Complex Space Research Articles

On A-normaloid d-tuples of operators and related questions

Let ß A (ℍ) denote the algebra of bounded linear operators on a complex Hilbert space ℍ that admit A-adjoint operators, where A is a non-zero positive semi-definite operator on ℍ. A commuting operator tuple T = (T 1 ,…, T d ) ∈ ß A (ℍ) d is called jointly A-normaloid if rA (T) = ∥T∥ A , where rA (T) and ∥T∥ A represent the joint A-spectral radius and the joint operator A-seminorm of T, respectively. This paper aims to investigate this new class of operators and provides several examples. Furthermore, a characterization of A-normaloidity is established. Additionally, the joint Euclidean A-seminorm of a d-tuple of A-bounded operators T, denoted by , is examined. Specifically, for all positive integers n, we prove that the following equivalence holds for any commuting operator tuple . Here . Finally, several related questions are explored.

Learning search algorithm: framework and comprehensive performance for solving optimization problems

In this study, the Learning Search Algorithm (LSA) is introduced as an innovative optimization algorithm that draws inspiration from swarm intelligence principles and mimics the social learning behavior observed in humans. The LSA algorithm optimizes the search process by integrating historical experience and real-time social information, enabling it to effectively navigate complex problem spaces. By doing so, it enhances its global development capability and provides efficient solutions to challenging optimization tasks. Additionally, the algorithm improves the collective learning capacity by incorporating teaching and active learning behaviors within the population, leading to improved local development capabilities. Furthermore, a dynamic adaptive control factor is utilized to regulate the algorithm’s global exploration and local development abilities. The proposed algorithm is rigorously evaluated using 40 benchmark test functions from IEEE CEC 2014 and CEC 2020, and compared against nine established evolutionary algorithms as well as 11 recently improved algorithms. The experimental results demonstrate the superiority of the LSA algorithm, as it achieves the top rank in the Friedman rank-sum test, highlighting its power and competitiveness. Moreover, the LSA algorithm is successfully applied to solve six real-world engineering problems and 15 UCI datasets of feature selection problems, showcasing its significant advantages and potential for practical applications in engineering problems and feature selection problems.

Research on fire detection method of complex space based on multi-sensor data fusion

Efficient and reliable fire detection methods are essential for addressing complex spatial characteristics of older residential communities, which often face poor fire safety conditions, various fire types, and challenges in rescue operations. In response to the problem of lacking comprehensive analysis of multidimensional data in traditional fire detection, which often leads to false alarms and missed alarms, this paper proposes a fire detection method based on multi-sensor data fusion. Various sensors are employed, including smoke sensors, temperature sensors, CO sensors, and flame sensors. These sensors collect multidimensional data, which are fused using improved adaptive filters and fuzzy Bayesian logical reasoning algorithms to establish a fire detection model based on multi-sensor data fusion. To additionally, false alarms and missed detections caused by sensor damage are further solved by exploring hidden spaces. Experimental results demonstrate that the proposed fire detection system based on multi-sensor data fusion effectively analyses fire occurrence and progression in older residential communities, and performs stability, accuracy and real-time property.

Research on Environmental Protection of Rural Ecotourism Based on PSR Model

This paper examines the relationship between rural ecotourism development and environmental protection in Province A, utilizing the PSR model and GAs. It employs a detailed evaluation of indicators in the tourism economy and ecological environment modules, using mathematical modeling and weight coefficient calculations to assess the impact of tourism activities. GAs are used to navigate the complex problem space inherent in balancing economic development with ecological sustainability. By analyzing data from 2005 to 2018, the coupling degree between tourism economy and ecological environment gradually increased from 0.1 to 0.6398. The results indicate a strengthening relationship between tourism economy and ecological environment, moving towards good or high-quality coordination. The degree of coupling, as measured by the PSR model, reflects the evolving interaction between rural tourism development and environmental protection. The findings provide valuable insights for policymakers and stakeholders in balancing economic growth with environmental sustainability in rural areas.

A typology of evaluative health platforms: Commercial interests and their implications for patient voice

Interactions in the healthcare system today involve an important new set of actors: evaluative health platforms (EHPs). These platforms are not neutral intermediaries, but active moderators of how patients express opinions, choose providers, and consume health-related information. This paper adds to our understanding of the varied and evolving commercial interests of EHPs and the implications these have for patient voice. We analyze 71 platforms in the USA, UK, and Germany and identify five ideal types: subscribers, analyzers, advertisers, regulators, and scammers. Each platform type enacts a unique competitive strategy through an evaluative infrastructure which constrains but also generates possibilities for patient voice. Based on our typology, we develop three contributions. First, we nuance universalizing claims about the consequences of platform capitalism by specifying the diverse strategies underpinning competition between EHPs in different countries, and showing how each strategy leads evaluative infrastructures to develop in ways that impact patient voice. Second, we show how patients can navigate the challenges of a complex EHP space by exercising their ability to choose between platforms. Finally, we outline the conditions platforms need to fulfil to become empowering. Overall, this study highlights the varied and complex relationship between platform business models and user voice, which exists not only in healthcare, but also in many other fields.

The Specific Application and Meaning Expression of Folk Art in Urban Landscape Design Under the Optimized Interactive Genetic Algorithm

With the rapid and stable growth of China's economy and the rapid development of urban construction, urban complexes have sprung up in major cities. The lag in urban ecological environmental protection, maintenance and construction has brought great pressure to the needs of today's urban residents in terms of life, safety, and sense of belonging. In the major cities with many high-rise buildings in contemporary China, land resources are becoming more and more scarce, and the urban ecological environment is in urgent need of recycling, and due to the blind imitation of Western culture and design mode and the neglect of my country's traditional regional culture, the urban landscape lacks interaction, resonance, and sense of belonging with citizens, and the phenomenon of landscape similarity emerges in various cities, focusing on the landscape space of urban complexes. There are also these problems. Urban residents urgently need a third space that can adjust their physical, mental, and spiritual needs. How to design an urban complex landscape that meets the aesthetic needs and humanistic needs of contemporary cities and has regional characteristics has become the first important task of my research. Folk art is an artistic treasure created by the working people in their production and life. Folk art is the embodiment of cultural regionality and the foundation of national culture. It awakens people's awareness of the importance of local culture, awakens people's sense of belonging, and is closer to the local public life. Today, the living soil and social and humanistic environment of folk art are in the process of urbanization in our country, and there is a trend of gradual disappearance of lifestyle changes. How to make the contemporary urban complex landscape an organic soil for the survival, expression, application, and development of folk art is an important task in contemporary urban landscape design. Based on optimization, related concepts such as symbols, folk art symbols, urban complexes, urban complex landscape design, etc. have been sorted out. The relevant experimental results show that the construction land accuracy of the logistic regression model based on genetic algorithm has increased from 78.0% to 85.3%. kappa increased from 74.5% to 81.2%. Research shows that the logistic regression parameter optimization method based on genetic algorithm has better simulation effect than the conventional logistic regression method and is more suitable for the situation of unbalanced data distribution and many data features in the simulation of large-scale urban land dynamic changes.

AR based Indoor Navigation System

Indoor navigation within college campuses poses unique challenges due to complex building layouts and varying levels of accessibility. This paper presents a student project aimed at developing an Augmented Reality based indoor navigation system (AR-INS) specifically tailored for college campuses. The system integrates computer vision, depth perception and Google’s ARCore techniques to provide users with real-time navigation guidance using their smartphones or AR-enabled devices. We describe the system architecture, implementation details, and evaluation results from user testing conducted on our college campus. The project highlights the feasibility of utilizing AR technology for enhancing campus navigation experiences and provides valuable insights for future research and development in this domain. This project addresses the growing need for efficient indoor navigation, which can significantly improve the overall user experience and accessibility in indoor spaces. The AR-INS is not only user- friendly but also adaptable to various environments, making it a valuable addition to smart building infrastructure and enhancing the quality of life for users. The implementation of this system can have an impact on how people navigate complex indoor spaces, and give an solution for complex indoor spaces. Keywords—AR-INS, computer vision, depth perception Google’s ARCore

Adaptive multi-spectral mimicking with 2D-material nanoresonator networks

Abstract Active nanophotonic materials that can emulate and adapt between many different spectral profiles—with high fidelity and over a broad bandwidth—could have a far-reaching impact, but are challenging to design due to a high-dimensional and complex design space. Here, we show that a metamaterial network of coupled 2D-material nanoresonators in graphene can adaptively match multiple complex absorption spectra via a set of input voltages. To design such networks, we develop a semi-analytical auto-differentiable dipole-coupled model that allows scalable optimization of high-dimensional networks with many elements and voltage signals. As a demonstration of multi-spectral capability, we design a single network capable of mimicking four spectral targets resembling select gases (nitric oxide, nitrogen dioxide, methane, nitrous oxide) with very high fidelity (> 90%). Our results could impact the design of highly reconfigurable optical materials and platforms for applications in sensing, communication and display technology, and signature and thermal management.

Linearization of holomorphic Lipschitz functions

AbstractLet and be complex Banach spaces with denoting the open unit ball of . This paper studies various aspects of the holomorphic Lipschitz space , endowed with the Lipschitz norm. This space consists of the functions in the intersection of the sets of Lipschitz mappings and of bounded holomorphic mappings, from to . Thanks to the Dixmier–Ng theorem, is indeed a dual space, whose predual shares linearization properties with both the Lipschitz‐free space and Dineen–Mujica predual of . We explore the similarities and differences between these spaces, and combine techniques to study the properties of the space of holomorphic Lipschitz functions. In particular, we get that contains a 1‐complemented subspace isometric to and that has the (metric) approximation property whenever has it. We also analyze when is a subspace of , and we obtain an analog of Godefroy's characterization of functionals with a unique norm preserving extension in the holomorphic Lipschitz context.

Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening.

Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While mathematical modeling and machine learning have become vital in predicting drug-target interactions and properties, there is untapped potential in computational drug discovery due to the vast and complex chemical space. This paper builds on our recently published computational fragment-based drug discovery (FBDD) method called fragment databases from screened ligand drug discovery (FDSL-DD). FDSL-DD uses in silico screening to identify ligands from a vast library, fragmenting them while attaching specific attributes based on predicted binding affinity and interaction with the target subdomain. In this paper, we further propose a two-stage optimization method that utilizes the information from prescreening to optimize computational ligand synthesis. We hypothesize that using prescreening information for optimization shrinks the search space and focuses on promising regions, thereby improving the optimization for candidate ligands. The first optimization stage assembles these fragments into larger compounds using genetic algorithms, followed by a second stage of iterative refinement to produce compounds with enhanced bioactivity. To demonstrate broad applicability, the methodology is demonstrated on three diverse protein targets found in human solid cancers, bacterial antimicrobial resistance, and the SARS-CoV-2 virus. Combined, the proposed FDSL-DD and a two-stage optimization approach yield high-affinity ligand candidates more efficiently than other state-of-the-art computational FBDD methods. We further show that a multiobjective optimization method accounting for drug-likeness can still produce potential candidate ligands with a high binding affinity. Overall, the results demonstrate that integrating detailed chemical information with a constrained search framework can markedly optimize the initial drug discovery process, offering a more precise and efficient route to developing new therapeutics.

Prismatic-element SBPML coupled with SBFEM for 3D infinite transient wave problems

In this paper, an enhanced prismatic-element scaled boundary perfectly matched layer (SBPML) is developed, which is a novel time-domain artificial boundary method for 3D infinite wave problems. The SBPML permits the utilization of an artificial boundary with general geometry and can consider planar physical surfaces and interfaces extending to infinity. Moreover, this enhancement enables the seamless integration with surface meshes of arbitrary polygonal faces (such as triangles, quadrangles, pentagons, heptagons, octagons, etc.) at the truncation boundaries. The interior domain can be modelled by polyhedrons formulated by the scaled boundary finite element method (SBFEM). The geometric shape of the SBPML elements in the proposed method, within the local coordinate system, takes the form of a regular prism with regular polygonal top and bottom faces. Consequently, it is referred to as the Prismatic-element SBPML. Local scaled boundary coordinates based on the polygonal shape function are firstly introduced into the truncated infinite domain to describe its general geometry properties and provide the coupled capabilities with arbitrary polygonal face meshes at the truncation boundaries of numerical models. Then, a complex stretching function from PML is applied to radial direction of the local scaled boundary coordinates to map the physical space onto the complex space, resulting in a SBPML domain. Upon spatial discretization, the proposed method is formulated using a 2nd-order mixed displacement-stress unsplit-field formulation, allowing seamless coupling with FEM and SBFEM originally designed for the interior computational domain. Finally, three benchmark examples of wave propagation problems in unbounded domains and two soil-structure interaction problems of engineering problems are presented to demonstrate the accuracy and robustness of the Prismatic-element SBPML.

Bacitracin Methylene Disalicylate (BMD) Treatment Affects Spleen Proteome in Broiler Chicks Infected with Salmonella enteritidis

Bacitracin Methylene Disalicylate (BMD), as a feed additive to poultry diets, enhances digestion, prevents Salmonella enteritidis (SE) colonization, and treats current infections. The objective of this study was to utilize a quantitative proteomic approach to determine the effect of BMD feed additive on broiler chickens challenged with SE in the spleen proteome. At 1 d of age, chicks were randomly allocated into four groups: control with and without SE challenge (CON, n = 60; CON-SE, n = 60), BMD with and without SE challenge (BMD, n = 60; BMD-SE, n = 60). Birds in the CON-SE and BMD-SE treatment were administered SE inoculum by oral gavage. On day three and day seven post-gavage, the spleen was collected aseptically from birds in each treatment group (CON, n = 4/day; CON-SE, n = 4/day; BMD, n = 4/day; BMD-SE, n = 4/day). Proteomic analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS) showed an increased abundance of 115 proteins and decreased of 77 due to the BMD. Proteins that decreased in abundance were enriched for fibrinogen complex and extracellular space, whereas proteins that increased in abundance were enriched for proteasome-mediated ubiquitin-dependent protein catabolic process and mitochondrion. Analysis of the interaction between BMD and the Salmonella challenge found 230 differentially abundant proteins including proteins associated with RNA binding, spliceosome, protein transport, and cell adhesion among the upregulated proteins, and those associated with protein folding, carbon metabolism, biosynthesis of nucleotide sugars, response to oxidative stress, positive regulation of NIK/NF-kappaB signaling, and inflammatory response among the downregulated proteins. The impact of BMD treatment on spleen proteome indicates an anti-apoptotic effect. BMD also modified the response of the spleen to the SE challenge with a marked decrease in proteins that prompt cytokine synthesis and an increase in proteins involved in the selective removal of unfolded proteins.

Rigidity results on totally real submanifolds in complex space forms

Rigidity results on totally real submanifolds in complex space forms

Loop Space Decompositions of Moment-Angle Complexes Associated to Flag Complexes

Abstract We prove that the loop space of the moment-angle complex associated with the k-skeleton of a flag complex belongs to the class $\mathcal{P}$ of spaces homotopy equivalent to a finite-type product of spheres and loops on simply connected spheres. To do this, a general result showing $\mathcal{P}$ is closed under retracts is proved.

Embedded parallel-actuated technology for deformable space segmented mirror

The deployable segmented space imaging system is an important solution for future ultra-large aperture space optical systems. To achieve the imaging capability of an equivalent aperture monolithic mirror, it requires not only to ensure the positional accuracy in the cophasing process, but also to have extremely high surface accuracy and curvature consistency of the sub-mirrors. However, this work is extremely challenging due to the manufacturing error of the sub-mirrors and the complex space environment. Active optical technology can ensure the surface shape accuracy of the spliced mirror by controlling the mirror surface deformation and compensating for the wavefront aberration. This article compares and analyzes the control ability of two types of deformable mirrors actuated by vertical and parallel methods. We explored the characteristics of the influence function mathematical models of the two types of actuation forms and compared the aberration and curvature correction abilities of them through finite element analysis, summarizing the advantages of the parallel actuation forms. Finally, a 300mm aperture embedded parallel-actuated deformable mirror was designed and manufactured, and relevant experiments were conducted to verify its adjustment ability. By comparing and analyzing the experimental results with the design results, the adjustment ability of the embedded parallel-actuated deformable mirror was verified.

Optimization of landscape garden greening design based on multi objective genetic algorithm

This paper presents a novel approach to optimize landscape garden greening design using a multi-objective genetic algorithm (MOGA)[1]. Incorporating genetic algorithms into landscape architecture offers a promising avenue for efficiently navigating the complex and multidimensional design space inherent in green infrastructure projects. Through a comprehensive bibliometric analysis of existing literature, this study synthesizes key insights into the application of genetic algorithms in landscape design and identifies gaps for further exploration[2]. Leveraging the evolutionary process of genetic algorithms, our methodology focuses on simultaneously optimizing multiple objectives such as biodiversity conservation, aesthetic appeal, water efficiency, and ecosystem services provisioning[3]. By iteratively evolving and selecting landscape configurations based on fitness criteria derived from these objectives, the MOGA enables designers to explore a diverse range of design alternatives and identify Pareto-optimal solutions that balance competing priorities. The integration of genetic algorithms into landscape design facilitates an iterative and adaptive design process, allowing for the exploration of complex trade-offs and the generation of innovative design solutions. Through a case study application, we demonstrate the effectiveness of the MOGA approach in optimizing landscape garden greening designs, showcasing its potential to enhance sustainability, resilience, and functionality in urban green spaces. This research contributes to the growing body of knowledge on computational design methods in landscape architecture and provides valuable insights for practitioners and researchers seeking to leverage genetic algorithms for optimizing green infrastructure projects.

Self-reinforced MOF-based Nanogel Alleviates Osteoarthritis by Long-acting Drug Release.

Intra-articular injection of drugs is an effective strategy for osteoarthritis (OA) treatment. However, the complex microenvironment and limited joint space result in rapid clearance of drugs. Herein, a nanogel-based strategy was proposed for prolonged drug delivery and microenvironment remodeling. Nanogel was constructed through functionalization of hyaluronic acid (HA) by amide reaction on the surface of Kartogenin (KGN)-loaded zeolitic imidazolate framework-8 (denoted as KZIF@HA). Leveraging the inherent hydrophilicity of HA, KZIF@HA spontaneously forms nanogels, ensuring extended drug release in the OA microenvironment. KZIF@HA exhibits sustained drug release over one month, with low leakage risk from the joint cavity compared to KZIF, enhanced cartilage penetration, and reparative effects on chondrocytes. Notably, KGN released from KZIF@HA serves to promote extracellular matrix (ECM) secretion for hyaline cartilage regeneration. Zn2+ release reverses OA progression by promoting M2 macrophage polarization to establish an anti-inflammatory microenvironment. Ultimately, KZIF@HA facilitates cartilage regeneration and OA alleviation within three months. Transcriptome sequencing validates that KZIF@HA stimulates the polarization of M2 macrophages and secretes IL-10 to inhibit the JNK and ERK pathways, promoting chondrocytes recovery and enhancing ECM remodeling. This pioneering nanogel system offers new therapeutic opportunities for sustained drug release, presenting a significant stride in OA treatment strategies. This article is protected by copyright. All rights reserved.

‘We are here to put the past behind us’: violence and welfare governmentality in Finnish family law

ABSTRACT Family justice occupies a complex space where public social (welfare) law intersects with private family law. These two legal domains bring distinct perspectives on subjects, rights, responsibilities, and entitlements. This article draws on interview data and previous research to examine welfare governmentality in family law, particularly in Finland and more broadly in the Nordic welfare states. Using intimate partner violence (IPV) as the lens of analysis, the article studies family law’s exclusionary structures that render particular forms of loss resulting from the relationship non-addressable. The rationales embedded in law, as evident through welfare governmentality, significantly impact the production of specific types of vulnerable, autonomous, gendered, and relational subjects. Moreover, these rationales shape our understanding of the law’s operations, categories, truth claims, knowledge development, and regulatory practices. Notably, family law-as-knowledge falls short in recognising violence and the subsequent losses suffered by victims within the contexts of financial settlements and child custody and access. This failure can be attributed to conceptual distinctions within family law, an overriding focus on the child’s interests, and the law’s national orientation.

Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm

The detection of two-line element (TLE) outliers and space events play a crucial role in enhancing spatial situational awareness. Therefore, this paper addresses the issue of TLE outlier detection methods that often overlook the mutual influence of multiple factors. Hence, a Multivariate Gaussian Mixture Model (MGMM) is introduced to consider the interdependencies among various indicators. Additionally, a Multi-strategy Genetic Algorithm (MGA) is employed to adjust the complexity of the MGMM, allowing it to accurately learn the actual distribution of TLE data. Initially, the proposed method applies probabilistic fits to the predicted error rate changes for both the TLE semi-major axis and the orbital inclination. Chaos initialization, a posterior probability penalty, and local optimization iterations are subsequently integrated into the genetic algorithm. These enhancements aim to estimate the MGMM parameters, addressing issues related to poor robustness and the susceptibility of the MGMM to converge to local optima. The algorithm’s effectiveness is validated using TLE data from typical space targets. The results demonstrate that the optimized algorithm can efficiently detect outliers and maneuver events within complex TLE data. Notably, the comprehensive detection performance index, measured, using the F1 score, improved by 15.9% compared to the Gaussian mixture model. This significant improvement underscores the importance of the proposed method in bolstering the security of complex space environments.

On some operator norm inequalities

In this paper, we use known classical inequalities to establish some operator norm inequalities for a pair of norm-attainable operators on a complex Hilbert space.