Structural Compliance Research Articles

Interface‐Filtering Structural Optimization

ABSTRACTIn this paper, we present an interface‐filtering structural optimization method that optimizes structural shape and topology through successive interface movements. This interface filtering is achieved via the combination of the variable‐radius‐based partial‐differential‐equation (PDE) filtering and the Heaviside projection on a density representation. In the proposed method, designs are represented by a density field with sharp interface and no internal grey features, and a filter radius field is used as the design variable in the optimization process. With this method, any density distribution with sharp interfaces can be used as initial designs, and sharp density contrast in density distribution is preserved throughout the optimization process. An analytical relation between the maximum movements of interfaces and the maximum filter radius is given, so that the interface movement can be controlled during the optimization process. Sensitivities with respect to filter radius variables are derived. Two numerical treatments, involving the density update scheme and the radius re‐initialization scheme, are developed to achieve smooth successive shape updates and avoid artificial local minima. Numerical examples, including geometric deformation problem, structural compliance minimization, thermal compliance minimization, and negative Poisson ratio problem, are presented to demonstrate the capabilities of the proposed method.

Structural topology optimization in linear and nonlinear elasticity using a discontinuous Galerkin finite element method

Abstract A discontinuous Galerkin finite element method is developed for structural topology optimization with the level set method. The discontinuous Galerkin finite element method is employed to discretize and solve both the elasticity system, possible adjoint, and the transport equation of the level set function. Structural compliance and compliant mechanisms are considered for linear and nonlinear elastic structures. Numerical examples are provided to verify the effectiveness of the algorithm presented.

ЭКСПЕРТНЫЙ ВЗГЛЯД НА ОСОБЕННОСТИ СИСТЕМЫ ТЕХНИЧЕСКОГО РЕГУЛИРОВАНИЯ ПОЖАРНОЙ БЕЗОПАСНОСТИ В СТРОИТЕЛЬСТВЕ

Проведена экспертная оценка особенностей применения способов обоснования соответствия зданий и сооружений требованиям пожарной безопасности с учетом правовых положений и практического опыта применения. Даны предложения по корректировке нормативно-правовых актов. An expert assessment of the application peculiarities of methods to substantiate the compliance of buildings and structures with fire safety requirements was carried out taking into account legal provisions and practical application experience. Proposals for the adjustment of regulatory legal acts are given. It is noted that in Article 6 of Federal Law 123-FZ, the methods of justification are present in a list, which creates the illusion, for example, that the choice of method is not limited by anything. In fact, each method of justification, as shown in this article, has its own scope of application. The changes affected both the list of compliance conditions and the regulatory legal acts issued in the development of the considered justification methods. This requires not only a deeper study of the system of technical regulation of fire safety in construction as a whole, but also to examine the specifics of the application of each of the methods of justification for fire safety of objects of protection. A situation where the requirements of the technical regulations will be met and only the standards of organizations, or only the calculation of risk, or only special technical conditions will be used, is impossible in practice. In any case, the conditions of the codes of rules will be fulfilled, just in some part there will be certain deviations from them through special technical conditions, standards of organizations or fire risk calculation. At the same time, there is no concept of "deviation from the rulebook". Keywords: technical regulations, fire safety in construction, code of rules, fire risk, special technical conditions, calculation justifications, organization standard

Prospects of Using AI Technologies in Open Journal Systems (OJS)

Open Journal Systems (OJS) is a widely used open-source platform for managing and publishing academic journals (Ndungu, 2020), (Hunter, 2010), (Tabatadze B. , 2024). Its modular design and open framework provide opportunities for integrating artificial intelligence (AI) technologies, which hold transformative potential for academic publishing. AI can enhance OJS by automating editorial workflows, improving user experience, and fostering global accessibility. Key areas for AI integration include automated peer review, where natural language processing (NLP) can identify relevant reviewers, detect plagiarism, and ensure structural compliance. AI-powered recommendation systems can personalize content delivery, offering tailored article suggestions based on user preferences and behaviors. Additionally, linguistic diversity can be bolstered through real-time translation tools and speech-to-text features, facilitating broader engagement with a global audience. AI also enables automation in editorial processes such as grammar checks, citation management, and abstract generation. Advanced analytics powered by AI can provide actionable insights into readership trends, engagement metrics, and impact factor predictions, supporting strategic decision-making for journal administrators. Despite its potential, AI integration poses challenges, including technical expertise requirements, financial constraints, and ethical concerns such as data privacy and bias. Overcoming these barriers will require careful planning and community collaboration. The future of AI in OJS looks promising, with opportunities to incorporate blockchain, adaptive learning systems, and AI-driven collaboration tools. By addressing these challenges, OJS can lead the evolution of open-access publishing, enhancing efficiency, accessibility, and inclusivity in scholarly communication.

Multi-objective optimization of truss structures using the enhanced Lichtenberg algorithm

Abstract The primary objective of numerous optimization problems is to enhance a single metric whose lowest or highest value accurately reflects the response quality of a system. However, in some instances, relying solely on one metric is not practical, leading to the consideration of multi-objective (MO) optimization problems that aim to improve multiple performance indicators simultaneously. This approach requires the use of a multi-objective optimization method adept at handling the intricacies of scenarios with various indices. Consequently, researchers have not explored multi-objective truss optimization as extensively as single-objective (SO) scenarios. The novel multi-objective Lichtenberg algorithm with two archives (MOLA-2arc) has been developed to address this. The efficacy of MOLA-2arc is evaluated against eight other MO algorithms, including the multi-objective bat algorithm (MOBA), multi-objective crystal structure algorithm (MOCRY), multi-objective cuckoo search (MOCS), multi-objective firefly algorithm (MOFA), multi-objective flower pollination algorithm (MOFPA), multi-objective harmony search (MOHS), multi-objective jellyfish search (MOJS) algorithm, and the original multi-objective Lichtenberg algorithm (MOLA). The challenge is to minimize structural mass and compliance while adhering to stress limitations. The outcomes demonstrate that MOLA-2arc shows notable improvements over its predecessor, MOLA, and surpasses all other competing algorithms in this study.

Application of Q4 Strain Gradient Notation Finite Element Method (SGN-FEM) in Level Set Optimization Using Radial Basis Functions

The Strain Gradient Notation Finite Element Method (SGN-FEM) employs physically interpretable polynomials in the development of finite elements, allowing for the precise identification and subsequent elimination of parasitic shear sources, which cause shear locking. The element is corrected a priori, during development, by simply removing the spurious terms from the shear strain polynomials.The Level Set Method (LVM) is an optimization technique widely employed in topology optimization to minimize the compliance of structures. This method consistently provides clear boundary and geometry information during the optimization process, giving it inherent advantages in solving boundary and geometry-related problems.While numerous studies investigate different approaches to calculate level set optimization, there are fewer studies evaluating how the finite element technique contributes to the analysis. Considering this, this paper presents the application of Q4 SGN-FEM in optimization studies using LVM to minimize the compliance of a two-dimensional linear elastic structure. The differences obtained when using the SGN-FEM model are investigated. The parametrized level set method, as defined in Wei et al. (2018), is utilized alongside SGN-FEM to conduct the study. The model is validated through comparison with other studies, demonstrating that SGN-FEM proves to be a viable alternative for conducting optimization studies using the level set method.

Semantic segmentation of optical satellite images for the illegal construction detection using transfer learning

As urban populations grow, the demand for new infrastructure intensifies. However, some builders often neglect to obtain necessary permits, resulting in unstable structures that endanger public safety and disrupt city planning. Illegal construction poses significant challenges to economic development and social harmony. To tackle this issue, there is an urgent need for an automated illegal building monitoring system that can accurately identify and notify authorities about unlawful constructions. Current methods are often inefficient due to limited data access, inadequate analysis, and lack of automation.Recent technological advancements suggest that using satellite imagery alongside semantic segmentation is a highly effective approach for detecting illegal buildings. This process not only ensures the safety and compliance of structures but also enhances overall regulatory enforcement. This research distinguishes itself from previous studies by primarily focusing on illegal construction. It employs a diverse range of machine learning models, including the U-Shaped Network and Visual Geometry Group, and incorporates customized evaluation metrics that are not typically found in earlier research. Additionally, it utilizes real-world data, enhancing its practical relevance, and features IoT integration for real-time monitoring capabilities.We chose U-Net as our architecture due to its symmetrical design, which facilitates effective feature extraction. Its skip connections play a crucial role in preserving important features during the segmentation process. U-Net is known for its high accuracy and adaptability across various domains, making it versatile for our needs. Additionally, it performs well even with limited data, demonstrating robustness in diverse conditions. Furthermore, its compatibility with transfer learning enhances its applicability. For all these reasons, U-Net was selected as our model.Our experiments revealed that the Unet_mini model outperformed all others, demonstrating its effectiveness in addressing illegal construction challenges in urban areas. Qualitative results further illustrate the model's robustness, showing a significant reduction in false positives and improved accuracy in identifying illegal structures. This comprehensive approach not only improves detection rates but also contributes to more efficient regulatory enforcement, thereby promoting safer urban environments.This research distinguishes itself from previous studies by primarily focusing on illegal construction detection rather than general segmentation tasks. It employs a diverse range of machine learning models, including the U-Shaped Network and Visual Geometry Group, and integrates customized evaluation metrics that are not typically found in earlier research. Furthermore, it utilizes real-world data, enhancing its practical relevance, and incorporates IoT integration for real-time monitoring capabilities, setting it apart from existing studies.

Concurrent multi-scale design optimization of fiber-reinforced composite material based on an adaptive normal distribution fiber optimization scheme for minimum structural compliance and additive manufacturing

Structural lightweight is a core technical requirement for the structural design of aerospace and new energy power equipment structures. For multi-scale variable stiffness design optimization of discrete fiber-reinforced composite laminates, one of the challenges is how to avoid the explosion of design variable combinations caused by the increase in the number of candidate discrete fiber laying angles. The Normal Distribution Fiber Optimization (NDFO) interpolation scheme has the numerical advantage that the number of design variables does not increase with an increase in the number of candidate discrete fiber laying angles. However, the traditional NDFO interpolation scheme uses uniform penalty parameters across all elements, which means that normalizing the penalty parameters for all the elements ignores the convergence differences of discrete fiber laying angles in different elements within the macro-scale structure topology. This leads to time-consuming and unstable optimization iteration of the macro-scale structural topology and micro-scale discrete fiber laying angle selection. Especially, it easily causes the micro-scale discrete fiber laying angle selection to fall into the local optimum prematurely. Therefore, considering the difficulties and challenges of the traditional NDFO interpolation scheme in the multi-scale variable stiffness design optimization of fiber-reinforced composites. This paper proposes an Adaptive Normal Distribution Fiber Optimization (ANDFO) interpolation scheme, and the feedback mechanism of the convergence rate of the element design variable and the objective function is introduced to achieve the adaptive reduction of the penalty parameters. Based on the proposed ANDFO interpolation scheme, a multi-scale design optimization model of fiber-reinforced composite laminates is established, considering the macro-scale structure topology and micro-scale discrete fiber laying angel selection. The explicit sensitivity of the objective function of minimizing structural compliance to the macro-scale topological design variables and the micro-scale fiber laying angle design variables is derived. Considering the manufacturability of additive manufacturing based on the optimized design results, a multi-scale nonlinear continuous filtering strategy for discrete fiber laying angle is adopted to improve the continuity of the local fiber laying path. Numerical examples systematically present the coupling effects of macro-scale structural topology and micro-scale fiber laying path, multi-scale nonlinear discrete fiber continuous filtering laying path structure, and continuous fiber additive manufacturing multi-scale optimized structure. The proposed ANDFO scheme provides a new theoretical and methodological approach for the lightweight and integrated multi-scale design and manufacturing of fiber-reinforced composite laminates through additive manufacturing technology.

Topology Optimization of the Bracket Structure in the Acquisition, Pointing, and Tracking System Considering Displacement and Key Point Stress Constraints

The lightweight and displacement-stable design of the mechanical support structure within the APTS (Acquisition, Pointing, and Tracking System) is crucial for enhancing the payload capacity of remote sensing, satellite communication, and laser systems, while still meeting specified functional requirements. This paper adopts the Solid Isotropic Material with Penalization (SIMP) method to investigate the structural topology optimization of the L-shaped bracket in the APTS, aiming to minimize structural compliance while using volume, key point displacement, and maximum stress as constraints. In the optimization model, differences in the topology of the L-shaped bracket structure are explored to minimize structural compliance, which was performed under volume, key point displacement, and stress constraints, and the results are compared with the initial reinforced structure. The innovative L-shaped bracket structure obtained through topology optimization uses significantly less material than the initial reinforced design, while still meeting the displacement and stress constraints. After smoothing, rounding, and finite element analysis, the displacement and stress performance of the optimized L-shaped bracket structure satisfies the set constraints. The method proposed in this paper offers an innovative solution for the lightweight design of mechanical support structures in APTS, with significant engineering application potential.

Tax System Optimization: A Comparative Analysis Between Personal Income Tax and Corporate Income Tax in Indonesia

This study adopts a qualitative approach to analyze the comparison between Personal Income Tax (PPh) and Corporate Income Tax (PPh Corporate) in Indonesia. Through literature studies and observations, this study explores the tariff structure, tax relief, and tax compliance level of the two tax systems. The economic implications of the implementation of Income Tax and Corporate Income Tax on economic growth, investment, and income distribution were also discussed. The findings show that tax reform, as stated in the Law on Harmonization of Tax Regulations (UU HPP), has a significant impact in improving the effectiveness and fairness of the tax system. The results of this study provide in-depth insights into the importance of appropriate tax policies to support inclusive and sustainable economic growth in Indonesia.

Uniform multiple laminates interpolation model and design method for double–double laminates based on multi-material topology optimization

Double–Double (DD) laminates, incorporating a repetition of sub-plies featuring two groups of balanced angles, offer broad design flexibility together with the ease of design and manufacturing. In this work, a novel optimization design method is proposed for DD composite laminates based on multi-material topology optimization. First, the uniform multiple laminates interpolation (UMLI) model is proposed to describe the certainty of the stacking direction in multi-layer composite structures, inspired by the interpolation model in multi-material topology optimization. Specifically, the stiffness matrices of all alternative angle combinations of laminates are interpolated to form virtual laminates. The UMLI model eliminates the need for adding interlayer constraints during the optimization process. Then, the optimization problem is defined to minimize the compliance of the composite structures and is solved using the gradient-based optimization algorithm. Finally, the proposed method is applied to the design of the composite stiffened panel, the composite Unmanned Aerial Vehicle (UAV) wing, and the rear fuselage. The results demonstrate that the UMLI model and proposed optimization method have considerable potential in the angle optimization design of multi-layer structures.

A Study on the Effect of Structural Compliance Placing in Soft Contact/Collision Properties of Multirotor Micro Aerial Vehicles

Adding compliance (softness) has been introduced as an effective way to improve the physical collision resilience characteristics of multirotor micro aerial vehicles (MAVs). This article answers the question “Where is the best place to apply compliance in a multirotor MAV to make it more collision‐resilient?” by analyzing the output data of more than 1200 drone collision tests through two sets of accelerated and nonaccelerated collision experiments for four main configurations of micro‐quadcopters each possessing a unique softness layout of physical frame. It is shown that while applying compliance to the protective propeller guards (bumpers) of a micro‐quadcopter provides a more elastic collision, it does not improve its energy‐dissipation (impact damping) characteristics. On the other hand, enhancing the inner frame of the micro‐quadcopter with a softer structure results in higher rates of impact energy damping during the collisions and an increase in the impact time, which results in lower impact accelerations the MAV experiences during the crush. A mathematical model is developed representing the contact/collision interactions as nonlinear viscoelastic forces. Comparing the results of the simulations to the experiments suggests that this model can effectively mimic the impact behavior of contacting/colliding MAVs with different structural stiffness and damping.

Compliance with COVID-19 government guidance and rules by disabled people and people from minoritised ethnic groups: Qualitative findings from the CICADA study.

Within the 2020/21 CICADA (Coronavirus Intersectionalities: Chronic Conditions or Disabilities and Migrants and other Ethnic minorities) study, we explored full, partial or noncompliance with government COVID-19 infection-containment measures by people from minoritised ethnic groups with a disabling health condition or impairment. We used an assets-based intersectional approach and purposive sampling, included non-disabled and White British comparators, and trained community co-researchers to help us reach undocumented migrants and asylum seekers. We undertook 271 semi-structured qualitative interviews, followed by participatory workshops with interviewees to explore data and changes in experiences five and 10 months after the interviews. Perceiving their vulnerability to COVID-19, most participants quickly and often zealously adopted infection-containment behaviours, and continued this after restrictions were lifted. This could reduce mental wellbeing, especially in community-facing cultures, and could create family conflict. Various structural inequities impeded compliance. Many, especially undocumented migrants, felt imprisoned. The intersection of gender, citizenship, socioeconomic status and culture impacted disclosures of COVID-19 infection, support seeking and use. Many were unclear what was safe as well as unsafe. People complained that disability and cultural considerations were omitted from policymaking. Participants mostly had taken the COVID-19 vaccine by October 2022, but ethnic minority participants needed time to deliberate and trusted, community-embedded information whereas White British participants were mostly influenced by mass media. The intersection of health condition or impairment, poverty, and living alone led to more non-compliance with general rules, and more vaccine hesitancy than did misinformation spread through ethnic community channels. Many participants were reluctant to reintegrate in May 2022 because of continued perceived vulnerability to COVID-19 but by September 2022 = seemed more concerned about the economic crisis. We add two new 'types' to existing compliance typologies: deliberators (who eventually decide to follow the rules), and 'necessity-driven non-compliers' who are totally unable to comply because of their disabilities.

Large-scale 3D multiphysics topology optimization of flow–heat–structural models including an islands constraint

This article demonstrates a large-scale 3D topology optimization problem formulation for components in need of internal cooling owing to surrounding hot gas flow. A conjugate heat transfer model is used and the goal of the optimization problem is to maximize the thermal performance subject to a coolant consumption limit. As a model problem, the interior design of a gas turbine guide-vane-like geometry is considered. High-quality finite element meshes are generated automatically by means of a voxelization method. A structural compliance constraint for thermo-mechanical loads, and a constraint for the suppression of free-floating structural parts, are included in the problem formulation. For the latter, an analytical expression for the constraint limit is derived. Several numerical examples indicate that the proposed problem formulation is able to generate interesting conceptual designs for interior-vane-cooling solutions.

Assessing the utility of large language models for phenotype-driven gene prioritization in the diagnosis of rare genetic disease

Phenotype-driven gene prioritization is fundamental to diagnosing rare genetic disorders. While traditional approaches rely on curated knowledge graphs with phenotype-gene relations, recent advancements in large language models (LLMs) promise a streamlined text-to-gene solution. In this study, we evaluated five LLMs, including two generative pre-trained transformers (GPT) series and three Llama2 series, assessing their performance across task completeness, gene prediction accuracy, and adherence to required output structures. We conducted experiments, exploring various combinations of models, prompts, phenotypic input types, and task difficulty levels. Our findings revealed that the best-performed LLM, GPT-4, achieved an average accuracy of 17.0% in identifying diagnosed genes within the top 50 predictions, which still falls behind traditional tools. However, accuracy increased with the model size. Consistent results were observed over time, as shown in the dataset curated after 2023. Advanced techniques such as retrieval-augmented generation (RAG) and few-shot learning did not improve the accuracy. Sophisticated prompts were more likely to enhance task completeness, especially in smaller models. Conversely, complicated prompts tended to decrease output structure compliance rate. LLMs also achieved better-than-random prediction accuracy with free-text input, though performance was slightly lower than with standardized concept input. Bias analysis showed that highly cited genes, such as BRCA1, TP53, and PTEN, are more likely to be predicted. Our study provides valuable insights into integrating LLMs with genomic analysis, contributing to the ongoing discussion on their utilization in clinical workflows.

Corporate Governance and Shareholder Value: A Meta-Analysis

Corporate governance is a critical determinant of shareholder value, influencing the effectiveness of management, transparency, and overall corporate performance. This article presents a meta-analysis of existing research on the relationship between corporate governance practices and shareholder value, synthesizing findings from a wide range of studies across different industries and geographical regions. The meta-analysis aims to identify the key governance mechanisms that consistently contribute to enhanced shareholder value, such as board structure, executive compensation, ownership concentration, and regulatory compliance. By analyzing these factors, the study provides a comprehensive overview of how corporate governance frameworks can drive firm performance and protect shareholder interests. The findings highlight that robust corporate governance practices are strongly associated with higher shareholder returns, reduced agency costs, and improved market confidence. Additionally, the study examines the contextual factors, such as legal environments and market maturity, that may influence the effectiveness of governance mechanisms. The article concludes by discussing the implications for policymakers, investors, and corporate leaders, emphasizing the importance of tailoring governance practices to the specific needs and challenges of individual firms. The meta-analysis underscores the value of strong governance structures in achieving sustainable long-term growth and maximizing shareholder value.

A hybrid FEM-IBM-level set algorithm for water entry of deformable body

Water entry of deformable bodies has been investigated extensively despite the major challenges in precise simulations of the whole physical process. Inspired by the basic idea of immersed boundary method (IBM), a hybrid FEM-IBM-level set algorithm is proposed where structural compliance is considered. The flow is described by Navier-Stokes equations with an added body force while the drastically moving object of arbitrary geometry is represented by several Lagrangian points on solid boundary. The direct forcing IBM is enhanced and coupled with finite element method (FEM) to facilitate simulation of severe fluid-structure interactions. Consequently, the added body force is calculated implicitly based on the exact velocity boundary condition with divergence-free condition ensured simultaneously. The improved conservative level set technique is adopted for accurate depiction of instantaneously distorted water surface, and the strongly coupled system is resolved iteratively through a partitioned scheme. Examples including sedimentation of a disk, water entry of deformable wedge and cylindrical shell are performed for validation. Specifically, hydroelastic impact of a three-dimensional conical shell is investigated, and the influence factors of entry dynamics are discussed. The results demonstrate the reliability and great potential of present algorithm for practical applications in ocean engineering, naval architecture and military engineering, etc.

Matricellular protein CCN1 promotes collagen alignment and scar integrity after myocardial infarction

BackgroundMembers of the cellular communication network family (CCN) of matricellular proteins, like CCN1, have long been implicated in the regulation of cellular processes underlying wound healing, tissue fibrogenesis, and collagen dynamics. While many studies suggest antifibrotic actions for CCN1 in the adult heart through the promotion of myofibroblast senescence, they largely relied on exogenous supplementation strategies in in vivo models of cardiac injury where its expression is already induced—which may confound interpretation of its function in this process. The objective of this study was to interrogate the role of the endogenous protein on fibroblast function, collagen structural dynamics, and its associated impact on cardiac fibrosis after myocardial infarction (MI). Methods/resultsHere, we employed CCN1 loss-of-function methodologies, including both in vitro siRNA-mediated depletion and in vivo fibroblast-specific knockout mice to assess the role of the endogenous protein on cardiac fibroblast fibrotic signaling, and its involvement in acute scar formation after MI. In vitro depletion of CCN1 reduced cardiac fibroblast senescence and proliferation. Although depletion of CCN1 decreased the expression of collagen processing and stabilization enzymes (i.e., P4HA1, PLOD1, and PLOD2), it did not inhibit myofibroblast induction or type I collagen synthesis. Alone, fibroblast-specific removal of CCN1 did not negatively impact ventricular performance or myocardial collagen content but did contribute to disorganization of collagen fibrils and increased matrix compliance. Similarly, Ccn1 ablated animals subjected to MI showed no discernible alterations in cardiac structure or function one week after permanent coronary artery ligation, but exhibited marked increases in incidence of mortality and cardiac rupture. Consistent with our findings that CCN1 depletion does not assuage myofibroblast conversion or type I collagen synthesis in vitro, Ccn1 knockout animals revealed no measurable differences in collagen scar width or mass compared to controls; however, detailed structural analyses via SHG and TEM of scar regions revealed marked alterations in their scar collagen topography—exhibiting changes in numerous macro- and micro-level collagen architectural attributes. Specifically, Ccn1 knockout mice displayed heightened ECM structural complexity in post-MI scar regions, including diminished local alignment and heightened tortuosity of collagen fibers, as well as reduced organizational coherency, packing, and size of collagen fibrils. Associated with these changes in ECM topography with the loss of CCN1 were reductions in fibroblast-matrix interactions, as evidenced by reduced fibroblast nuclear and cellular deformation in vivo and reduced focal-adhesion formation in vitro; findings that ultimately suggest CCN1’s ability to influence fibroblast-led collagen alignment may in part be credited to its capacity to augment fibroblast-matrix interactions. ConclusionsThese findings underscore the pivotal role of endogenous CCN1 in the scar formation process occurring after MI, directing the appropriate arrangement of the extracellular matrix's collagenous components in the maturing scar—shaping the mechanical properties that support its structural stability. While this suggests an adaptive role for CCN1 in regulating collagen structural attributes crucial for supporting scar integrity post MI, the long-term protracted expression of CCN1 holds maladaptive implications, potentially diminishing collagen structural complexity and compliance in non-infarct regions.

Intelligent cooperative control for flexible landing on small celestial bodies

In view of the potential landing risks in small celestial body landing missions, the concept of flexible landing has been proposed in recent years. By employing a flexible lander with structural compliance to complex surface topography, the risks of rebound or overturning upon touchdown can be reduced. During flexible landing, the overall motion of the flexible lander is controlled by several nodes embedded in the flexible structure. Due to the flexible deformation, the motion among the nodes is intricately coupled, posing challenges to the control algorithm design. To address this problem, an intelligent cooperative control method for flexible landing is proposed in this paper. By decomposing the flexible internal force into a low-order principal term with specific physical interpretation and an unmodeled high-order residual term, the coupling relationship among the nodes is characterized. On this basis, the low-order dynamics system is reconstructed into a piecewise affine system, upon which a multi-node cooperative optimal controller is designed. A long-short-term memory recurrent neural network is further established to compensate for the unmodeled high-order term. Through the combination of the low-order principal control and the high-order intelligent compensation, a feasible approach for achieving flexible landing is obtained. Finally, the effectiveness of the proposed control method is verified via 433 Eros-based flexible landing simulations.

Разработка и апробация опросника диспозиционной терпимости личности

<p>Objective of the work was to construct and validate a dispositional tolerance questionnaire that aims to measure the subject's tolerance level in a complex way. Data collection was carried out in an online format. The methodology was approved with 160 students, the average age was 20 years old. In order to validate the presented questionnaire of dispositional tolerance we used methods of internal coherence and reliability of the scale, convergent validity, as well as confirmatory factor analysis to check the compliance of the theoretical factor structure of the questionnaire with the real one. Internal consistency of the methodology was assessed by calculating the value of Cronbach's α coefficients = 0.725. Convergent validity was assessed with the tolerance index questionnaire and the correlation level was 0.601. Factor analysis of the dispositional tolerance questionnaire confirmed the factor structure of the methodology. The meaning of dispositional tolerance was conceptualized and compared with existing concepts of psychological tolerance for further presentation of the psychometric instrument. This questionnaire stands as a method to diagnose tolerance as a subject's attitude. The obtained results confirmed sufficient reliability for using the presented psychometric tool in psychological practice.</p>