1,\mathbb{R})$| acting on |$H\backslash \textrm{SL}(m+1,\mathbb{R})$|⁠ , with respect to a filtration of growing norm balls. The novelty of our work is that the groups |$H$| we consider have infinitely many non-trivial connected components. For a specific such |$H$|⁠ , the homogeneous space |$H\backslash G$| identifies with |$X_{m,m+1}$|⁠ , a moduli space of rank |$m$| -discrete subgroups in |$\mathbb{R}^{m+1}$|⁠. This study is motivated by the work of Shapira-Sargent who studied random walks on |$X_{2,3}$|⁠. [ABSTRACT FROM AUTHOR]

Due to the intermittent and unstable nature of PV systems, maximum power point tracking (MPPT) of PV systems is necessary in practice. At the same time, PV cells are also shaded by objects such as trees and houses in the environment causing local shadows, so the Lévy flight particle swarm optimisation (LFPSO) is proposed in this paper. The random walk process of Lévy flight is added to the particle swarm optimisation (PSO) to increase the diversity of the search, which can avoid falling into local optimal solutions. The experimental simulation results show that the algorithm proposed in this paper can still accurately track the global maximum power in the case of partial shading, which well avoids falling into the local maximum power. The performance is much higher than the traditional maximum power point tracking algorithm, which improves the efficiency of the PV system. [ABSTRACT FROM AUTHOR]

This paper discusses how to derive multidimensional conformable diffusion equation from continuous time random walks process by employing fractional Laplace transform and the conformable fractional derivative defined by Khalil et al in [10]. The results show that there exists intimate relationship between the stretched exponential function and conformable diffusion equation involving the conformable fractional derivative. The relationship is analogous to the relationship between the exponential function and usual diffusion equation involving usual derivative and the relationship between the Mittag-Leffler function and subdiffusion or slow diffusion equation involving Caputo fractional derivative. Multidimensional conformable FokkerPlanck equation is also derived here. The equation describes diffusion process influenced by external force fields. Conformable semigroup associated with the solution to the conformable diffusion equation has similar properties to the properties of the semigroup associated with the solution to usual diffusion except the semigroup property. For sufficiently large time, the graphs of the solution to the conformable diffusion equation and the subdiffusion equation involving Caputo fractional derivative are sufficiently close. Especially, for, the graphs are sufficiently close for . In presence of nonzero constant external force fields, the particle involved in the diffusion process moves faster toward the directions of the external force fields. [ABSTRACT FROM AUTHOR]

Heterogeneous Information Networks (HINs) consist of multiple categories of nodes and edges and encompass rich semantic information. Representing HINs in a low-dimensional feature space is challenging due to its complex structure and rich semantics. In this paper, we focus on link prediction and node classification by learning efficient low-dimensional feature representations of HINs. Metapath-guided walkers have been extensively studied in the literature for learning feature representations. However, the metapath walker does not control the length of random walks, resulting in weak structural and semantic information embeddings. In this work, we present an influence propagation controlled metapath-guided random walk model (called IPCMetapath2Vec) for representation learning in HINs. The model works in three phases: first, we perform node transition to generate a metapath-guided random walk, which is conditioned on two factors: (i) type mapping of the next node according to the metapath, and (ii) compute influence propagation score for each node and detect potential influencers on the walk by a threshold based filter. Next, we provide the collected random walks as input to the skip-gram model to learn each node's feature representation. Lastly, we employ an attention mechanism that aggregates the learned feature representations of each node from various semantic metapath-guided walks, preserving the importance of different semantics. We use these network representation features to address link prediction and multi-label node classification tasks. Experimental results on two public HIN datasets, namely DBLP and IMDB, show that our model outperforms the state-of-the-art representation learning models such as DeepWalk, Node2vec, Metapath2Vec, and HIN2Vec by 4.5% to 17.2% in terms of micro-F1 score for multi-label node classification and 4% to 14.50% in terms of AUC-ROC score for link prediction. [ABSTRACT FROM AUTHOR]

Quantum walks are referred to as quantum analogs to random walks in mathematics. They have been studied as quantum algorithms in quantum information for quantum computers. There are two types of quantum walks. One is the discrete-time quantum walk and the other is the continuous-time quantum walk. We study a continuous-time quantum walk on the half line and challenge to find a limit theorem for it in this paper. As a result, approximate behavior of the quantum walker is revealed after the system of quantum walk gets updated in long time. [ABSTRACT FROM AUTHOR]

To enhance the security of image data, this paper proposes a quantum-classical hybrid image encryption scheme based on parameterized pixel ratios. The scheme utilizes the pixel ratio of the image as parameters for quantum walks, Henon mapping, and Arnold Cat mapping, approximating the "one-time pad" encryption effect. With this scheme, even when encrypting multiple images using the same parameters, the resulting quantum walks random matrix and chaotic system trajectories are almost completely different. This encryption scheme eliminates the need for quantum representation of the image, thereby conserving quantum resources and reducing implementation complexity. Experimental results show that the scheme can resist statistical analysis, key analysis, noise and occlusion attacks, and has strong key sensitivity and large key space. Compared to other encryption schemes, the proposed approach exhibits advantages in terms of information entropy and plaintext sensitivity, indicating its higher level of security. [ABSTRACT FROM AUTHOR]

Aldous and Fill (2002) conjectured that the maximum relaxation time for the random walk on a connected regular graph with n vertices is (1 + o (1)) 3 n 2 2 π 2 . A conjecture by Guiduli and Mohar (1996) predicts the structure of graphs whose algebraic connectivity μ is the smallest among all connected graphs whose minimum degree δ is a given d. We prove that this conjecture implies the Aldous–Fill conjecture for odd d. We pose another conjecture on the structure of d -regular graphs with minimum μ , and show that this also implies the Aldous–Fill conjecture for even d. In the literature, it has been noted empirically that graphs with small μ tend to have a large diameter. In this regard, Guiduli (1996) asked if the cubic graphs with maximum diameter have algebraic connectivity smaller than all others. Motivated by these, we investigate the interplay between the graphs with maximum diameter and those with minimum algebraic connectivity. We show that the answer to Guiduli problem in its general form, that is for d -regular graphs for every d ≥ 3 is negative. We aim to develop an asymptotic formulation of the problem. It is proven that d -regular graphs for d ≥ 5 as well as graphs with δ = d for d ≥ 4 with asymptotically maximum diameter, do not necessarily exhibit the asymptotically smallest μ. We conjecture that d -regular graphs (or graphs with δ = d) that have asymptotically smallest μ , should have asymptotically maximum diameter. The above results rely heavily on our understanding of the structure as well as optimal estimation of the algebraic connectivity of nearly maximum-diameter graphs, from which the Aldous–Fill conjecture for this family of graphs also follows. [ABSTRACT FROM AUTHOR]

The conformation of macromolecules attached to a surface is influenced by both their excluded volume and steric forces. Here, self‐avoiding random walk simulations are used to evaluate the occurrence of various conformations as a function of the number of monomeric units to estimate the effect of conformational entropy of a tethered chain. Then, a more realistic scenario is assessed, which can more accurately reproduce the shape of a tethered macromolecule. The simulations presented here confirm that it is more likely for a polymer to undergo a collapse conformation rather than a stretched one, as a collapse conformation can be realized in more different ways. Also, they confirm the "mushroom" shape of polymers close to a surface. From this simple approach, the conformation entropy of a model 100‐unit polymer close to a surface is estimated to contribute with over 129 kBT${{k}_{\mathrm{B}}}T$ toward its collapse. This conformation entropy is higher than that of typical hydrogen bonds and even barriers that keep proteins folded. As such, entropic collapse of macromolecules plays an important role in realizing the mushroom shape of attached polymers and can be the driving force in protein folding, while the polypeptide chain emerges from the ribosome. [ABSTRACT FROM AUTHOR]

We show that if 퐺 is an admissible group acting geometrically on a CAT ⁡ (0) space 푋, then 퐺 is a hierarchically hyperbolic space and its 휅-Morse boundary (∂ κ G , ν) is a model for the Poisson boundary of (G , μ) , where 휈 is the hitting measure associated to the random walk driven by 휇. [ABSTRACT FROM AUTHOR]

A random walk with counterbalanced steps is a process of partial sums Š(n) = X1 + • • • + Xn whose steps Xn are given recursively as follows. For each n ≥ 2, with a fixed probability p, Xn is a new independent sample from some fixed law μ, and with complementary probability 1 - p, Xn = -Xυ(n) counterbalances a previous step, with υ(n) uniform random pick from {1,...,n -1} We determine the asymptotic behavior of Š(n) in terms of p and the first two moments of μ. Our approach relies on a coupling with a reinforcement algorithm due to H. A. Simon, and on properties of random recursive trees and Eulerian numbers, which may be of independent interest. The method can be adapted to the situation where the step distribution μ belongs to the domain of attraction of a stable law. [ABSTRACT FROM AUTHOR]

Random walk particle tracking (RWPT) is a discrete particle method that offers several advantages for simulating solute transport in heterogeneous geological systems. The formulation is a discrete solution to the advection‐dispersion equation, yielding results that are not influenced by grid‐related numerical dispersion. Numerical dispersion impacts the magnitude of concentrations and gradients obtained from classical grid‐based solvers in advection‐dominated problems with relatively large grid Péclet numbers. Accurate predictions of concentrations are crucial for reactive transport studies, and RWPT has been recognized for its potential benefits for this kind of application. This highlights the need for a solute transport program based on RWPT that can be seamlessly integrated with industry‐standard groundwater flow models. This article presents a solute transport code that implements the RWPT method by extension of the particle tracking model MODPATH, which provides the base infrastructure for interacting with several variants of MODFLOW groundwater flow models. The implementation is achieved by developing a method for determining the exact cell‐exit position of a particle undergoing simultaneous advection and dispersion, allowing for the sequential transfer of particles between flow model cells. The program is compatible with rectangular unstructured grids and integrates a module for the smoothed reconstruction of concentrations. In addition, the program incorporates parallel processing of particles using the OpenMP library, enabling faster simulations of solute transport in heterogeneous systems. Numerical test cases involving different applications in hydrogeology benchmark the RWPT model with well‐known transport codes. Article impact statement: Extension of MODPATH source code for modeling advection dispersion with random walk particle tracking. [ABSTRACT FROM AUTHOR]

In the Transformer architecture, positional encoding is a vital component because it provides the model with information about the structure and position of data. In Graph Transformer, there have been attempts to introduce different positional encodings and inject additional structural information. Therefore, in terms of integrating positional and structural information, we propose a Structural and Positional Ensembled Graph Transformer (SPEGT). We developed SPEGT by noting the different properties of structural and positional encodings of graphs and the similarity of their computational processes. We have set a unified component that integrates the functionalities: (i) Random Walk Positional Encoding, (ii) Shortest Path Distance between each node, and (iii) Hierarchical Cluster Encoding. We find a problem with a well-known positional encoding and experimentally verify that combining it with other encodings can solve their problem. In addition, SPEGT outperforms previous models on a variety of graph datasets. We also show that SPEGT using unified positional encoding, performs well on structurally indistinguishable graph data through error case analysis. [Display omitted] • We combined information of graph structures to create a novel positional encoding. • The components of the new unified encoding compensate for each other's shortcomings. • Our new architecture continuously injects unified information into the attention. • Our model performs better when edge information is scarce. • The model discriminates graphs that are structurally indistinguishable. [ABSTRACT FROM AUTHOR]

The rapid evolution of computer technology and social networks has led to massive data generation through interpersonal communications, necessitating improved methods for information mining and relational analysis in areas such as criminal activity. This paper introduces a Social Network Forensic Analysis model that employs network representation learning to identify and analyze key figures within criminal networks, including leadership structures. The model incorporates traditional web forensics and community algorithms, utilizing concepts such as centrality and similarity measures and integrating the Deepwalk, Line, and Node2vec algorithms to map criminal networks into vector spaces. This maintains node features and structural information that are crucial for the relational analysis. The model refines node relationships through modified random walk sampling, using BFS and DFS, and employs a Continuous Bag-of-Words with Hierarchical Softmax for node vectorization, optimizing the value distribution via the Huffman tree. Hierarchical clustering and distance measures (cosine and Euclidean) were used to identify the key nodes and establish a hierarchy of influence. The findings demonstrate the effectiveness of the model in accurately vectorizing nodes, enhancing inter-node relationship precision, and optimizing clustering, thereby advancing the tools for combating complex criminal networks. [ABSTRACT FROM AUTHOR]

In this study, the key sectors of Turkey are determined using Input-Output (IO) network analysis. Different centrality measures are analyzed and compared. Accordingly, eigenvector, page rank, hub, and authority centralities are calculated. The effects of an economic shock are also analyzed using random walk centrality and counting betweenness. Findings indicate that the key sectors are "Wholesale and Retail Trade; Repair of Motor Vehicles", "Transport, Storage and Communications", "Agriculture, Hunting and Forestry", "Construction", "Real Estate Activities", and "Textiles, Textile Products, Leather and Footwear". Furthermore, the same sectors are determined to be vulnerable to economic shocks. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Development & Capital / Majallah-i tusiah & Sarmāyah is the property of Shahid Bahonar University of Kerman, Faculty of Management & Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Development & Capital / Majallah-i tusiah & Sarmāyah is the property of Shahid Bahonar University of Kerman, Faculty of Management & Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

I propose an affine model of short rates that incorporates a random walk with stochastic drift. This framework enables my model to capture the behavior of monetary authorities in the short rate market, allowing for minor deviations while reacting strongly to deviations large enough to threaten production. Importantly, my model facilitates the derivation of closed-form bond prices, thereby providing an analytical solution for bond-option prices. I compare my model with nine standard short rate models found in the literature. Among these, five are single-factor models and four are multifactor models. Remarkably, my model outperforms all competing short rate models, including the constant elasticity of volatility, stochastic mean, and stochastic volatility models. Moreover, it yields interest rate forecasts consistent with common term structure priors and surpasses the performance of the naive random walk model. Additionally, my stochastic mean model can explain the unspanned risks documented in the literature. [ABSTRACT FROM AUTHOR]

R. Lyons, R. Pemantle and Y. Peres (Ann. Probab. 24 (4), 1996, 1993–2006) conjectured that for a Cayley graph G with a growth rate gr(G) > 1, the speed of a biased random walk exists and is positive for the biased parameter λ ∈ (1, gr(G)). And Gabor Pete (Probability and geometry ´ on groups, Chaper 9, 2024) sheds light on the intricate relationship between the spectral radius of the graph and the speed of the biased random walk. Here, we focus on an example of a Cayley graph, a free product of complete graphs. In this paper, we establish the continuity of the spectral radius of biased random walks with respect to the bias parameter in this class of Cayley graphs. Our method relies on the Kesten-Cheeger-Dodziuk-Mohar theorem and the analysis of generating functions. [ABSTRACT FROM AUTHOR]

This research explored the number of returns to the origin within the framework of a symmetric simple random walk. Our primary objective was to approximate the distribution of return events to the origin by utilizing the half-normal distribution, which is chosen for its appropriateness as a limit distribution for nonnegative values. Employing the Stein’s method in conjunction with concentration inequalities, we derived an exponential non-uniform bound for the approximation error. This bound signifies a significant advancement in contrast to existing bounds, encompassing both the uniform bounds proposed by Döbler [1] and polynomial non-uniform bounds presented by Sama-ae, Chaidee, and Neammanee [2], and Siripraparat and Neammanee [3]. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an extended version of the elephant random walk model. Unlike the traditional approach where step sizes remain constant, our model introduces a novel feature: step sizes are generated as a sequence of positive independent and identically distributed random variables, and the step of the walker at time n+1 depends only on the steps of the walker between times 1, ..., mn, where (mn)n⩾1 is a sequence of positive integers growing to infinity as n goes to infinity. Our main results deal with the validity of the central limit theorem for this new variation of the standard ERW model introduced by Schutz and Trimper in 2004. [ABSTRACT FROM AUTHOR]

The article offers a method for estimating the volatility covariance matrix of vectors of financial time series data using a change point approach. The proposed method supersedes general varying-coefficient parametric models, such as GARCH, whose coefficients may vary with time, by a change point model. In this study, an adaptive pointwise selection of homogeneous segments with a given right-end point by a local change point analysis is introduced. Sufficient conditions are obtained under which the maximum likelihood process is adaptive against the covariance estimate to yield an optimal rate of convergence with respect to the change size. This rate is preserved while allowing the jump size to diminish. Under these circumstances, argmax results of a two-sided negative Brownian motion or a two-sided negative drift random walk under vanishing and non-vanishing jump size regimes, respectively, provide inference for the change point parameter. Theoretical results are supported by the Monte–Carlo simulation study. A bivariate data on daily log returns of two US stock market indices as well as tri-variate data on daily log returns of three banks are analyzed by constructing confidence interval estimates for multiple change points that have been identified previously for each of the two data sets. [ABSTRACT FROM AUTHOR]

The Grey Wolf Optimization Algorithm (GWO) is a member of the swarm intelligence algorithm family, which possesses the highlights of easy realization, simple parameter settings and wide applicability. However, in some large-scale application problems, the grey wolf optimization algorithm easily gets trapped in local optima, exhibits poor global exploration ability and suffers from premature convergence. Since grey wolf's update is guided only by the best three wolves, it leads to low population multiplicity and poor global exploration capacity. In response to the above issues, we design a multi-strategy collaborative grey wolf optimization algorithm (NOGWO). Firstly, we use a random walk strategy to extend the exploration scope and enhance the algorithm's global exploration capacity. Secondly, we add an opposition-based learning model influenced by refraction principle to generate an opposite solution for each population, thereby improving population multiplicity and preventing the algorithm from being attracted to local optima. Finally, to balance local exploration and global exploration and elevate the convergence effect, we introduce a novel convergent factor. We conduct experimental testing on NOGWO by using 30 CEC2017 test functions. The experimental outcomes indicate that compared with GWO and some swarm intelligence algorithms, NOGWO has better global exploration capacity and convergence accuracy. In addition, we also apply NOGWO to three engineering problems and an unmanned aerial vehicle path planning problem. The outcomes of the experiment suggest that NOGWO performs well in solving these practical problems. [ABSTRACT FROM AUTHOR]

The performance of 5G/6G cellular systems operating in millimeter wave (mmWave, 30–100 GHz) and sub-terahertz (sub-THz, 100–300 GHz) bands is conventionally assessed by utilizing the static distributions of user locations. The rationale is that the use of the beam tracking procedure allows for keeping the beams of a base station (BS) and user equipment (UE) aligned at all times. However, by introducing 3GPP Reduced Capability (RedCap) UEs utilizing the Radio Resource Management (RRM) Relaxation procedure, this may no longer be the case, as UEs are allowed to skip synchronization signal blocks (SSB) to improve energy efficiency. Thus, to characterize the performance of such UEs, methods explicitly accounting for UE mobility are needed. In this paper, we will utilize the tools of the stochastic geometry and random walk theory to derive signal-to-noise ratio (SNR), spectral efficiency, and rate as an explicit function of time by accounting for mmWave/sub-THZ specifics, including realistic directional antenna radiation patterns and micro- and macro-mobilities causing dynamic antenna misalignment. Different from other studies in the field that consider time-averaged performance measures, these metrics are obtained as an explicit function of time. Our numerical results illustrate that the macro-mobility specifies the overall trend of the time-dependent spectral efficiency, while local dynamics at 1–3 s scales are mainly governed by micro-mobility. The difference between spectral efficiency corresponding to perfectly synchronized UE and BS antennas and time-dependent spectral efficiency in a completely desynchronized system is rather negligible for realistic cell coverages and stays within approximately 5–10% for a wide range of system parameters. These conclusions are not affected by the utilized antenna array at the BS side. However, accounting for realistic radiation patterns is critical for a time-dependent performance analysis of 5G/6G mmWave/sub-THz systems. [ABSTRACT FROM AUTHOR]

Purpose — The current study seeks to understand whether individual stock returns exhibit random movement and are not dependent (efficient at weak form) on fourteen out of sixteen actively traded Arab stock markets in the Middle East and North Africa (MENA) region, based on the size of the market value. Method — Various non-parametric methods, including autocorrelation test, variance ratio test, Phillips-Perron unit root test, and runs test, are used to assess the random walk hypothesis for daily data following the Covid-19 vaccination program. This analysis covers the period from January 3, 2021, to March 28, 2023. Result — The study results present evidence that all individual stock returns deviate from random walk behavior. However, only Kuwait, Jordan, and Palestine stock returns follow the random walk based on the run test results at a significance level of 10%. Therefore, it can be concluded that all stock returns are inefficient at the weak-form, suggesting that investors have opportunities for unexpected gains.. Practical implications — The findings of this study suggest that investors in the MENA region may have opportunities for unexpected gains, as individual stock returns deviate from random walk behavior, highlighting the importance of considering market dynamics and employing informed investment strategies. Additionally, policymakers could benefit from understanding the inefficiencies in stock returns to implement measures that promote market stability and efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Electric Power Science & Technology is the property of Changsha University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

UK top income shares have varied hugely over the past two centuries, ranging from more than 30% to less than 7% of pre‐tax national income allocated to the top 1 percentile. We build a congruent dynamic linear regression model of the top 1% income share allowing for economic, political and social factors. Saturation estimation is used to model outliers and trend breaks, proxying underlying structural changes driving income inequality in the UK. We use the model to forecast the top 1% income share over the last 15 years, and compare to a range of forecast devices. Despite a well‐specified constant parameter model conditioning on significant explanatory variables, the best performing forecasts are obtained from a random walk and a smoothed random walk. These results are explained by the presence of shifts in the income share over the forecast period, resulting in forecasts from equilibrium correction models converging to the wrong equilibrium. Our best prediction for 2026 based on the most recent data from 2021 (a 5‐year ahead projection) is that the pre‐tax top 1% income share will remain at the most recent realized value of 12.7%, but there is a large degree of uncertainty, with a 95% confidence band ranging from 10% to 15.7%. This paper is part of the Economica 100 Series. Economica, the LSE "house journal" is now 100 years old. To commemorate this achievement, we are publishing 100 papers by former students, as well as current and former faculty. David Forbes Hendry received his MSc and PhD from the LSE. [ABSTRACT FROM AUTHOR]

In this paper, we consider the forecasting of domestic food price inflation (DFPI) using global indicators, with emphasis on episodes of macroeconomic turbulence, namely, the Global Financial Crisis (GFC) and the COVID-19 pandemic and its subsequent repercussions. Our monthly dataset covers about two decades for more than a hundred economies. We employ dynamic model averaging (DMA) to tackle both model uncertainty and parameter instability and produce pseudo out-of-sample forecasts. Thus, we are able to focus on the forecasting ability of the global predictors of DFPI before and during the global crises. We find evidence that the DMA specification tends to outperform statistical models frequently used in the literature such as random walks, autoregressive models, and time-varying parameter models, especially during global crises. We also identify the most successful predictors during the crises using their posterior probabilities of inclusion. By comparing the distributions of such probabilities, we find that the international food price inflation is the most useful predictor of DFPI for numerous countries during both crises. Other indicators such as domestic CPI inflation as well as the international inflation of agricultural commodities, fertilizers, and other food categories improved their forecasting ability, particularly during the COVID-19 period. [ABSTRACT FROM AUTHOR]

We prove universal inequalities for Laplacian eigenvalues with Dirichlet boundary condition on subsets of certain discrete groups. The study of universal inequalities on Riemannian manifolds was initiated by Weyl, Pólya, Yau, and others. Here we focus on a version by Cheng and Yang. Specifically, we prove Yang-type universal inequalities for Cayley graphs of finitely generated amenable groups, as well as for the d-regular tree (simple random walk on the free group). [ABSTRACT FROM AUTHOR]

We present a computationally efficient multiscale method for preparing equilibrated, isotropic long-chain model polymer melts. As an application, we generate Kremer–Grest melts of 1000 chains with 200 entanglements and 25 000–2000 beads/chain, which cover the experimentally relevant bending rigidities up to and beyond the limit of the isotropic–nematic transition. In the first step, we employ Monte Carlo simulations of a lattice model to equilibrate the large-scale chain structure above the tube scale while ensuring a spatially homogeneous density distribution. We then use theoretical insight from a constrained mode tube model to introduce the bead degrees of freedom together with random walk conformational statistics all the way down to the Kuhn scale of the chains. This is followed by a sequence of simulations with carefully parameterized force-capped bead–spring models, which slowly introduce the local bead packing while reproducing the larger-scale chain statistics of the target Kremer–Grest system at all levels of force-capping. Finally, we can switch to the full Kremer–Grest model without perturbing the structure. The resulting chain statistics is in excellent agreement with literature results on all length scales accessible in brute-force simulations of shorter chains. [ABSTRACT FROM AUTHOR]

We develop a multiscale simulation model for diffusion of solutes through porous triblock copolymer membranes. The approach combines two techniques: self-consistent field theory (SCFT) to predict the structure of the self-assembled, solvated membrane and on-lattice kinetic Monte Carlo (kMC) simulations to model diffusion of solutes. Solvation is simulated in SCFT by constraining the glassy membrane matrix while relaxing the brush-like membrane pore coating against the solvent. The kMC simulations capture the resulting solute spatial distribution and concentration-dependent local diffusivity in the polymer-coated pores; we parameterize the latter using particle-based simulations. We apply our approach to simulate solute diffusion through nonequilibrium morphologies of a model triblock copolymer, and we correlate diffusivity with structural descriptors of the morphologies. We also compare the model's predictions to alternative approaches based on simple lattice random walks and find our multiscale model to be more robust and systematic to parameterize. Our multiscale modeling approach is general and can be readily extended in the future to other chemistries, morphologies, and models for the local solute diffusivity and interactions with the membrane. [ABSTRACT FROM AUTHOR]

Electron leakage currents seriously hinder GaN-based blue laser diodes (LDs) from high wall-plug efficiencies. Inserting an ultra-thin AlGaN electron blocking layer (EBL) in the epitaxy structure is a major technique to suppress the leakage currents for which a high Al composition in the EBL is necessary. Despite many studies on the optimization of the compositions of EBLs, it is questionable whether they reach the designed value in real growths by metal-organic vapor phase epitaxy. We investigate the influence of the growth conditions of upper cladding layers (CLs) on the underlying EBLs. A strong composition degradation of the EBL is observed when the growth rate of the CL is low, which drastically reduces the output performance of both LEDs and LDs. A 30-nm fast-growing protecting layer can efficiently prevent the EBL from such degradation. The phenomenon cannot be explained by a composition pulling effect nor an etch effect by hydrogen, but by a mutual solid solution between the EBL and the adjacent CL. The solution process is found thermally favored by calculating the Gibbs energy where strain and entropies are considered. It is inferred that the chemically active Ga adatoms at the surface play an important role in accelerating the solution process. Based on these considerations, we introduce a random walk model to clarify the kinetic influence of CL growth rates on EBL degradation semi-quantitatively. The results help to understand the subtle process in the growth of heterostructures and the transport process of GaN-based LDs. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The clogging phenomenon finds extensive application in both industrial processes and daily life events. While this broad spectrum of application motivated extensive research to identify the general factors underlying the clogging mechanism, it results in a fragmented and system-specific understanding of the entire clogging process. Therefore, it is essential to establish a holistic understanding of all contributing factors of clogging based on the microscopic physical mechanisms. In this paper, we experimentally investigate clogging of granular materials in a two-dimensional hopper flow and present a self-consistent physical mechanism of clogging based on precursory chain structures. These chain structures follow a specific modified restricted random walk, and clogging occurs when they are mechanically stable enough to withstand the flow fluctuations. We introduce a single-particle model that can explain the arch-forming probability. Our results provide insight into the microscopic mechanism behind clogging and a broader understanding of the dynamics of dense granular flow. The study of the general factors underlying clogging are fragmented and application-driven due to its broad spectrum of applications. The authors propose a holistic understanding of the clogging process by investigating the clogging of a granular hopper flow using high-speed imaging, finding a signature formation of precursory chain structures. [ABSTRACT FROM AUTHOR]

We studied the first detection problem of the quantum particle transition on a one-dimensional Lévy crystal, which includes the long-range interactions and the transition process on the system can be described by the spatial fractional Schrödinger equation. The results demonstrate that the long-range interactions can be directly obtained for short time. The average detection times 〈 n 〉 and the corresponding variance var (n) are discontinuous with respect to the detection interval τ , which could be caused by the beat of the detection frequency 1 / τ and the oscillation frequency of the first detection probability F n . [ABSTRACT FROM AUTHOR]

Ant Lion Optimization (ALO) method is one of the population-based nature-inspired optimization algorithms which mimics the hunting strategy of antlions. ALO is successfully employed for solving many complicated optimization problems. However, it is reported in the literature that the original ALO has some limitations such as the requirement of high number of iterations and possibility of trapping to local optimum solutions, especially for complex or large-scale problems. For this purpose, the SHuffled Ant Lion Optimization (SHALO) approach is proposed by conducting two improvements in the original ALO. Performance of the proposed SHALO approach is evaluated by solving some unconstrained and constrained problems for different conditions. Furthermore, the identified results are statistically compared with the ones obtained by using the original ALO, two improved ALOs which are the self-adaptive ALO (saALO) and the exponentially weighted ALO (EALO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) approaches. Identified results indicated that the proposed SHALO approach significantly improves the solution accuracy with a mean success rate of 76% in terms of finding the global or near-global optimum solutions and provides better results than ALO (22%), saALO (25%), EALO (14%), GA (28%), and PSO (49%) approaches for the same conditions. [ABSTRACT FROM AUTHOR]

In the last two decades or so, a large number of image ciphers have been written. The majority of these ciphers encrypt only one image at a time. Few image ciphers were written which could encrypt multiple images in one session. The current era needs speedy multiple image ciphers to address its varied needs in different settings. Motivated by this dictation, the current study has ventured to write a multi-image cipher based on the fleet of pawns walking in the large hypothetical chessboard. This walk of pawns on the chessboard has been ingeniously linked with transferring the pixels from the plain image to the scrambled image. The confusion effects have been realized through the XOR operation between the scrambled image and the key image. The plaintext sensitivity has been incorporated by embedding the SHA-384 hash codes of the given large combined plain image. Moreover, the Henon map has been employed to spawn the streams of random numbers. Besides, Blum Blum Shub random number generator has been used to further cement the security of the proposed cipher. We got a computational time of 0.2278 seconds and an encryption throughput of 5.5782 MBit/seconds by using the four images with a size of 256×256. Apart from that, the information entropy gained is 7.9993. Lastly, the cipher has been subjected to an array of validation metrics to demonstrate its aversion to the myriad threats from the cryptanalysis savvy. We contend that the proposed work has great potential for some real-world applications. [ABSTRACT FROM AUTHOR]

Planet–planetesimal interactions cause a planet to migrate, manifesting as a random walk in semimajor axis. In models for Neptune's migration involving a gravitational upheaval, this planetesimal-driven migration is a side-effect of the dynamical friction required to damp Neptune's orbital eccentricity. This migration is noisy, potentially causing Trans-Neptunian Objects (TNOs) in mean motion resonance to be lost. With N -body simulations, we validate a previously derived analytic model for resonance retention and determine unknown coefficients. We identify the impact of random-walk (noisy) migration on resonance retention for resonances up to fourth order lying between 39  and 75 au. Using a population estimate for the weak 7:3 resonance from the well-characterized Outer Solar System Origins Survey (OSSOS), we rule out two cases: (1) a planetesimal disc distributed between 13.3 and 39.9 au with ≳ 30 Earth masses in today's size distribution and T mig ≳ 40 Myr and (2) a top-heavy size distribution with ≳2000 Pluto-sized TNOs and T mig ≳10 Myr, where T mig is Neptune's migration time-scale. We find that low-eccentricity TNOs in the heavily populated 5:2 resonance are easily lost due to noisy migration. Improved observations of the low-eccentricity region of the 5:2 resonance and of weak mean motion resonances with Rubin Observatory's Legacy Survey of Space and Time will provide better population estimates, allowing for comparison with our model's retention fractions and providing strong evidence for or against Neptune's random interactions with planetesimals. [ABSTRACT FROM AUTHOR]

This paper studies a few randomized algorithms (e.g., random walks, gossip) in peer-to-peer networks. We leverage the Docker virtual container technology to develop implementations of the peer-to-peer networks and of these distributed randomized algorithms executing on top of them. We can thus analyze their behavior and performance in realistic settings. We further consider the problem of identifying high-risk bottleneck links in the network with the objective of improving the resilience and reliability of peer-to-peer networks. We propose a randomized algorithm to solve this problem and evaluate its performance by simulations. [ABSTRACT FROM AUTHOR]

The propagation of signalling molecules within cellular networks is affected not only by network topology, but also by the spatial arrangement of cells in the networks. Understanding the collective reaction–diffusion behaviour in space of signals propagating through cellular networks is an important consideration, for example, for regenerative signals that convey positional information. In this work, we consider stochastic and deterministic versions of random walk models of signalling molecules propagating and reacting within one-dimensional spatial networks with arbitrary node placement and connectivity. By taking a continuum limit of the random walk models, we derive an inhomogeneous reaction–diffusion–advection equation, where diffusivity and advective velocity depend on local node density and connectivity within the network. Our results show that large spatial variations of molecule concentrations can be induced by heterogeneous node distributions. Furthermore, we find that noise within the stochastic random walk model is directly influenced by node density. We apply our models to consider signal propagation within the osteocyte network of bone, where signals propagating to the bone surface regulate bone formation and resorption processes. We investigate signal-to-noise ratios for different damage detection scenarios and show that the location of perturbations to the network can be detected by signals received at the network boundaries. [ABSTRACT FROM AUTHOR]

Navigation of mobile agents in unknown, unmapped environments is a critical task for achieving general autonomy. Recent advancements in combining Reinforcement Learning with Deep Neural Networks have shown promising results in addressing this challenge. However, the inherent complexity of these approaches, characterized by multi-layer networks and intricate reward objectives, limits their autonomy, increases memory footprint, and complicates adaptation to energy-efficient edge hardware. To overcome these challenges, we propose a brain-inspired method that employs a shallow architecture trained by a local learning rule for self-supervised navigation in uncharted environments. Our approach achieves performance comparable to a stateof-the-art Deep Q Network (DQN) method with respect to goal-reaching accuracy and path length, with a similar (slightly lower) number of parameters, operations, and training iterations. Notably, our self-supervised approach combines novelty-based and random walks to alleviate the need for objective reward definition and enhance agent autonomy. At the same time, the shallow architecture and local learning rule do not call for error backpropagation, decreasing the memory overhead and enabling implementation on edge neuromorphic processors. These results contribute to the potential of embodied neuromorphic agents utilizing minimal resources while effectively handling variability. [ABSTRACT FROM AUTHOR]

Rosettes are self-organizing, circular multicellular communities that initiate developmental processes, like organogenesis and embryogenesis, in complex organisms. Their formation results from the active repositioning of adhered sister cells and is thought to distinguish multicellular organisms from unicellular ones. Though common in eukaryotes, this multicellular behavior has not been reported in bacteria. In this study, we found that Escherichia coli forms rosettes by active sister-cell repositioning. After division, sister cells "fold" to actively align at the 2-and 4-cell stages of clonal division, thereby producing rosettes with characteristic quatrefoil configuration. Analysis revealed that folding follows an angular random walk, composed of ~1 µm strokes and directional randomization. We further showed that this motion was produced by the flagellum, the extracellular tail whose rotation generates swimming motility. Rosette formation was found to require de novo flagella synthesis suggesting it must balance the opposing forces of Ag43 adhesion and flagellar propulsion. We went on to show that proper rosette formation was required for subsequent morphogenesis of multicellular chains, rpoS gene expression, and formation of hydrostatic clonal-chain biofilms. Moreover, we found self-folding rosette-like communities in the standard motility assay, indicating that this behavior may be a general response to hydrostatic environments in E. coli. These findings establish self-organization of clonal rosettes by a prokaryote and have implications for evolutionary biology, synthetic biology, and medical microbiology. [ABSTRACT FROM AUTHOR]