Functional Constraints Research Articles

Event-Triggered Bipartite Formation Control for Switched Nonlinear Multi-Agent Systems with Function Constraints on States

A distributed adaptive fuzzy event-triggered bipartite formation tracking control scheme is proposed for switched nonlinear multi-agent systems (MASs) with function constraints on states. Fuzzy logic systems (FLSs) are used to identify uncertain items. To improve the transient performance of the system, a fixed-time prescribed performance function (FTPPF) is introduced to make the formation error converge to a prescribed boundary range within a fixed time. Considering that the state constraint boundary is restricted by multiple pieces of information (historical state, topological relationship, neighbor agent output, leader signal and time), a tan-type barrier Lyapunov function (BLF) is constructed to address the challenges brought by the state function constraint. The shortcoming of the “explosion of complexity” is compensated by fusing the backstepping control and command filter. To mitigate the communication burden while ensuring a steady-state performance, a distributed event-triggered fixed-time bipartite formation control scheme is proposed. Finally, the performance of the proposed control method is verified by an MAS consisting of four followers and one leader.

Optimizing plant species composition of green roofs for ecological functionality and biodiversity conservation

Green roofs provide vital functions within the urban ecosystem, from supporting biodiversity, to sustainable climate-positive ESS provisioning. However, how plant communities should best be designed to reach these objectives, and how specific green roof systems vary in their capacity to support these functions is not well understood. Here we compiled data on plant traits and plant–insect interaction networks of a regional calcareous grassland species pool to explore how designed plant communities could be optimised to contribute to ecological functionality for predefined green roof solutions. Five distinct systems with practical functionality and physical constraints were designed, plant communities modelled using object-based optimization algorithms and evaluated using five ecological functionality metrics (incl. phylogenetic and structural diversity). Our system plant communities supported a range of plant–insect interactions on green roofs, but not all species were equally beneficial, resulting in wide-ranging essentiality and redundancy in ecological processes. Floral traits were not predictive of pollinator preferences, but phylogeny was observed to govern the preferences. Large differences in ecological functionality can be expected between green roofs depending on system design and the extent of the plant community composition. Multifunctionality covariance diverged between systems, suggesting that ecological functionality is not inherently universal but dependent on structural limitations and species pool interactions. We conclude that informed system design has a potential to simultaneously support ecosystem services and urban biodiversity conservation by optimising green roof plant communities to provide landscape resources for pollinating insects and herbivores.

Research on Water Flow Control Strategy for PEM Electrolyzer Considering the Anode Bubble Effect

At higher current densities, the bubble effect in the anode flow field of the PEM electrolyzer (PEM EL) worsens mass transfer losses and energy consumption. This study employs a moderate increase in the water flow rate to remove accumulated bubbles under fluctuating electrical input, thereby improving PEM EL system efficiency. An enhanced PEM EL equivalent circuit model incorporating bubble over-potential based on the oxygen volume fraction is developed. Considering the energy consumption of auxiliary equipment and the reduction in losses from mitigating the bubble effect, a numerical simulation evaluates the impact of flow rate variations on overall electrolysis energy consumption, leading to a comprehensive energy consumption model for the PEM EL system, incorporating electrical, chemical, and thermal energy conversions. The control objective is to maximize system efficiency by optimizing the water flow rate, with a performance-preset-based controller implemented in MATLAB/Simulink. The simulation results show that the controller can accurately track the target flow rate, and the dynamic regulation time improved by 1.5 s compared to the traditional performance constraint function, better matching the rate of change in electrical energy. Under the water flow control mode, hydrogen production increased by 6.6 L within 130 s of the simulation, available energy increased by 8.32 × 106 J, and the efficiency of the PEM EL system improved by 2.79%.

The role of river connectivity in the distribution of fish in an anthropized watershed.

The ongoing biodiversity crisis is especially severe in freshwater habitats. Anthropized watersheds, such as the Seine-Normandie basin in France, are particularly affected by human interference. The study of fish species distribution in watersheds often relies on environmental drivers such as land use or climate. Yet, fish are also exposed to river connectivity constraints, such as dams, that are understudied despite their potential impact on fish dispersal. For this study, we investigated the role of local and whole-basin longitudinal connectivity in fish distribution. We designed connectivity indices based on river network characteristics and specific mobility for 33 species and included these indices in species distribution models, taking into account habitat suitability, to quantify their role in species distribution. Keeping the best index for each species, an average of 29% - and up to 57% - of explained fish distribution, depending on species, was tied to connectivity. We found that high connectivity often had a significant and positive linear effect on species presence probability. Using a scoring system across multiple indices, we found connectivity indices that took local context into account (e.g. the ecological zonation of the river) performed consistently better than others. Indices that took only dispersal limitation into account scored higher for 12 species, while barriers, alone, were the most important constraint for 10 species, the remaining 11 being associated with both. This work points to fragmentation as a cause for lower likelihood of presence for many non-diadromous river fish species. It highlights the importance of considering both physical and functional connectivity constraints in fish distribution and provides additional insights for river management and restoration.

Probing the functional constraints of influenza A virus NEP by deep mutational scanning

Probing the functional constraints of influenza A virus NEP by deep mutational scanning

Ground and Space (LEO) Based GNSS Ionospheric Detection Using Chapman Function Constraint

AbstractLimited by the number and location distribution of ground stations, the ground based ionospheric detection using Global Navigation Satellite Systems (GNSS) technology suffers from low accuracy and poor reliability in some regions. To improve the performance of the global ionospheric model, this study combines the Continuously Operating Reference Station and the Low Earth Orbit (LEO) Satellite observation to conduct the ground and space‐based (G/SBased) joint ionospheric detection technique. With reference to the ionospheric radar data of the global ionospheric radio observatory, the performance of the G/SBased GNSS joint ionospheric detection technique was tested in quiet and disturbed geomagnetic environments. Results show that in the ground and space based joint mode, the distribution of ionospheric puncture points (IPPs) is uniform, thus effectively avoiding the problem of IPPs blank in the absence of ground stations. In the quiet geomagnetic environment, the ionospheric detection of the G/SBased joint mode has a remarkable performance improvement compared with the ground GNSS mode, with an average improvement of 55.51%. In the geomagnetically disturbed environment, the G/SBased joint mode still has high consistency with the ionospheric radar results, and the detection performance is better than that of the ground GNSS mode. This research shows that combining with ground GNSS and LEO satellite observation data can substantially provide the performance of GNSS ionospheric total electron content detection and can provide good data support for the construction of a global ionospheric refinement model.

A smooth gradient approximation neural network for general constrained nonsmooth nonconvex optimization problems.

Nonsmooth nonconvex optimization problems are pivotal in engineering practice due to the inherent nonsmooth and nonconvex characteristics of many real-world complex systems and models. The nonsmoothness and nonconvexity of the objective and constraint functions bring great challenges to the design and convergence analysis of the optimization algorithms. This paper presents a smooth gradient approximation neural network for such optimization problems, in which a smooth approximation technique with time-varying control parameter is introduced for handling nonsmooth nonregular objective functions. In addition, a hard comparator function is introduced to ensure that the state solution of the proposed neural network remains within the nonconvex inequality constraint sets. Any accumulation point of the state solution of the proposed neural network is proved to be a stationary point of the nonconvex optimization under consideration. Furthermore, the neural network demonstrates the ability to find optimal solutions for some generalized convex optimization problems. Compared with the related neural networks, the constructed neural network has weaker convergence conditions and simpler algorithm structure. Simulation results and an application in optimizing condition number verify the practical applicability of the presented algorithm.

Dynamic Analysis and Neural-Adaptive Prescribed-Time Control of the FO Memristive Magnetic-Field Electromechanical Transducer.

This article is concerned with dynamic analysis and neural-adaptive prescribed-time control of the magnetic-field electromechanical transducer incorporating a memristor. First, a fractional-order (FO) mathematical model is developed, which comprehensively characterizes fractional properties of various dielectrics and establishes the relationship between magnetic flux and electric charge. The dynamical analysis explores internal evolution and complexity performance concerning a single factor or double factors among the FO, system parameter, and memristor configuration by the Bifurcation diagram, sample entropy, and C0 complexity from multiple perspectives. Subsequently, a neural-adaptive prescribed-time control scheme is proposed to transform detrimental chaotic oscillations into orderly motions, achieve the pregiven tracking precision and accommodating both actuator fault and system uncertainty. The controller design consists of three key steps: 1) a deferred constraint function is imposed on the tracking error starting from anywhere to get assignable tracking precision within a specified time, ensuring collision avoidance; 2) a type-2 fuzzy wavelet neural network (FWNN) is utilized effectively to handle parameter perturbations and system uncertainties; and 3) a second-order FO tracking differentiator (TD) is utilized to address the "explosion of complexity" of traditional backstepping under actuator fault model. It is shown that the proposed scheme is able to ensure the boundness of all signals of the closed-loop system. Finally, extensive simulation experiments are conducted to validate the effectiveness and robustness of the rendered scheme.

Комбинированный метод планирования релизов в Agile-командах на основании генетических алгоритмов

The paper analyzes the practices of release planning and task allocation in agile project management using multi-criteria optimization methods and genetic algorithms. A combined method that solves the problem of selecting re-quirements for release (Next Release Problem) based on NSGA-II algorithm and optimization of task scheduling con-sidering resource constraints and sequence of stages (Resource Constrained Project Scheduling Problem) is developed. The peculiarities of using DEAP library in Python to implement evolutionary computations are described, as well as the use of NumPy library to accelerate vector operations and Matplotlib to visualize the results. The architecture of the software solution is presented, including modular code organization and integration of custom heuristics for building optimal schedules. The description of utility, risk and penalty functions for dependency violations and resource con-straints is given, as well as the application of crossover and mutation methods for dictionary structures of schedules. Iterative optimization linking the stages of epics selection and schedule construction to reach a stable optimum is shown. Experimental validation confirmed the effectiveness of the proposed approach as it achieves utility maximiza-tion of the set of epics, minimization of penalties and idle time of employees in constructing optimal schedules. The developed system is applicable for complex release scheduling in Agile teams and forms the basis for further research in agile project management and resource optimization.

MATHEMATICAL MODEL AND OPTIMIZATION ALGORITHM ACCORDING TO THE CRITERIA OF MINIMUM CONTACT STRESS FOR INVOLUTE SPUR GEARS WITH INCREASED CONTACT RATIO

Reducing the mass and dimensions of involute spur gears is an actual task of modern mechanical engineering. One of the perspective ways to solve it is the use of gearing with an increased working tooth depth and profile contact ratio εα ≥ 2. The study is devoted to the development of optimal design methods for such gears. Optimality criteria is formulated as follows: the contact stresses in the pitch point must take a minimum value when all constructive, geometric, kinematic, and technological constraints are met, first, when the profile contact ratio εα ≥ 2 is ensured. Variables planning are defined. These are addendum coefficients of the basic racks tooth for pinion and wheel h*a1, h*a2; profile angle of the basic rack α; addendum modification coefficient of the pinion x1. Formed a system of constraints for the variables planning: the main functional constraint of the minimum value of the profile contact ratio: εα ≥ 2; constraint for the addendum coefficients of the basic racks tooth for pinion and wheel h*a1, h*a2; constraint for profile angle of the basic rack α; constraint for addendum modification coefficients x1, x2; absence of the cutter interference for tooth dedendum; absence of the sharpening for tooth addendum; absence of the mesh interference; ensuring the bending strength of pinion and wheel teeth. A method for solving the problem of optimal design is chosen. The method of probing the space of design parameters was chosen from all the variety. The points of the LPτ-sequence are used as test points. The method allows you to operate with a significant number of parameters – up to 51, provides a sufficiently large number of evenly distributed test points – up to 220. The algorithm for optimal design of gear has been developed. The main stages of the algorithm are as follows: setting input data (numerical constraints on design variables, gear parameters and its load); generation of LPτ-sequence for variables planning with simultaneous consideration of their numerical constraints; checking of functional constraints; additional verification calculations (if necessary) of the gearing contact strength; formation of an array of possible solutions; searching of the best solution variant (the test point corresponding to the minimum value of the objective function) by sorting the array.

On higher-order strong KKT optimality conditions for multiobjective semi-infinite programming problems

This paper is intended to study higher-order necessary optimality conditions for multiobjective semi-infinite programming problems, in which the objective and constraint functions are considered to be m-stable at a reference point. Two data qualifications, called the weak Abadie data qualification (WADQ) and the strong Su–Luu data qualification (SSLDQ), are introduced in terms of mth-order upper Studniarski derivatives. The mth-order strong KKT necessary optimality conditions for a local weakly efficient solution are presented under the WADQ and the SSLDQ. Additionally, if the reference point is further a local Borwein-properly efficient solution, the mth-order strong KKT necessary optimality conditions are satisfied without requiring the assumption of SSLDQ.

Robustness of ancestral sequence reconstruction to among-site evolutionary heterogeneity and epistasis.

Ancestral sequence reconstruction (ASR) is typically performed using homogeneous evolutionary models, which assume that the same substitution propensities affect all sites and lineages. These assumptions are routinely violated: heterogeneous structural and functional constraints favor different amino acid states at different sites, and these constraints often change among lineages as epistatic substitutions accrue at other sites. To evaluate how realistic violations of the homogeneity assumption affect ASR, we developed site-specific substitution models and parameterized them using data from deep mutational scanning experiments on three protein families; we then used these models to perform ASR on the empirical alignments and on alignments simulated under heterogeneous conditions derived from the experiments. Extensive among-site and -lineage heterogeneity is present in these datasets, but the sequences reconstructed from empirical alignments are almost identical, irrespective of whether heterogeneous or homogeneous models are used for ASR. The rare differences occur primarily when phylogenetic signal is weak - at fast-evolving sites and nodes connected by long branches. When ASR is performed on simulated data, errors in the reconstructed sequences become more likely as branch lengths increase, but incorporating heterogeneity into the model does not improve accuracy. These data establish that ASR is robust to unincorporated realistic forms of evolutionary heterogeneity, because the primary determinant of ASR is phylogenetic signal, not the substitution model. The best way to improve accuracy is therefore not to develop more elaborate models but to apply ASR to densely sampled alignments that maximize phylogenetic signal at the nodes of interest.

Identification of Retrocopies in Lepidoptera and Impact on Domestication of Silkworm.

During the domestication of silkworm, an economic insect, its physiological characteristics have changed greatly. RNA-based gene duplication, known as retrocopy, plays an important role in the formation of new genes and genome evolution, but the retrocopies of lepidopteran insects have not been fully identified and analyzed, which not only severely limits researchers from exploring the effects of retrocopies on lepidopteran insects but also affects the studies on the domestication of silkworm. We compared the genomes and proteomes of eight lepidopteran insects and used a series of screening criteria for auxiliary screening to obtain the retrocopies in lepidopteran insects and explored their characteristics. In addition, based on the silkworm transcriptome data from the SilkDB3.0 website, we explored the functions of the retrocopies on the domestication of the silkworm. A total of 1993 retrocopies and 1208 parental genes in lepidopteran insects were obtained. We revealed that the retrocopies in Lepidoptera do not conform to the "out of X" hypothesis but fit the "out of testis" hypothesis. These retrocopies were subject to strong functional constraints and performed important functions in growth and development. Transcriptome analysis revealed that the expression pattern of the retrocopies and their parental genes were irrelevant. Through the analysis of the retrocopies in silkworm generated after domestication and located in the candidate domestication regions, the possible universal connection between the retrocopies and the domestication of silkworm were found. Our study pioneered the exploration of retrocopies in multiple Lepidoptera species and found the potential association between the retrocopies and the domestication of silkworm.

Functional traits and adaptation of lake microbiomes on the Tibetan Plateau

BackgroundTibetan Plateau is credited as the “Third Pole” after the Arctic and the Antarctic, and lakes there represent a pristine habitat ideal for studying microbial processes under climate change.ResultsHere, we collected 169 samples from 54 lakes including those from the central Tibetan region that was underrepresented previously, grouped them to freshwater, brackish, and saline lakes, and generated a genome atlas of the Tibetan Plateau Lake Microbiome. This genomic atlas comprises 8271 metagenome-assembled genomes featured by having significant phylogenetic and functional novelty. The microbiomes of freshwater lakes are enriched with genes involved in recalcitrant carbon degradation, carbon fixation, and energy transformation, whereas those of saline lakes possess more genes that encode osmolyte transport and synthesis and enable anaerobic metabolism. These distinct metabolic features match well with the geochemical properties including dissolved organic carbon, dissolved oxygen, and salinity that distinguish between these lakes. Population genomic analysis suggests that microbial populations in saline lakes are under stronger functional constraints than those in freshwater lakes. Although microbiomes in the Tibet lakes, particularly the saline lakes, may be subject to changing selective regimes due to ongoing warming, they may also benefit from the drainage reorganization and metapopulation reconnection.ConclusionsAltogether, the Tibetan Plateau Lake Microbiome atlas serves as a valuable microbial genetic resource for biodiversity conservation and climate research.9n3SXpnTm6EbgSwcGAa_6cVideo

Observer-based adaptive control for uncertain nonholonomic systems subject to time-varying function constraints on states

Observer-based adaptive control for uncertain nonholonomic systems subject to time-varying function constraints on states

MPC Path Tracking Control Based on GA-PAO

In order to improve the path tracking performance and stability of driverless vehicles. In this paper, a path tracking control strategy based on MPC is proposed to establish a three-degree-of-freedom vehicle dynamical model as a reference model, design the MPC controller, determine the objective function and add constraints. Optimization of important time-domain parameters of model predictive controllers by improved GA-PSO. A Carsim/Simulink co-simulation platform was built to simulate the controller under double-shifted line conditions to verify its effectiveness. Simulation results show that the tracking performance and stability of the optimized MPC controller are much better.

Extraction of agricultural plastic greenhouses based on a U-Net convolutional neural network coupled with edge expansion and loss function improvement

ABSTRACT Agricultural plastic greenhouses (APGs) are crucial for sustainable agricultural planting, and accurate spatial distribution information acquisition is crucial. Deep learning network models can extract target features from remote sensing images more effectively than traditional interpretation methods, which face challenges like high workloads and poor repeatability. In this study, we aim to enhance the inventorying of Agricultural Plastic Greenhouses (APGs) by improving the extraction accuracy of their locations and numbers through remote sensing techniques. Utilizing GF-7 satellite imagery, we propose an enhanced U-Net convolutional neural network (CNN) model that incorporates edge information expansion and a joint loss function to optimize performance. The primary objective is to provide a rapid and accurate method for mapping APGs, which is crucial for effective agricultural management and environmental monitoring. The U-Net network’s accuracy was enhanced by 1.1% after expanding 3 × 3 sample edge information, and further by 1.9% by combining edge extension and loss function constraints. Our results demonstrate that the modified U-Net model significantly improves extraction accuracy compared to traditional methods, thereby facilitating better inventory management and planning for agricultural cash crops. This advancement not only supports farmers in optimizing resources but also contributes to sustainable agricultural practices by enabling precise monitoring of APG distribution. Implications: Compared to traditional interpretation methods, which suffer problems such as heavy workloads, small adaptation ranges and poor repeatability, deep learning network models can better extract target features from remote sensing images. In this study, we used GF-7 image data to improve the traditional U-Net convolutional neural network (CNN) model. The Canny operator and Gaussian kernel (GK) function were used for sample edge expansion, and the binary cross-entropy and GK functions were used to jointly constrain the loss. Finally, APGs were accurately extracted and compared to those obtained with the original model. The results indicated that the APG extraction accuracy of the U-Net network was improved through the expansion of sample edge information and adoption of joint loss function constraints.

Kinematic Analysis and Trajectory Planning of Multi-Segment Continuum Manipulator

Abstract The flexible or continuum manipulators present excellent dexterity in confined space, which is beneficial to wide application prospects in many fields. In this paper, we establish the kinematic model and propose a trajectory planning algorithm for multi-segment continuum manipulators. The multi-level mapping from the actuator variables to the manipulator tip is described. A novel modal kinematic method is proposed to overcome the singularity problem generated by the piecewise constant curvature kinematics modeling method. Then, the improved particle swarm optimization algorithm is proposed to solve the problem of redundant inverse kinematics and realize the trajectory tracking of the manipulator tip. In addition, the configuration parameters are restricted by the constraint functions, so that the precise shape of the multi-segment manipulator can be controlled. Finally, a series of numerical simulations are conducted to implement the investigation. Simulation results demonstrate that the proposed modal kinematic method and the trajectory planning algorithm are effective for multi-segment continuum manipulator.

Building a simpler moderated nonlinear factor analysis model with Markov Chain Monte Carlo estimation.

Moderated nonlinear factor analysis (MNLFA) has emerged as an important and flexible data analysis tool, particularly in integrative data analysis setting and psychometric studies of measurement invariance and differential item functioning. Substantive applications abound in the literature and span a broad range of disciplines. MNLFA unifies item response theory, multiple group, and multiple indicator multiple cause modeling traditions, and it extends these frameworks by conceptualizing latent variable heterogeneity as a source of differential item functioning. The purpose of this article was to illustrate a flexible Markov chain Monte Carlo-based approach to MNLFA that offers statistical and practical enhancements to likelihood-based estimation while remaining plug and play with established analytic practices. Among other things, these enhancements include (a) missing data handling functionality for incomplete moderators, (b) multiply imputed factor score estimates that integrate into existing multiple imputation inferential methods, (c) support for common data types, including normal/continuous, nonnormal/continuous, binary, ordinal, multicategorical nominal, count, and two-part constructions for floor and ceiling effects, (d) novel residual diagnostics for identifying potential sources of differential item function, (e) manifest-by-latent variable interaction effects that replace complex moderation function constraints, and (f) integration with familiar regression modeling strategies, including graphical diagnostics. A real data analysis example using the Blimp software application illustrates these features. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Optimal consumption under a drawdown constraint over a finite horizon

This paper studies a finite horizon utility maximization problem on excessive consumption under a drawdown constraint. Our control problem is an extension of the one considered in Angoshtari et al. (2019) to the model with a finite horizon and an extension of the one considered in Jeon and Oh (2022) to the model with zero interest rate. Contrary to Angoshtari et al. (2019), we encounter a parabolic nonlinear HJB variational inequality with a gradient constraint, in which some time-dependent free boundaries complicate the analysis significantly. Meanwhile, our methodology is built on technical PDE arguments, which differs from the martingale approach in Jeon and Oh (2022). Using the dual transform and considering the auxiliary variational inequality with gradient and function constraints, we establish the existence and uniqueness of the classical solution to the HJB variational inequality after the dimension reduction, and the associated free boundaries can be characterized in analytical form. Consequently, the piecewise optimal feedback controls and the time-dependent thresholds for the ratio of wealth and historical consumption peak can be obtained.