Functional Convergence Research Articles

Point process convergence for symmetric functions of high-dimensional random vectors

The convergence of a sequence of point processes with dependent points, defined by a symmetric function of iid high-dimensional random vectors, to a Poisson random measure is proved. This also implies the convergence of the joint distribution of a fixed number of upper order statistics. As applications of the result a generalization of maximum convergence to point process convergence is given for simple linear rank statistics, rank-type U-statistics and the entries of sample covariance matrices.

Application of an intelligent fuzzy logic based sliding mode controller for frequency stability analysis in a deregulated power system using OPAL-RT platform

Owing to unpredicted power demand, integration of distributed generators and parametric disparity in the system cause the power system more intricacy which in turn affects the frequency and power fluctuation critically. To mitigate these phenomena, an intelligent and efficient automatic generation control (AGC) is indispensable. Therefore, in this paper, an intelligent and robust controller named fuzzy sliding mode controller (FSMC) has been recommended to tackle the AGC problem in a deregulated power system in the presence of distributed generators effectively. Again, to manifest its dominance over SMC, fuzzy-PID and PID controllers, dynamic response of the deregulated system under Poolco, Bilateral and Contract violation conditions has been evaluated and compared. Further, to support the FSMC controller’s ability, another test model has been taken for the AGC study. The gains of these controllers have been enumerated by Gannet Optimization Algorithm (GOA) subjecting to a minimization problem. Exploration phase of GOA involves two steps, namely U- & V-shaped diving patterns of Gannets and exploitation phase again involves two steps namely sudden rotation and random walk of Gannets. These four steps ensures quick convergence of the objective function to its optimal value. Apart from transient response under different power transaction conditions, parametric uncertainties of the system, solar and wind power variation and abrupt load change have been considered to support the robustness & credibility of FSMC controller. In addition to this, the stability of the proposed model has been analyzed in the frequency domain. Finally, the MATLAB/SIMULINK based transient response has been validated by a real-time simulator using OPAL-RT-4510, Xilinx Kintex-7 FPGA software.

A RESULT OF MEAN SQUARE EXPONENTIAL STABILITY FOR DIFFERENTIAL DELAY EQUATIONS WITH STOCHASTIC NOISE.

In the present paper, we aim to study of a class of nonlinear differential equations with stochastic noise. Firstly, we introduce the condition of local Lipschitz and a new non-linear growth condition. Then by applying Lyapunov function and semi-martingale convergence theorem, we prove that the stochastic system under consideration has a unique global solution. Additionally, we also investigate the exponential stability of the mean square.

Underwater Structured Light Stripe Center Extraction with Normalized Grayscale Gravity Method.

The non-uniform reflectance characteristics of object surfaces and underwater environment disturbances during underwater laser measurements can have a great impact on laser stripe center extraction. Therefore, we propose a normalized grayscale gravity method to address this problem. First, we build an underwater structured light dataset for different illuminations, turbidity levels, and reflective surfaces of the underwater object and compare several state-of-the-art semantic segmentation models, including Deeplabv3, Deeplabv3plus, MobilenetV3, Pspnet, and FCNnet. Based on our comparison, we recommend PSPnet for the specific task of underwater structured light stripe segmentation. Second, in order to accurately extract the centerline of the extracted light stripe, the gray level values are normalized to eliminate the influence of noise and light stripe edge information on the centroids, and the weights of the cross-sectional extremes are increased to increase the function convergence for better robustness. Finally, the subpixel-structured light center points of the image are obtained by bilinear interpolation to improve the image resolution and extraction accuracy. The experimental results show that the proposed method can effectively eliminate noise interference while exhibiting good robustness and self-adaptability.

An asymptotic development of the Poisson integral for Laguerre polynomial expansions

The purpose of this paper is the study of the rate of convergence of Poisson integrals for Laguerre expansions. The convergence of partial sums of Fourier series of functions in Lp spaces was studied, for several classes of orthogonal polynomials. In the Laguerre case Askey and Waigner proved convergence for functions f∈Lp0,+∞ with 4/3<p<4. In this paper we deal with the Poisson integral Arf0<r<1 which arises by applying Abel’s summation method to the Laguerre expansion of the function f. About 50 years ago, Muckenhoupt intensively studied the Poisson integral for the Laguerre and Hermite polynomials. Among other things he proved pointwise convergence, the convergence by norm, and that the Poisson integral is a contraction mapping in Lp0,∞. Toczek and Wachnicki gave a Voronovskaja-type theorem by calculating the limit 1−r−1Arfx−fx as r→1−, provided that f′′x exists. We generalize this formula by deriving a complete asymptotic development. All its coefficients are explicitly given in a concise form. As an application we apply extrapolation methods in order to improve the rate of convergence of Arfx as r→1−.

Uniform Convergence of the Scattering Angle Function in Gas Mixtures

Uniform Convergence of the Scattering Angle Function in Gas Mixtures

Synergetic microRNA delivery for therapeutic targeting in Alzheimer’s disease

AbstractBackgroundAlzheimer’s disease (AD) is a complex multigenic disorder primarily defined by the presence of β‐amyloid‐containing plaques and Tau‐containing neurofibrillary tangles. To date, there is no available treatment and the failure of numerous single‐target clinical trials shifts more and more the focus to ‘multiple drugs – multiple targets’ pharmacological approaches. miRNAs are short non‐coding RNAs that control the expression of specific target genes, often within the same biological pathways. This makes miRNA‐based therapeutics uniquely suited for gene network‐based approaches, as they may regulate common sensitive hubs in biological pathways relevant for AD. Importantly, most gene transcripts are regulated by multiple miRNAs, leading to functional convergence and increased regulatory strength and accuracy. miR‐124 and miR‐132 are among the miRNAs dysregulated in human AD brain and may counteract AD pathology and memory deficits via common pathways in rodent models of AD. Here, we directly address the therapeutic potential of synergistic miR‐124/miR‐132 delivery in AD.MethodWe first evaluate the in vitro efficiency of miRNA‐carrying pAAV plasmids to overexpress mature miRNAs individually or combined using transfection in HEK293T cells. Second, delivery of the generated AAVs via stereotactic surgery is employed to assess their efficiency in vivo.ResultHere we show preliminary data on the development of cell type‐specific AAVs for the combined delivery of two miRNAs, namely miR‐124 and miR‐132, in the mammalian brain. We demonstrate their efficiency in vitro as well as in vivo to label targeted cells and sufficiently increase miRNA levels.ConclusionOur approach provides a distinctive toolkit to enable precise cell targeting, dosing and toxicity control of a combinatorial miRNA carrying AAV system in the central nervous system.

A phenome-wide scan reveals convergence of common and rare variant associations

BackgroundCommon and rare variants contribute to the etiology of complex traits. However, the extent to which the phenotypic effects of common and rare variants involve shared molecular mediators remains poorly understood. The question is essential to the basic and translational goals of the science of genomics, with critical basic-science, methodological, and clinical consequences.MethodsLeveraging the latest release of whole-exome sequencing (WES, for rare variants) and genome-wide association study (GWAS, for common variants) data from the UK Biobank, we developed a metric, the COmmon variant and RAre variant Convergence (CORAC) signature, to quantify the convergence for a broad range of complex traits. We characterized the relationship between CORAC and effective sample size across phenome-wide association studies.ResultsWe found that the signature is positively correlated with effective sample size (Spearman ρ = 0.594, P < 2.2e − 16), indicating increased functional convergence of trait-associated genetic variation, across the allele frequency spectrum, with increased power. Sensitivity analyses, including accounting for heteroskedasticity and varying the number of detected association signals, further strengthened the validity of the finding. In addition, consistent with empirical data, extensive simulations showed that negative selection, in line with enhancing polygenicity, has a dampening effect on the convergence signature. Methodologically, leveraging the convergence leads to enhanced association analysis.ConclusionsThe presented framework for the convergence signature has important implications for fine-mapping strategies and drug discovery efforts. In addition, our study provides a blueprint for the expectation from future large-scale whole-genome sequencing (WGS)/WES and sheds methodological light on post-GWAS studies.

Convergence of halo statistics: code comparison between rockstar and compaso using scale-free simulations

ABSTRACT In this study, we perform a halo-finder code comparison between rockstar and compaso. Based on our previous analysis aiming at quantifying resolution of N-body simulations by exploiting large (up to N = 40963) simulations of scale-free cosmologies run using abacus, we focus on convergence of the halo mass function, two-point correlation function, and mean radial pairwise velocities of halo centres selected with the aforementioned two algorithms. We establish convergence, for both rockstar and compaso, of mass functions at the 1 per cent precision level and of the mean pairwise velocities (and also two-point correlation function) at the 2 per cent level. At small scales and masses, we find that rockstar exhibits greater self-similarity. We also highlight the role played by the merger-tree post-processing of compaso haloes on their convergence. Finally, we give resolution limits expressed as a minimum particle number per halo in a form that can be directly extrapolated to Lambda cold dark matter.

Functional convergence of genomic and transcriptomic architecture underlies schooling behaviour in a live-bearing fish.

The organization and coordination of fish schools provide a valuable model to investigate the genetic architecture of affiliative behaviours and dissect the mechanisms underlying social behaviours and personalities. Here we used replicate guppy selection lines that vary in schooling propensity and combine quantitative genetics with genomic and transcriptomic analyses to investigate the genetic basis of sociability phenotypes. We show that consistent with findings in collective motion patterns, experimental evolution of schooling propensity increased the sociability of female, but not male, guppies when swimming with unfamiliar conspecifics. This finding highlights a relevant link between coordinated motion and sociability for species forming fission-fusion societies in which both group size and the type of social interactions are dynamic across space and time. We further show that alignment and attraction, the two major traits forming the sociability personality axis in this species, showed heritability estimates at the upper end of the range previously described for social behaviours, with important variation across sexes. The results from both Pool-seq and RNA-seq data indicated that genes involved in neuron migration and synaptic function were instrumental in the evolution of sociability, highlighting a crucial role of glutamatergic synaptic function and calcium-dependent signalling processes in the evolution of schooling.

Shifting from Web2 to Web3: The adaptive creator experiences on blockchain-based video-sharing and streaming platforms

Blockchains have inspired imaginaries of a new iteration of the internet, hailed as Web3, where the power of centralized platform companies would be limited and the ownership of personal data and content could be retained by their individual owners and creators. Web3 is expected to facilitate the emergence of novel protocols and platforms that enable decentralized coordination of data and digital assets. This article examines critically the experiences and imaginaries of creators working on two blockchain-based video-sharing platforms: Theta.tv and Odysee. Building on the studies of creator culture and institutionalist blockchain economics and based on open-ended interviews with the early adopters of these platforms, the paper investigates how the creators experience these decentralized social media applications in terms of their processes of governance, community creation, and career development. We show how the affordances of blockchains and creator expectations can result in further convergence of community management and career-building functions potentially benefiting creators. We also show that the new wave of decentralization, against optimistic blockchain visions, has not yet led to the distributed ‘ownership’ of social media networks. Rather, while blockchains seem to have increased creator autonomy and added career opportunities, novel forms of platform governance and power have also introduced new perceptions of precarity among creators.

Adaptive control of a class of uncertain nonlinear systems using brain emotional learning and Legendre polynomials

In this paper, an adaptive controller for a class of uncertain nonlinear systems is presented using a combination of Legendre polynomials and brain emotional learning-based intelligent controller (BELBIC). Recently, some versions of BELBIC have been presented with the aim of satisfying the universal approximation property using Gaussian basis function. However, the size of regressor vector is too large that imposes a heavy computational load to the processor. The novelty of this paper is presenting a new version of BELBIC with less computational burden using Legendre polynomials. Moreover, there are very few tuning parameters in Legendre polynomials. Another contribution of this paper is editing the stability analysis presented in recent related works. Due to the intrinsic non-differentiability of the adaptation rules of BELBIC, the second time derivative of Lyapunov function is undefined and thus, the Barbalat’s lemma cannot be applied to verify the asymptotic convergence of the error function. Therefore, bounded-input-bounded-output (BIBO) stability can only be claimed for this controller. Simulation results on different case studies show that Legendre polynomials can improve the universal approximation property of BELBIC with less tuning parameters. Moreover, in the absence of the robust control term in the control law, the performance Legendre polynomials will not deteriorate, while the performance degrade in Gaussian basis function is quite considerable.

A study of a nonlinear vibration isolator supported on an imperfect boundary plate

The research on the nonlinear vibration isolation of continuum systems always focuses on the perfect boundary conditions. In this work, nonlinear isolation of transverse vibrations of the arbitrary boundary rectangular plate was investigated. A novel analytical approach was proposed. The rigid body dynamics and high-order energy Fourier series expansion method were utilized to establish a mathematical model of the arbitrary boundary nonlinear continuum system. This method involves the simultaneous Fourier series expansion in temporal and spatial domains. The dynamic behaviors of an arbitrary boundary rectangular plate with nonlinear vibration isolators can be described through a harmonic balance analysis together with arc-length continuation. The convergence and stability for the frequency response functions were checked via the Lyapunov stability theory. The numerical results supported the analytical solutions. Both the analytical and numerical results demonstrated that nonlinear broadband vibration isolation is sensitive to the elasticity of the arbitrary boundary flexible foundation. Furthermore, increasing damping can effectively suppress the transmission of low-order vibrations and control the divergence of higher-order vibrations. Finally, an experiment rig was designed to validate the nonlinear isolation of the transverse vibrations of an arbitrary boundary plate.

Nonzero-Sum Game-Based Voltage Recovery Consensus Optimal Control for Nonlinear Microgrids System.

Since most of the existing models based on the microgrids (MGs) are nonlinear, which could cause the controller oscillate, resulting in the excessive line loss, and the nonlinear could also lead to the controller design difficulty of MGs system. Therefore, this article researches the distributed voltage recovery consensus optimal control problem for the nonlinear MGs system with N -distributed generations (DGs), in the case of providing stringent real power sharing. First, based on the distributed cooperative control concept of multiagent systems and the critic neural networks (NNs), a novel distributed secondary voltage recovery consensus optimal control protocol is constructed via applying the backstepping technique and nonzero-sum (NZS) differential game strategy to realize the voltage recovery of island MGs. Meanwhile, the model identifier is established to reconstruct the unknown NZS games systems based on a three-layer NN. Then, a critic NN weight adaptive adjustment tuning law is proposed to ensure the convergence of the cost functions and the stability of the closed-loop system. Furthermore, according to Lyapunov stability theory, it is proven that all signals are uniform ultimate boundedness in the closed loop system and the voltage recovery synchronization error converges to an arbitrarily small neighborhood of the origin near. Finally, some simulation results in MATLAB illustrate the validity of the proposed control strategy.

An Extensible Gradient-Based Optimization Method for Parameter Identification in Power Distribution Network

Accurate parameter identification of power distribution network (PDN) has attracted remarkable attention recently. However, power device parameters usually show an instability attributed to both the operating status and manual entry. Therefore, it is urgent to develop reliable algorithms for identifying PDN parameters with both high accuracy and high efficiency. Most of the existing algorithms are gradient-free and based on the heuristic schemes, resulting in an unstable numerical calculation. Herein, based on our previous work about the adaptive gradient-based optimization (AGBO) method, we propose an extensive version, namely, AGBO-Pro model. In this method, both the numerical and categorical features of experimental observations are utilized and incorporated with each via a weighted average. By comparing the proposed method with several heuristic algorithms, it is found that the errors in RMSE, MAE, and MAPE criteria via AGBO-Pro are all about 2 times lower with a much faster and more stable convergence of the loss function. By further taking a linear transformation of the loss function, the AGBO-Pro model achieves a more robust performance with a much lower variance in repeat numerical calculations. This work shows great potential in possible extension of gradient-based optimization methods for parameter identification in PDN.

Functional diversity and community convergence of land snails in the Aegean Sea islands

AbstractAimIsland biological communities are considered to comprise non‐random assemblages from surrounding source pools, but whether they converge towards predictable structural properties remains unclear. Here, we (i) test whether insular communities of land snails converge towards similar functional and/or taxonomic properties and (ii) evaluate whether island functional diversity is determined by island biogeographical characteristics such as area and distance to the pool as well as human‐related variables.LocationSixty‐six continental Aegean islands.TaxonLand snails.MethodsWe compiled a database of two morphological traits with functional significance (shell height and width) for 163 island species and 1529 species from the major species pools. We quantified inter‐specific morphological dissimilarity between pairs of islands (turnover), using a modified index of the mean nearest taxon distance. We tested for functional and taxonomic convergence using null models and assessing whether overall mean turnover among islands and pairwise island‐by‐island turnover were lower than expected by chance. We performed multiple regression analyses to test whether functional diversity metrics scale with island biogeographical characteristics and human‐related variables.ResultsOur analyses provide strong evidence that communities of land snails across the Aegean islands converge towards non‐random functional properties and taxonomic structure. At the island level, a wide range of different shell shapes is observed, indicating greater functional richness than expected by chance. Regression analyses showed that island area is the only efficient predictor of functional diversity, indicating that available ecological/resource space is of central importance in driving the assembly of different shell shapes.Main ConclusionsOur findings, consistent with previous studies of other taxa from oceanic islands, highlight that island species communities are not randomly assembled and display convergence in their functional and taxonomic composition. Integrating functional diversity metrics within biogeographic analyses has the potential to further our understanding of island biodiversity patterns.

Harnessing large language models (LLMs) for candidate gene prioritization and selection

BackgroundFeature selection is a critical step for translating advances afforded by systems-scale molecular profiling into actionable clinical insights. While data-driven methods are commonly utilized for selecting candidate genes, knowledge-driven methods must contend with the challenge of efficiently sifting through extensive volumes of biomedical information. This work aimed to assess the utility of large language models (LLMs) for knowledge-driven gene prioritization and selection.MethodsIn this proof of concept, we focused on 11 blood transcriptional modules associated with an Erythroid cells signature. We evaluated four leading LLMs across multiple tasks. Next, we established a workflow leveraging LLMs. The steps consisted of: (1) Selecting one of the 11 modules; (2) Identifying functional convergences among constituent genes using the LLMs; (3) Scoring candidate genes across six criteria capturing the gene’s biological and clinical relevance; (4) Prioritizing candidate genes and summarizing justifications; (5) Fact-checking justifications and identifying supporting references; (6) Selecting a top candidate gene based on validated scoring justifications; and (7) Factoring in transcriptome profiling data to finalize the selection of the top candidate gene.ResultsOf the four LLMs evaluated, OpenAI's GPT-4 and Anthropic's Claude demonstrated the best performance and were chosen for the implementation of the candidate gene prioritization and selection workflow. This workflow was run in parallel for each of the 11 erythroid cell modules by participants in a data mining workshop. Module M9.2 served as an illustrative use case. The 30 candidate genes forming this module were assessed, and the top five scoring genes were identified as BCL2L1, ALAS2, SLC4A1, CA1, and FECH. Researchers carefully fact-checked the summarized scoring justifications, after which the LLMs were prompted to select a top candidate based on this information. GPT-4 initially chose BCL2L1, while Claude selected ALAS2. When transcriptional profiling data from three reference datasets were provided for additional context, GPT-4 revised its initial choice to ALAS2, whereas Claude reaffirmed its original selection for this module.ConclusionsTaken together, our findings highlight the ability of LLMs to prioritize candidate genes with minimal human intervention. This suggests the potential of this technology to boost productivity, especially for tasks that require leveraging extensive biomedical knowledge.

Neural correlates of pronoun processing: An activation likelihood estimation meta-analysis

Pronouns are unique linguistic devices that allow for the expression of referential relationships. Despite their communicative utility, the neural correlates of the operations involved in reference assignment and/or resolution, are not well-understood. The present study synthesized the neuroimaging literature on pronoun processing to test extant theories of pronoun comprehension. Following the PRISMA guidelines and thebest-practice recommendations for neuroimaging meta-analyses, a systematic literature search and record assessment were performed. As a result, 16 fMRI studies were included in the meta-analysis, and were coded in Scribe 3.6 for inclusion in the BrainMap database. The activation coordinates for the contrasts of interest were transformed into Talairach space and submitted to an Activation Likelihood Estimation (ALE) meta-analysis in GingerALE 3.0.1. The results indicated that pronoun processing had functional convergence in the left posterior middle and superior temporal gyri, potentially reflecting the retrieval, prediction and integration roles of these areas for pronoun processing.

Prescribed-time control for hypersonic morphing vehicles with state error constraints and uncertainties

This paper focuses on the attitude control problem of hypersonic morphing vehicles (HMVs) with output state constraints and uncertainties in morphing phase. The main contribution of this work is not only realizing arbitrarily predefined settling time based on accelerated tracking rate but also ensuring smooth control performance under morphing effects and uncertainties. Firstly the control-oriented model is established based on the dynamic model of HMVs. Then, a prescribed-time disturbance observer (PTDO) is developed to enhance the robustness of flight control system based on radial basis function neural networks (RBFNN), allowing the lumped disturbances to be estimated within appointed time. Subsequently, based on tangent-type barrier Lyapunov function (TBLF) and prescribed-time convergence technique, the controller is designed via backstepping method, and the tracking errors of output states converge to the near origin within arbitrarily prescribed time without violating the preassigned constraints during tracking process. Finally, the closed-loop stability is ensured via Lyapunov synthesis, and numerical test results are presented to demonstrate the effectiveness of the proposed control scheme. Simulation results show that superior performance is obtained in terms of both transient process and stabilization.

RepVGG-YOLOv7: A Modified YOLOv7 for Fire Smoke Detection

To further improve the detection of smoke and small target smoke in complex backgrounds, a novel smoke detection model called RepVGG-YOLOv7 is proposed in this paper. Firstly, the ECA attention mechanism and SIoU loss function are applied to the YOLOv7 network. The network effectively extracts the feature information of small targets and targets in complex backgrounds. Also, it makes the convergence of the loss function more stable and improves the regression accuracy. Secondly, RepVGG is added to the YOLOv7 backbone network to enhance the ability of the model to extract features in the training phase, while achieving lossless compression of the model in the inference phase. Finally, an improved non-maximal suppression algorithm is used to improve the detection in the case of dense smoke. Numerical experiments show that the detection accuracy of the proposed algorithm can reach about 95.1%, which contributes to smoke detection in complex backgrounds and small target smoke.