Objective Function Research Articles

Prevalence and associated body composition factors of sarcopenia in community-dwelling older adults

BackgroundThe objective of this investigation was to examine the frequency and determinants related to body composition of sarcopenia among aging individuals residing in the Beijing community through implementation of the diagnostic criteria for sarcopenia outlined by the 2019 Asian Working Group for Sarcopenia (AWGS).MethodsA cross-sectional examination employing multistage cluster sampling was conducted on a sample consisting of 933 individuals aged 50 years or above. The study utilized the AWGS 2019 criteria for muscle mass, muscle strength, and physical function to gauge sarcopenia. In addition, data on demographic characteristics, anthropometry, and body composition were collected. Logistic regression analysis was carried out to ascertain the relationships between sarcopenia and correlated factors.ResultsThe study found that the overall prevalence of sarcopenia was 8.3%. Sex (p < 0.001) and body mass index (BMI p < 0.001) were determined to have a significant association with the occurrence of sarcopenia. The adjusted analyses demonstrated that with each standard deviation increase in fat-free mass index (OR = 0.02 (0.01–0.05), of 95%), skeletal muscle mass (OR = 0.17 (0.12–0.25), of 95%), and other body composition indicators (including total body water 0.26 (0.2–0.35), intracellular water 0.1 (0.06–0.17), protein 0.01 (0–0.02), soft lean mass 0.35 (0.28–0.44), BMI 0.58 (0.51–0.67), body cell mass 0.21 (0.15–0.29), OR < 0.60, of 95%), the risk of sarcopenia decreased by more than 40%. Certain straightforwardly accessible anthropometric indices such as upper arm circumference (correlation coefficient 0.94 (0.9–0.97), 0.97 (0.95–1), 1.01 (0.99–1.03), respectively, of 95%), waist circumference (0.94(0.92–0.96), 1.01 (0.99–1.03), 1.02 (1–1.03), of 95%), hip circumference (0.93 (0.9–0.96), 0.99 (0.97–1.02), 1.02 (1–1.04), of 95%), waist-to-hip ratio (0.93 (0.87–1), 1.04 (1–1.08), 1 (0.97–1.03), of 95%), body fat percentage (1 (0.98–1.02), 1.02(1–1.04), 1.01 (0.99–1.02), of 95%), and phase angle (0.91 (0.89–0.93), 0.96 (0.94–0.98), 0.99 (0.98–1), of 95%) exhibited substantial correlation with skeletal muscle index, muscle strength or physical performance. However, no noteworthy link between sarcopenia and most variables associated with physical function was discovered.ConclusionsThe findings of the study highlight the prevalence of sarcopenia among the aging residents of Beijing. Certain easily measured anthropometric indices demonstrated strong correlation with muscle strength or physical performance, thereby providing an avenue for screening and diagnosing sarcopenia in older people who may not be able to undergo grip strength or physical function assessments. To investigate causal relationships, future studies employing longitudinal or interventional designs with a more comprehensive population are warranted.

Preload Multi-Objective Optimization Method for Ultrasonic Motors Based on NSGA-II

The preload has a significant impact on the output performance of ultrasonic motors. To achieve optimal output from the ultrasonic motor, this paper proposes a novel multi-objective optimization method for preload, utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve the problem of determining the optimal preload. Firstly, a platform for preload adjustment and testing is established to conduct experimental tests on the motor’s characteristics. Based on the experimental data, the influence of preload on the motor’s output performance is analyzed. Secondly, linear regression models are established for three performance indicators: no-load speed, stall torque, and output efficiency with respect to preload, and the objective functions are fitted accordingly. Finally, the NSGA-II algorithm is used for multi-objective optimization with three objectives to obtain the Pareto front and determine the optimal preload for the ultrasonic motor. Simulation and experimental results show that after preload optimization, the no-load speed increased by 4.93%, stall torque increased by 26.10%, and output efficiency increased by 11.28%. Compared to existing optimization methods, this approach has lower computational complexity and better optimization performance, ensuring higher optimization accuracy and precision. The preload optimization method based on NSGA-II can improve the performance of ultrasonic motors in practical applications.

Neighborhood persistency of the linear optimization relaxation of integer linear optimization

AbstractFor an integer linear optimization (ILO) problem, persistency of its linear optimization (LO) relaxation is a property that for every optimal solution of the relaxation that assigns integer values to some variables, there exists an optimal solution of the ILO problem in which these variables retain the same values. Although persistency has been used to develop heuristic, approximation, and fixed-parameter algorithms for special cases of ILO, its applicability remains unknown in the literature. In this paper, we reveal a maximal subclass of ILO such that its LO relaxation has persistency. Specifically, we show that the LO relaxation of ILO on unit-two-variable-per-inequality (UTVPI) systems has persistency and is (in a certain sense) maximal among such ILO. Our result generalizes the persistency results by Nemhauser and Trotter, Hochbaum et al., and Fiorini et al. Even more, we propose a stronger property called neighborhood persistency and show that the LO relaxation of ILO on UTVPI systems in general has this property. Using this stronger result, we obtain a fixed-parameter algorithm (where the parameter is the optimal value) and another proof of 2-approximability for ILO on UTVPI systems where objective functions and variables are non-negative.

An Improved Pied Kingfisher Optimizer for Maritime UAV Path Planning

Maritime activities have become increasingly frequent with the deepening of economic globalization, highlighting the burgeoning significance of maritime rescue. However, in practical applications, UAVs for maritime rescue face numerous challenges, such as limited endurance and inadequate autonomous planning capabilities. To optimize flight routes and circumvent adverse sea conditions, an improved Pied Kingfisher Optimizer (IPKO) that incorporates refraction reverse learning, variable spiral search, and Cauchy mutation strategies was proposed. Comparative experiments conducted on CEC2005 and CEC2022 datasets with seven traditional algorithms demonstrate that the proposed algorithm exhibits superior precision and convergence speed. Subsequently, a path planning objective function was constructed based on trajectory cost and threat cost to simulate a 3D space for UAV maritime rescue missions, and the IPKO algorithm was applied to address the UAV path planning problem. The results showed that the total cost incurred by the IPKO algorithm decreased by 5.77% compared to the PKO algorithm and by 51.19% compared to the SCA algorithm. Finally, through UAV flight tests validating its practical applicability, it is ascertained that IPKO can enhance rescue efficiency in complex maritime rescue environments.

Optimizing microgrid integration of renewable energy for sustainable solutions in off/on-grid communities

Rising energy costs, climate change impacts, and transmission losses have increased demand for renewable energy sources and decentralized solutions. As more people seek smart living and working environments, integrated smart microgrids powered by hybrid renewable systems have become attractive solutions for off-grid and on-grid communities. This study proposes designing a solar-wind-battery hybrid microgrid supplying a medical load et al.-Ain Al-Sokhna, Egypt. The optimization objectives aim to minimize the loss of power supply probability (LPSP %) and the levelized cost of energy (LCOE, $/kWh). A key consideration when designing and optimizing hybrid microgrids is the energy management strategy, which coordinates different generation sources and fluctuating load demand. Therefore, optimization algorithms were applied to balance energy flows while meeting loads, mitigating weather impacts, and preventing overcharging/deep discharge of battery storage. Models of wind turbines, photovoltaic panels, and battery storage were developed to simulate and analyze proposed microgrid operations. A multi-objective optimization approach evaluated LPSP and LCOE metrics using transit search, grey wolf, and particle swarm algorithms to find optimal system configurations. The optimization algorithms demonstrated varying performances in minimizing the multi-objective functions for the on-grid and off-grid microgrids. The particle-swarm optimization technique is the best solution for the off-grid system, which contains PV, wind, and battery storage, with a minimum LCOE of 0.3435 $/kWh and an LPSP of 4.5334%. Meanwhile, the transit-search optimization algorithm found the optimal solution for the on-grid configuration according to the objective function, yielding an LCOE of 0.116 $/kWh and an LPSP value of 3.0639 × 10−16. Statistical analysis confirmed that the algorithms generally exhibited stable and robust optimization capabilities. Of the methods, transit search was the most effective overall optimization approach.

The Extraordinary Effect of Religious Sect Endorsements on Vote Choice: Evidence From Iglesia ni Cristo’s “Vote as One” Teaching

Sects and strict denominations are growing more rapidly and retaining members better than mainstream religions. To what extent do they influence vote choice? I investigate whether the Iglesia ni Cristo (INC), a religious sect in the Philippines, can compel all members to “vote as one” and support a full ticket of endorsed candidates. Using a triple difference identification strategy, I show that the municipal-level vote-shares of INC-endorsed national candidates are unit elastic with respect to the share of INC voters across municipalities. Conventional low-information rationality models of voting cannot fully explain this. But augmented with social voting in which actions of other group members inform the objective function of voters, a voting model with religious sect endorsements can produce extremely large behavioral responses. These findings have implications on policy as well as on our understanding of strict religions’ influence on politics.

Means of Hitting Times for Random Walks on Graphs: Connections, Computation, and Optimization

For random walks on graph \(\mathcal{G}\) with \(n\) vertices and \(m\) edges, the mean hitting time \(H_{j}\) from a vertex chosen from the stationary distribution to vertex \(j\) measures the importance for \(j\) , while the Kemeny constant \(\mathcal{K}\) is the mean hitting time from one vertex to another selected randomly according to the stationary distribution. In this paper, we first establish a connection between the two quantities, representing \(\mathcal{K}\) in terms of \(H_{j}\) for all vertices. We then develop an efficient algorithm estimating \(H_{j}\) for all vertices and \(\mathcal{K}\) in nearly linear time of \(m\) . Moreover, we extend the centrality \(H_{j}\) of a single vertex to \(H(S)\) of a vertex set \(S\) , and establish a link between \(H(S)\) and some other quantities. We further study the NP-hard problem of selecting a group \(S\) of \(k\ll n\) vertices with minimum \(H(S)\) , whose objective function is monotonic and supermodular. We finally propose two greedy algorithms approximately solving the problem. The former has an approximation factor \((1-\frac{k}{k-1}\frac{1}{e})\) and \(O(kn^{3})\) running time, while the latter returns a \((1-\frac{k}{k-1}\frac{1}{e}-\epsilon)\) -approximation solution in nearly-linear time of \(m\) , for any parameter \(0 \lt \epsilon \lt 1\) . Extensive experiment results validate the performance of our algorithms.

Relieving Overexposure in Information Diffusion Through a Budget Multi-stage Allocation

When information dissemination campaigns on Online Social Networking platforms are too aggressive, this can easily cause information overexposure. Overexposure can break through the psychological and physiological limits that the audience can tolerate, making it difficult for the audience to obtain reasonable cognitive concepts. For example, the overexposure of information referring to an object in a promotional campaign can lead to inflated expectations in individuals. Building on this, we introduce two indicators for individuals’ expectations and actual utility of the object and design a multi-stage triggered mechanism for seed individuals to explore the relieving overexposure problem in information diffusion. We build a multi-stage information diffusion model and characterize the evolution of individual expectations. We verify the hardness result of the relieving overexposure problem by budget multi-stage allocation, and the non-submodularity and non-monotonicity of the objective function. Addressing the non-monotonic and non-submodular set function, we provide a direct influence-oriented algorithm with a greedy approach. Extensive experiments are performed on four real networks to explore how model parameters and network properties affect the effects of multi-stage triggered strategies for seed individuals. Using the experiments, we found that the seed individual multi-stage incremental triggered strategy of dissemination campaign of information referring to an object shows better performance, and the lower the actual utility of the specific object, the more accurate the promotion strategy needs to be developed.

Inversion of Fracture Weakness Parameters Based on the 3CVSP Data—Part II: ORT Media Composed of A Single Fracture Set in the VTI Background Media

The inversion of fracture parameters from seismic data is a key issue in predicting fractured reservoirs. When a set of vertical fractures developed in a background medium of laminated shales or thin interbedded sandstone and mudstone with VTI (transversely isotropic media with a vertical symmetry axis) characteristics, this medium is a typical ORT (orthotropic) medium. In such a medium, due to the presence of fractures, the PS wave in the original VTI medium splits into two types of waves: PS1 and PS2 waves (fast and slow shear waves). To use the travel times of these two waveforms to invert the fracture weakness parameters, we derive approximate formulas for the travel times of PS1 and PS2 waves recorded in a Vertical Seismic Profile (VSP) for horizontally layered media. These approximate formulas are obtained using rational form approximation methods for short offsets with respect to the shot position. This approximation transforms the calculation of travel time from the complex phase velocity domain (the function of the two horizontal slownesses) to the group velocity domain (the function of shot point coordinates). The results from numerical errors analysis indicate that even with a near offset assumption, the approximated formula has high accuracy for offsets close to the depth of the target layer. By utilizing the travel times of PS1 and PS2 waves picked up by velocity under isotropic conditions, combined with undetermined anisotropic parameters and rational form approximations, we establish an objective function for inversion. The inversion results from different azimuths indicate that the accuracy of the inverted anisotropic parameters has different azimuthal sensitivities. The inversion method is applied to field multi-directional 3CVSP data and the results confirm the effectiveness of the method.

Development of an Automotive-Relevant Recycling Process for Paper Fiber-Reinforced Polypropylene Composites

The automotive industry is under growing pressure from regulatory agencies to improve the recyclability of its plastic components. Simultaneously, manufacturers are adopting natural fiber composites in vehicles to reduce their carbon footprint and decrease reliance on petroleum-based materials. This presents a challenge at vehicle end-of-life, however, as natural fiber-reinforced polymers are substantially more difficult to recycle than their unreinforced counterparts. This study investigated the development of a mechanical recycling process for paper fiber-reinforced polypropylene composites, focusing on the impact of injection molding parameters—specifically, injection temperature and rate—on the thermal, mechanical, and water uptake properties of the composites. The results showed that processing temperature had a greater influence on composite performance than injection rate, with some limited interaction effects between the two. Higher processing intensity damaged the paper fibers, increasing the number of nucleation sites and resulting in greater polypropylene crystallinity. These structural changes reduced tensile properties at higher intensities, while flexural properties improved. Objective function analysis was applied to identify optimal processing conditions, balancing these competing trends. Overall, the findings demonstrate that paper fiber-reinforced polypropylene composites can be recycled into automotive-relevant injection molding compounds using conventional plastic manufacturing techniques, though careful tuning of processing parameters is essential to achieve optimal performance.

Comparing AI versus Optimization Workflows for Simulation-Based Inference of Spatial-Stochastic Systems

Abstract Model parameter inference is a universal problem across science. This challenge is particularly pronounced in developmental biology, where faithful mechanistic descriptions require spatial-stochastic models with numerous parameters, yet quantitative empirical data often lack sufficient granularity due to experimental limitations. Parameterizing such complex models therefore necessitates methods that elaborate on classical Bayesian inference by incorporating notions of optimality and goal-orientation through low-dimensional objective functions that quantitatively encapsulate target system behavior. In this study, we contrast two such inference workflows and apply them to biophysically inspired spatial-stochastic models. Technically, both workflows employ simulation-based inference (SBI) methods: the first leverages a modern deep-learning technique known as sequential neural posterior estimation (SNPE), while the second relies on a classical optimization technique called simulated annealing (SA). We evaluate these workflows by inferring the parameters of two complementary models for the inner cell mass (ICM) lineage differentiation in the blastocyst-stage mouse embryo. This developmental biology system serves as a paradigmatic example of a highly robust and reproducible cell-fate proportioning process that self-organizes under strongly stochastic conditions, such as intrinsic biochemical noise and cell-cell signaling delays. Our results reveal that while both methods provide consistent model parameter estimates, the modern SBI workflow yields significantly richer inferred distributions at an equivalent computational cost. We identify the computational scenarios that favor the modern SBI method over its classical counterpart, and propose a plausible strategy to exploit the complementary strengths of both workflows for enhanced parameter space exploration.

An Improved Spider Wasp Optimizer for UAV Three-Dimensional Path Planning

This paper proposes an Improved Spider Wasp Optimizer (ISWO) to address inaccuracies in calculating the population (N) during iterations of the SWO algorithm. By innovating the population iteration formula and integrating the advantages of Differential Evolution and the Crayfish Optimization Algorithm, along with introducing an opposition-based learning strategy, ISWO accelerates convergence. The adaptive parameters trade-off probability (TR) and crossover probability (Cr) are dynamically updated to balance the exploration and exploitation phases. In each generation, ISWO optimizes individual positions using Lévy flights, DE’s mutation, and crossover operations, and COA’s adaptive update mechanisms. The OBL strategy is applied every 10 generations to enhance population diversity. As the iterations progress, the population size gradually decreases, ultimately yielding the optimal solution and recording the convergence process. The algorithm’s performance is tested using the 2017 test set, modeling a mountainous environment with a Gaussian function model. Under constraint conditions, the objective function is updated to establish a mathematical model for UAV flight. The minimal cost for obstacle-avoiding flight within the specified airspace is obtained using the fitness function, and the flight path is smoothed through cubic spline interpolation. Overall, ISWO generates high-quality, smooth paths with fewer iterations, overcoming premature convergence and the insufficient local search capabilities of traditional genetic algorithms, adapting to complex terrains, and providing an efficient and reliable solution.

Probabilities estimating for attacks on supply chain for critical infrastructure facilities

The different types of supply chain for critical infrastructure facilities of industrial sector were analyzed. Also, the main types of attacks in supply chain were considered. The character of resource dependencies was analysed and representation of supply chain in form of hierarchical oriented graph, with division into levels, was considered. The algorithm of taking into account of attack probabilities for objects, which giveresources for functioning of some endpoint object of supply chain was developed basing on dynamic programming principles. Calculation complexity of proposed algorithm was estimated, and it confirmed its effectiveness for practical situations. For thetarget area of use the proposed approach gives better calculation complexity in compare existing solutions.

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.

An Advanced Whale Optimization Algorithm for Grayscale Image Enhancement

Image enhancement is an important step in image processing to improve contrast and information quality. Intelligent enhancement algorithms are gaining popularity due to the limitations of traditional methods. This paper utilizes a transformation function to enhance the global and local information of grayscale images, but the parameters of this function can produce significant changes in the processed images. To address this, the whale optimization algorithm (WOA) is employed for parameter optimization. New equations are incorporated into WOA to improve its global optimization capability, and exemplars and advanced spiral updates improve the convergence of the algorithm. Its performance is validated on four different types of images. The algorithm not only outperforms comparison algorithms in the objective function but also excels in other image enhancement metrics, including peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), and patch-based contrast quality index (PCQI). It is superior to the comparison algorithms in 11, 6, 11, 13, and 7 images in these metrics, respectively. The results demonstrate that the algorithm is suitable for image enhancement both subjectively and statistically.

Community detection in stochastic block models via penalized variational estimation

Stochastic Block Models (SBMs) have emerged as a powerful framework for modelling community structures in networks. However, accurately determining the number of communities in SBMs remains challenging. In this paper, we propose a novel approach for community detection in stochastic block models through penalized variational estimation. Our method introduces two penalties on the variational objective function to ensure elimination of redundant blocks. We present the variational computing procedure, and demonstrate through simulation and empirical analysis that our approach can effectively identifies community structures and achieves accurate parameter estimation simultaneously.

Algorithms for designing a trajectory of a communication aerial platform for collecting data from wireless sensor network nodes

Algorithms are proposed for solving the problem designing the flight path of a communication aerial platform for collecting monitoring data from the nodes of a large-scale disconnected wireless sensor network to achieve various objective functions: minimizing the collection time or maximizing the network operation time. To achieve the given objective functions, the problem is solved by applying a certain sequence of algorithms: clustering, finding the shortest path and its correction according to the defined heuristics. Obtaining admissible solutions in real time is based on the application of a set of developed algorithms (heuristics), which take into account the relative location of nodes, the energy level of their batteries, and the volume of monitoring data. In order to evaluate the efficiency of the application of flight path construction and data collection algorithms, a corresponding simulation model was developed. The obtained dependences of efficiency indicators (data collection time, battery energy consumption, network operation time) on a set of algorithms for building the spacecraft data collection trajectory with different input data. The results of simulations proved the possibility of reducing data collection time by up to 20 % or increasing network operation time by up to 15 % compared to existing solutions.

Modern Methods and Algorithms of Path Planning for Autonomous Mobile Robots, Their Objective Functions

The current situation in the world of research algorithms for path planning by autonomous robots shows that there are a large number of algorithms and their modifications that require researchers and engineers of autonomous robotics to have a fine understanding of the fundamental aspects of their functioning. The general goal of the research is to conduct an analysis of existing algorithms, methods and their modifications from the point of view of the component of the mathematical apparatus, namely their target functions. The research examines 38 modern algorithms and methods of path planning by autonomous robots. Considered the importance and criticality of understanding the objective function for the field of robotics. Examples illustrating the importance of this aspect are given. In the process of research, algorithms are presented in the form of formalized generalized mathematical formulas taking into account possible minimization/maximization, their improvements and improvements. For each considered algorithm and method, a conclusion is given regarding its target function. In the study, the methods of "Dynamic games" are separately highlighted and the application of the fundamental "Method of solving functions of A.O. Chikryi" for path planning is considered. In general, information on the advantages and importance of understanding the mathematical apparatus of pathfinding algorithms by autonomous robots for solving scientific problems is provided. The generalized results are summarized in a single comparative table, which provides the main formalized function of the algorithms, reveals the main idea, advantages/disadvantages, the scope of application and an example of use. The comparative table is presented in the form of a generalized auxiliary reference element of the study.

FUZZY PRODUCTION MODEL WITH FUZZY VALUED DEMAND WITH NONLINEAR SELLING PRICE, WARRANTY, AND LEVEL OF GREENNESS OF THE PRODUCT UNDER FUZZY DETERMINATION RATE

Many studies on manufacturing and inventory management for increasingly problematic commodities have focused on how quality, greenness, product warranties, and environmental concerns might be incorporated into industrial operations. However, comparatively few articles have integrated these two themes. To close this knowledge gap and advance practice, these two decaying object criteria must be included in the study. The following assumptions are used to create a warranty-based, sustainable production inventory model for green products. In this article, we look at how greenness, product assurances, and reducing carbon emissions technologies impact overall profit to assist those making decisions to make better choices regarding pricing and replenishment. Due to the consideration of fuzzy valued inventory parameters along with the objective function's nonlinearity and the presence of five decision variables, we have addressed this problem with the help of the Lingo 18 software along with the defuzzification technique. The mathematical formula is quantitatively demonstrated in one experiment, and a sensitivity analysis is performed to determine the effects of various factors on overall profit and management insights.

Solving a Fully Intuitionistic Fuzzy Transportation Problem Using a Hybrid Multi-Objective Optimization Approach

In this study, a typical transportation problem involving intuitionistic fuzzy-type variables and parameters is focused on. The approaches proposed in the literature for such transportation problems have many shortcomings, such as the use of ranking functions and obtaining an infeasible solution with negative values for variables and objective functions in the presence of non-negative unit transportation charges. To overcome such weaknesses, a new approach without a ranking function is introduced in this paper. The proposed approach first constructs an equivalent crisp multi-objective form of the intuitionistic fuzzy transportation problem and then proposes a new hybrid multi-objective solution procedure to tackle the obtained crisp multi-objective problem. The conducted computer experiments with benchmark problems from the existing studies of the literature reflect the effectiveness of the proposed solution approach of this study in terms of the quality of the results when compared to the available approaches of the literature.