Functional Convergence Research Articles

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.

Double lacunary statistical convergence of Δ-measurable functions on product time scales

Abstract We first present a notion of a double lacunary sequence on product time scales. Using this notion, we define the notions of the double lacunary statistical convergence and double lacunary strongly p-Cesàro summability of 2-multiple functions on product time scales and we study some fundamental properties of both notions. We also present a theorem that connects the above-mentioned two concepts. Furthermore, we define a refinement of a double lacunary sequence on product time scales and provide some fundamental properties as well as inclusion theorems for a refined and a non-refined double lacunary sequence on product time scales.

A Scalable Spatial–Temporal Correlated Non-Stationary Channel Fading Generation Method

To address the challenges of complex implementation structures and high hardware resource consumption in multiple-input multiple-output (MIMO) channel emulators, this paper proposes a hardware generation method for spatial–temporal correlated non-stationary channel fading. Firstly, a hardware generation architecture is developed for field-programmable gate array (FPGA) platforms, which can also reduce the complexity of the channel emulator. Secondly, an improved CORDIC method is introduced to reduce algorithm latency and hardware consumption while expanding the function convergence domain, with a relative error maintained at the level of 10-4. Furthermore, based on the idea of time-division multiplexing, an efficient hardware operation of a lower triangular matrix is adopted to minimize the consumption of hardware resources. Finally, the measured results demonstrate that the statistical characteristics of the channel fading generated by the proposed method are in good agreement with the theoretical ones, with an average error of less than 2%. Additionally, under identical simulation conditions, hardware resource consumption is reduced by 6.87%. These findings provide compelling evidence of the enhanced efficiency and accuracy in simulating MIMO channels achieved through the proposed method.

Primary complex motor stereotypies are associated with de novo damaging DNA coding mutations that identify KDM5B as a risk gene.

Motor stereotypies are common in children with autism spectrum disorder (ASD), intellectual disability, or sensory deprivation, as well as in typically developing children ("primary" stereotypies, pCMS). The precise pathophysiological mechanism for motor stereotypies is unknown, although genetic etiologies have been suggested. In this study, we perform whole-exome DNA sequencing in 129 parent-child trios with pCMS and 853 control trios (118 cases and 750 controls after quality control). We report an increased rate of de novo predicted-damaging DNA coding variants in pCMS versus controls, identifying KDM5B as a high-confidence risk gene and estimating 184 genes conferring risk. Genes harboring de novo damaging variants in pCMS probands show significant overlap with those in Tourette syndrome, ASD, and those in ASD probands with high versus low stereotypy scores. An exploratory analysis of these pCMS gene expression patterns finds clustering within the cortex and striatum during early mid-fetal development. Exploratory gene ontology and network analyses highlight functional convergence in calcium ion transport, demethylation, cell signaling, cell cycle and development. Continued sequencing of pCMS trios will identify additional risk genes and provide greater insights into biological mechanisms of stereotypies across diagnostic boundaries.

Design of freeform phase diffractive optical elements based on the quadratic assignment problem.

The design of freeform phase diffractive optical elements is a challenging task, typically necessitating the use of complex differential equations or a large number of iterative calculations. This paper proposes what we believe to be a novel approach to address this problem. In this strategy, we introduce overall comparison optimization (OCO) to ensure the fast convergence of the cost function. The quadratic assignment problem (QAP) is used as the mathematical framework for designing freeform phase diffraction optics. Specifically, the ray mapping calculation problem in geometric optics is simplified as a QAP. To solve this problem, we apply the OCO method, which ensures that the cost function rapidly progresses in the "non-negative" direction, thereby facilitating fast convergence in each optimization iteration. In this manner, the proposed approach alleviates the computational burden associated with repeated evaluations of the cost function and accelerates convergence in the design process. We construct holographic masks using the OCO method and perform simulations to demonstrate the potential of the proposed method in swiftly realizing complex illumination patterns. The results show that the design model has good performance when dealing with complex illumination tasks. The conclusions obtained in this paper can be extended to the realization of phase-only holography and the solution of freeform surfaces illumination design.

Two new conjugate gradient methods in unconstrained optimization problems

Zheng Y. and Zheng B. in [14] modified Dai-Liao conjugate gradient method to come up with two new Dai-Liao-type conjugate gradient methods. These methods were shown to have satisfied descent condition taken into consideration the strong Wolfe line search. Convergence for objective functions were also guarantied. In this work, two new conjugate gradient methods are introduced in line with the work of Zheng Y. and Zheng B. [14] by changing the first term in AyO-CG method [3] to solve unconstrained non-linear optimization problems. Descent properties of these methods are shown and guarantied. Convergence analyses of these methods in line with strong Wolfe conditions showed that they are globally convergent. Comparison based on Dolan More performance profile of the numerical strength of these methods with the two modified Dai-Laio type methods proved that our methods compare favorably well with them

Convergent evolution of dim light vision in owls and deep-diving whales

Animals with enhanced dim-light sensitivity are at higher risk of light-induced retinal degeneration when exposed to bright light conditions.1,2,3,4 This trade-off is mediated by the rod photoreceptor sensory protein, rhodopsin (RHO), and its toxic vitamin A chromophore by-product, all-trans retinal.5,6,7,8 Rod arrestin (Arr-1) binds to RHO and promotes sequestration of excess all-trans retinal,9,10 which has recently been suggested as a protective mechanism against photoreceptor cell death.2,11 We investigated Arr-1 evolution in animals at high risk of retinal damage due to periodic bright-light exposure of rod-dominated retinas. Here, we find the convergent evolution of enhanced Arr-1/RHO all-trans-retinal sequestration in owls and deep-diving whales. Statistical analyses reveal a parallel acceleration of Arr-1 evolutionary rates in these lineages, which is associated with the introduction of a rare Arr-1 mutation (Q69R) into the RHO-Arr-1 binding interface. Using invitro assays, we find that this single mutation significantly enhances RHO-all-trans-retinal sequestration by ∼30%. This functional convergence across 300 million years of evolutionary divergence suggests that Arr-1 and RHO may play an underappreciated role in the photoprotection of the eye, with potentially vast clinical significance.

UAV-assisted NOMA secure communications: joint transmit power and trajectory optimization

With the inherent advantages of exceptional maneuverability, flexible deployment options and cost-effectiveness, unmanned aerial vehicles (UAVs) present themselves as a viable solution for providing aerial communication services to Internet of Things devices in high-traffic or remote areas. Nevertheless, the openness of the air–ground channel poses significant security challenges to UAV-based wireless systems. In this paper, a UAV-assisted secure communication system model is established based on non-orthogonal multiple access (NOMA) from the perspective of physical layer security, aiming to conceal the transmission behavior between UAVs and legitimate users (LUs). Specifically, a mobile UAV assumes the role of an aerial base station, leveraging NOMA technique to transmit data to LUs while evading detection from mobile eavesdropper situated on the ground. To fortify the security performance of the system, a hovering UAV acts as a friendly jammer and transmits interference signals to mobile eavesdropper (referred to as Eve). The objective of this scheme is to maximize the minimum average secure rate of all LUs by meticulously optimizing the trajectory and power allocation of the mobile UAV, subject to secrecy performance constraints. The highly interdependent and non-convex nature of this optimization problem renders direct solutions infeasible. Hence, this paper designs an efficient iterative algorithm that decouples the original problem into two subproblems, enabling an alternating optimization process for the trajectory and power allocation of the mobile UAV until the convergence of the objective function is achieved. Simulation results demonstrate that the proposed algorithm effectively improves the minimum average secure rate of all LUs compared with the benchmark scheme.

Identification and evolution of nsLTPs in the root nodule nitrogen fixation clade and molecular response of Frankia to AgLTP24

Non-specific lipid transfer proteins (nsLTPs) are antimicrobial peptides, involved in several plant biological processes including root nodule nitrogen fixation (RNF). Nodulating plants belonging to the RNF clade establish symbiosis with the nitrogen-fixing bacteria rhizobia (legumes symbiosis model) and Frankia (actinorhizal symbiosis model) leading to root nodule formation. nsLTPs are involved in processes active in early step of symbiosis and functional nodule in both models. In legumes, nsLTPs have been shown to regulate symbiont entry, promote root cortex infection, membrane biosynthesis, and improve symbiosis efficiency. More recently, a nsLTP, AgLTP24 has been described in the context of actinorhizal symbiosis between Alnus glutinosa and Frankia alni ACN14a. AgLTP24 is secreted at an early step of symbiosis on the deformed root hairs and targets the symbiont in the nitrogen-fixing vesicles in functional nodules. nsLTPs are involved in RNF, but their functions and evolutionary history are still largely unknown. Numerous putative nsLTPs were found up-regulated in functional nodules compared to non-infected roots in different lineages within the RNF clade. Here, results highlight that nodulating plants that are co-evolving with their nitrogen-fixing symbionts appear to have independently specialized nsLTPs for this interaction, suggesting a possible convergence of function, which opens perspectives to investigate nsLTPs functions in RNF.

Quasi-monotone convergence of plurisubharmonic functions

The complex Monge-Ampère operator has been defined for locally bounded plurisubharmonic functions by Bedford-Taylor in the 80's. This definition has been extended to compact complex manifolds, and to various classes of mildly unbounded quasi-plurisubharmonic functions by various authors. As this operator is not continuous for the L1-topology, several stronger topologies have been introduced over the last decades to remedy this, while maintaining efficient compactness criteria. The purpose of this note is to show that these stronger topologies are essentially equivalent to the natural quasi-monotone topology that we introduce and study here.

Haplotype-resolved genome assembly provides insights into the evolution of S-locus supergene in distylous Nymphoides indica.

Distyly has evolved independently in numerous animal-pollinated angiosperm lineages. Understanding of its molecular basis has been restricted to a few species, primarily Primula. Here, we investigate the genetic architecture of the single diallelic locus (S-locus) supergene, a linkage group of functionally associated genes, and explore how it may have evolved in distylous Nymphoides indica, a lineage of flowering plants not previously investigated. We assembled haplotype-resolved genomes, used read-coverage-based genome-wide association study (rb-GWAS) to locate the S-locus supergene, co-expression network analysis to explore gene networks underpinning the development of distyly, and comparative genomic analyses to investigate the origins of the S-locus supergene. We identified three linked candidate S-locus genes - NinBAS1, NinKHZ2, and NinS1 - that were only evident in the short-styled morph and were hemizygous. Co-expression network analysis suggested that brassinosteroids contribute to dimorphic sex organs in the short-styled morph. Comparative genomic analyses indicated that the S-locus supergene likely evolved via stepwise duplications and has been affected by transposable element activities. Our study provides novel insight into the structure, regulation, and evolution of the supergene governing distyly in N. indica. It also provides high-quality genomic resources for future research on the molecular mechanisms underlying the striking evolutionary convergence in form and function across heterostylous taxa.

Deforestation strengthens environmental filtering and competitive exclusion in Neotropical streams and rivers.

Understanding how anthropization impacts the assembly of species onto communities is pivotal to go beyond the observation of biodiversity changes and reveal how disturbances affect the environmental and biotic processes shaping biodiversity. Here, we propose a simple framework to measure the assembly processes underpinning functional convergence/divergence patterns. We applied this framework to northern Amazonian fish communities inventoried using environmental DNA in 35 stream sites and 64 river sites. We found that the harsh and unstable environmental conditions characterizing streams conveyed communities towards functional convergence, by filtering traits related to food acquisition and, to a lower extent, dispersal. Such environmental filtering also strengthened competition by excluding species having less competitive food acquisition traits. Instead, random species assembly was more marked in river communities, which may be explained by the downstream position of rivers facilitating the dispersion of species. Although fish assembly rules differed between streams and river fish communities, anthropogenic disturbances reduced functional divergence in both ecosystems, with a reinforcement of both environmental filtering and weaker competitor exclusion. This may explain the substantial biodiversity alterations observed under slight deforestation levels in Neotropical freshwater ecosystems and underlines their vulnerability to anthropic disturbances that not only affect species persistence but also modify community assembly rules.

Functional evolutionary convergence of long noncoding RNAs involved in embryonic development

Long noncoding RNAs have been identified in most vertebrates, but the functional characterization of these molecules is challenging, mainly due to the lack of linear sequence homology between species. In this work, we aimed to find functional evolutionary convergent lncRNAs involved in development by screening of k-mer content (nonlinear similarity) and secondary structure-based approaches combining in silico, in vitro and in vivo validation analysis. From the Madagascar gecko genes, we have found a non-orthologous lncRNA with a similar k-mer content and structurally concordant with the human lncRNA EVX1AS. Analysis of function-related characteristics together with locus-specific targeting of human EVX1AS and gecko EVX1AS-like (i.e., CRISPR Display) in human neuroepithelial cells and chicken mesencephalon have confirmed that gecko EVX1AS-like lncRNA mimics human EVX1AS function and induces EVX1 expression independently of the target species. Our data shows functional convergence of non-homologous lncRNAs and presents a useful approach for the definition and manipulation of lncRNA function within different model organisms.

YOLO-SSFS: A Method Combining SPD-Conv/STDL/IM-FPN/SIoU for Outdoor Small Target Vehicle Detection

As a vital part of autonomous driving, vehicle detection, especially for outdoor small target vehicles, has attracted great attention from researchers during recent years. To ameliorate the difficulty in accurately identifying outdoor small vehicle targets in dense environments, this paper proposes a new structure named YOLO-SSFS, in which SPD-Conv, a small target detection layer (STDL), the Improved Feature Pyramid Network structure (IM-FPN), and the SCYLLA-IoU (SIoU) loss function are introduced. Firstly, the multi-scale fusion module of the original algorithm is improved by adding a detection layer for smaller targets. This detection layer preserves shallow semantic information, which helps to refine the algorithm’s detection accuracy for small targets. Then, a new Convolutional Neural Network (CNN) building block named SPD-Conv is constructed to replace the pooling layers and convolutional layers in the YOLOv5 algorithm, which reduces information loss, ensures the original fine-grained details of the image and improves the learning ability. Afterwards, a new FPN structure is created to retain more information conducive to small target detection during the feature fusion process so as to enhance the robustness of the method. Finally, to speed up the convergence of the loss function, the SIoU loss function is introduced to replace Complete-IoU (CIoU) in the original algorithm. In order to verify the authenticity of the improved algorithm, we conduct a series of experiments on the VisDrone dataset and perform comparative analyses of the results. The results obtained demonstrate that compared with the original YOLOv5, the proposed model performs better in small target detection. The mean average precision (mAP) is 83.07%, which is 7.63% higher than that for YOLOv5, while the detection speed reaches 52 frames per second (FPS), meeting the requirements for real-time detection.

Adaptive Techniques for the Online Estimation of Spatial Fields With Mobile Sensors

The regression-based reconstruction of spatial fields by static sensors requires the number of measurements be at least equal to the number of unknown parameters in order to provide a unique solution. Improvements include the use of mobile sensor networks to arrive at higher accuracy estimates. An alternate approach utilizes adaptive techniques to update the parameter estimates online. Such adaptive techniques need persistence of excitation to ensure convergence of the parameter and function estimates. It is shown that the motion of just a single sensor is a necessary condition for persistence of excitation and, subsequently, of function convergence. Having a prescribed sensor guidance is also a sufficient condition for function convergence. First, a Lyapunov-based adaptation and guidance is presented and it is shown that persistence of excitation necessitates the time dependence of the outer product of the regressor vector with itself and which in turn requires the motion of the sensor within the spatial domain. Subsequently, a gradient-based adaptive scheme relaxes the Lyapunov-based sensor guidance to any user-defined guidance as long as the outer product of the associated rank-one regressor vector with itself has a uniformly positive definite integral. The latter adaptation is conducive to the inclusion of platform dynamics resulting in a vehicle control design needed to attain parameter convergence. Extension of the centralized solution to distributed estimation using a network of mobile sensors is also examined. These results are demonstrated with simulations in one and two dimensions.

Percolation on Triangulations: A Bijective Path to Liouville Quantum Gravity

We set the foundation for a series of works aimed at proving strong relations between uniform random planar maps and Liouville quantum gravity (LQG). Our method relies on a bijective encoding of site-percolated planar triangulations by certain 2D lattice paths. Our bijection parallels in the discrete setting the mating-of-trees framework of LQG and Schramm-Loewner evolutions (SLE) introduced by Duplantier, Miller, and Sheffield. Combining these two correspondences allows us to relate uniform site-percolated triangulations to 8 / 3 \sqrt {8/3} -LQG and SLE 6 _6 . In particular, we establish the convergence of several functionals of the percolation model to continuous random objects defined in terms of 8 / 3 \sqrt {8/3} -LQG and SLE 6 _6 . For instance, we show that the exploration tree of the percolation converges to a branching SLE 6 _6 , and that the collection of percolation cycles converges to the conformal loop ensemble CLE 6 _6 . We also prove convergence of counting measure on the pivotal points of the percolation. Our results play an essential role in several other works, including a program for showing convergence of the conformal structure of uniform triangulations and works which study the behavior of random walk on the uniform infinite planar triangulation.

Stochastic homogenisation of free-discontinuity functionals in randomly perforated domains

Abstract In this paper, we study the asymptotic behaviour of a family of random free-discontinuity energies E ε {E_{\varepsilon}} defined in a randomly perforated domain, as ε goes to zero. The functionals E ε {E_{\varepsilon}} model the energy associated to displacements of porous random materials that can develop cracks. To gain compactness for sequences of displacements with bounded energies, we need to overcome the lack of equi-coerciveness of the functionals. We do so by means of an extension result, under the assumption that the random perforations cannot come too close to one another. The limit energy is then obtained in two steps. As a first step, we apply a general result of stochastic convergence of free-discontinuity functionals to a modified, coercive version of E ε {E_{\varepsilon}} . Then the effective volume and surface energy densities are identified by means of a careful limit procedure.

Rigidity of mean convex subsets in non-negatively curved RCD spaces and stability of mean curvature bounds

We prove splitting theorems for mean convex open subsets in Riemannian curvature-dimension (RCD) spaces that extend results by Kasue et al. for Riemannian manifolds with boundary to a non-smooth setting. A corollary is for instance Frankel’s theorem. Then, we prove that our notion of mean curvature bounded from below for the boundary of an open subset is stable with respect to uniform convergence of the corresponding boundary distance function. We apply this to prove almost rigidity theorems for uniform domains whose boundary has a lower mean curvature bound.

Anomaly Detection for Agile Satellite Attitude Control System Using Hybrid Deep-Learning Technique

Agile low-Earth-orbit (LEO) observation satellites need a robust attitude control and determination system. It is a critical satellite subsystem, which stabilizes the satellite to different desired orientations during its mission using different actuators. The detection of satellite misorientation is a highly challenging problem because it requires continuous monitoring of data from hundreds of satellite sensors to guarantee healthy operability. In this paper, the authors propose a data-driven deep-learning framework for detecting satellite misorientation by analyzing attitude control subsystem telemetry data. The proposed approach combines a hybrid predictive deep-learning model that consists of long short-term memory and convolutional neural networks in two parallel paths to predict telemetry data and a robust isolation forest classifier for anomaly detection purposes that can classify output residuals as normal or anomalous. The hybrid model was optimized by the particle swarm optimization algorithm to ensure faster fitness function convergence with optimal model hyperparameters. The suggested data-driven model was validated using real telemetry datasets, including real anomalous case studies. The experimental results proved the suggested approach’s superiority for identifying satellite misorientation as well as helping satellite operators monitor the system’s health and deduce the causes of anomalies to aid in decision-making.