Space Complex Research Articles

Sequential topological complexity of aspherical spaces and sectional categories of subgroup inclusions

AbstractWe generalize results from topological robotics on the topological complexity (TC) of aspherical spaces to sectional categories of fibrations inducing subgroup inclusions on the level of fundamental groups. In doing so, we establish new lower bounds on sequential TCs of aspherical spaces as well as the parametrized TC of epimorphisms. Moreover, we generalize the Costa–Farber canonical class for TC to classes for sequential TCs and explore their properties. We combine them with the results on sequential TCs of aspherical spaces to obtain results on spaces that are not necessarily aspherical.

New curvature characterizations for spherical space forms and complex projective spaces

In this paper, we introduce a new positivity notion for curvature of Riemannian manifolds and obtain characterizations for spherical space forms and the complex projective space C P n \mathbb {C}\mathbb {P}^n .

Computational Complexity of Minimal Trap Spaces in Boolean Networks

Computational Complexity of Minimal Trap Spaces in Boolean Networks

Noise hindering sound complexity in urban environments: Towards a novel evaluation criterion for urban sustainability

Urban environments are complex adaptive systems, wherein changes in one area can lead to unforeseen consequences in others. Environmental noise emerges as a multifaced issue influencing various receivers across different spatial and temporal scales. The perception of noise as a high-intensity or unwanted sound has defined the concept of quietness accordingly overlooking sustainability co-benefits. This study proposes an alternative evaluation criterion for urban planning and design, focusing on the sound complexity of public spaces that are vulnerable to environmental noise fluctuations. To determine the sensitivity of sound complexity to environmental noise, sound level measurements and sound recordings were conducted along the waterfront of a Mediterranean metropolis during the pandemic lockdowns (April 2021). Additionally, the bird dawn chorus phenomenon was studied. The analysis of collected data revealed an inverse relationship between complexity and noise, highlighting an additional impact on the urban sound environment. The promotion of sound complexity as a strategy for livable and sustainable cities can guide urban planners and designers in developing quiet and ecologically resilient urban environments.

Kinematics and dynamics characteristics of a double-ring truss deployable antenna mechanism based on triangular prism deployable unit

In order to effectively improve the single-ring truss deployable antenna mechanism due to the large aperture caused by the problem with low structural strength and low-profile accuracy, a series of double-ring truss deployable antenna mechanisms (DRTDAM) are proposed with constant height during folding and deployment process. First, a variety of DRTDAMs are proposed based on tetrahedral units and their topologies are analyzed. Secondly, degree-of-freedom (DOF) characteristics of DRTDAM proposed in this paper are analyzed based on the screw theory and screw-constrained topological graphs and based on this, the kinematic characteristics of DRTDAM is investigated. Thirdly, the dynamics of the whole DRTDAM is built with Newton-Euler equations of multi-rigid body system. Finally, the correctness of above analysis is verified through dynamics analysis software ADAMS and numerical analysis software MATLAB, and the principle prototype is produced to verify the correctness of DOF analysis. The mechanism proposed in this paper enriches the configuration of DRTDAM, and the process of kinematic characterization method is clear and simple, which is meaningful for the research in space complex mechanism.

The Power of Monuments. From Social Unrest to Urban Violence (2020–2023) | O Poder dos Monumentos: Da Agitação Social à Violência Urbana (2020–2023)

This paper examines the complexities of heritage, memory, and monumentality in public spaces, highlighting the dynamic interplay of top-down and bottom-up processes, competing narratives, and the shaping of collective identity. It argues that excluding communities from shaping commemorative landscapes often leads to conflict, with monuments becoming contentious and vandalized. Conversely, unilateral decisions by political elites can deepen social divides. The paper concludes by stressing the need for collaborative, participatory approaches to creating inclusive and democratic public memory.

Patient´s perception of spatial complexity in emergency spaces of a hospital in China: a method for stakeholders to improve design and management

ABSTRACT The focus of medical facility users has shifted from prioritizing epidemic prevention to prioritizing experience after COVID-19, particularly in the emergency department (ED) of a large-scale Chinese hospital. The current visual elements of the emergency department provide a negative perception to the patients who visit the department. The study aims to optimize the ED's spatial design by understanding how visual elements affect patients' perception of spatial complexity. The methodology involved recreating a patient's typical path through the ED using data from the hospital information system (HIS), capturing images at 70 location nodes, and calculating the fractal dimensions of these images using the box-counting method. Visual elements were categorized into eight groups, and their fractal dimensions and proportions within the images were analyzed. A breakpoint graph was developed to track changes in spatial complexity perception as patients move through the ED. Statistical analysis, including correlation and one-way ANOVA, revealed that spatial attributes and lighting conditions significantly impact spatial complexity. From the results, grouping analysis was conducted on the visual elements of the horizontal surfaces, vertical surfaces, ceiling, furniture, and vegetation. Further optimization was proposed. In conclusion, this study highlights the importance of visual elements in emergency spaces, directly influencing users' perception of spatial complexity. The relationship between spatial complexity and fractal dimension is further emphasized. Stakeholders and designers can use the findings of this study as references to make informed decisions to improve the user's experience in the healthcare-built environment.

Ice age-driven range shifts of diploids and expanding autotetraploids of Biscutella laevigata within a conserved niche.

Early studies of the textbook mixed-ploidy system Biscutella laevigata highlighted diploids restricted to never-glaciated lowlands and tetraploids at high elevations across the European Alps, promoting the hypothesis that whole-genome duplication (WGD) is advantageous under environmental changes. Here we addressed long-held hypotheses on the role of hybridisation at the origin of the tetraploids, their single vs multiple origins, and whether a shift in climatic niche accompanied WGD. Climatic niche modelling together with spatial genetics and coalescent modelling based on ddRAD-seq genotyping of 17 diploid and 19 tetraploid populations was used to revisit the evolution of this species complex in space and time. Diploids differentiated into four genetic lineages corresponding to allopatric glacial refugia at the onset of the last ice age, whereas tetraploids displaying tetrasomic inheritance formed a uniform group that originated from southern diploids before the last glacial maximum. Derived from diploids occurring at high elevation, autotetraploids likely inherited their adaptation to high elevation rather than having evolved it through or after WGD. They further presented considerable postglacial expansion across the Alps and underwent admixture with diploids. Although the underpinnings of the successful expansion of autotetraploids remain elusive, differentiation in B. laevigata was chiefly driven by the glacial history of the Alps.

Optimising outbound scheduling in shipyard steel stockyard

ABSTRACT In shipyard operations, steel stockyards manage the intake, storage and distribution of raw materials. However, the inherent customisation of steel plates presents challenges such as inefficient outbound scheduling and elevated energy consumption. To address these issues, this study introduces a novel outbound scheduling model that comprehensively considers manufacturing orders and the current warehouse status during the outbound process. To solve this model, an improved ant colony optimisation algorithm, based on an energy consumption selection strategy (ECS-IACO), is proposed. The algorithm adopts a dynamic pheromone matrix and updates rules to address the complexity of high-dimensional state spaces. The energy consumption selection strategy is integrated into initialisation and state transitions, guiding and decisions throughout. Extensive numerical tests show that the ECS-IACO is robust and highly efficient in solving problems. In real-world scenarios tests, the ECS-IACO reduced energy use by up to 22% more than other algorithms, supporting sustainability efforts in business operations.

Unraveling the Hemolytic Toxicity Tapestry of Peptides using Chemical Space Complex Networks.

Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, while aspartic acid, methionine, histidine, asparagine and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models (MQSSMs), which outperformed cutting-edge machine learning (ML)-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, to characterize the chemical space and to discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity.

Sovereignty cubed: The Arctic as a territorial and ontological volume

State sovereignty over space has traditionally been viewed as confined to a bounded land area. However, scholarship in critical cartography, political, and social theory shows this understanding to be flawed. This perspective not only limits understandings of sovereignty density, but also weakens our understanding of the limits of the state. This is particularly evident in the Arctic, where sovereignty discourse often fails to address the complexities of space in connection with authority and legitimacy for decision-making in the region. This article first critically examines Arctic sovereignty, challenging the notion of sovereignty as merely a horizontal area with land or sea boundaries. Instead, it argues that Arctic geopolitics and governance are better understood when sovereignty is considered as a scalar concept encompassing a territorial volume. Second, it contextualises the processes involved in the legitimisation of sovereignty and governance practices across the Arctic, framing this as ontological sovereignty. The article contends that Arctic sovereignty and governance are more comprehensively understood by shifting from a strictly material perspective to an ontological framing, which encompasses control over the nature and existence of Arctic spaces, with implications for future development and understandings of Arctic governance.

XgCPred: Cell type classification using XGBoost-CNN integration and exploiting gene expression imaging in single-cell RNAseq data

BackgroundThe advent of single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomic research by enabling the exploration of gene expression at an individual cell level. This advancement sheds light on how cells differentiate and evolve over time. Effectively classification cell types within scRNA-seq datasets are essential for understanding the intricate cell compositions within tissues and elucidating the origins of various diseases. Challenges persist in the field, emphasizing the need for precise categorization across diverse datasets, addressing aggregated cell data, and managing the complexity of high-dimensional data spaces. MethodologyXgCPred is a novel approach combining XGBoost with convolutional neural networks (CNNs) to provide cell type classification with better accuracy in single-cell RNA-seq data. This combo reveals how well CNNs can detect spatial hierarchy in gene expression images and how XGBoost performs with large volumes of data. XgCPred utilizes an imaging representation of gene expression that is based on the hierarchical organization of genes found in the KEGG BRITE database. ResultsRigorous testing of XgCPred across multiple scRNA-seq datasets, each presenting unique challenges such as varying cell counts, gene expression diversity, and cellular heterogeneity, has demonstrated its superiority compared to earlier methods. The algorithm shows remarkable accuracy and precision in cell type annotation, achieving near-perfect classification scores in some cases. These results underscore its capability to effectively manage data variability. ConclusionsXgCPred distinguishes itself through its dependable and accurate cell type classification across a range of scRNA-seq datasets. Its effectiveness stems from sophisticated data handling and its ability to adapt to the complexities inherent in scRNA-seq data. XgCPred delivers reliable cell annotations essential for further biological analysis and research, marking a significant advancement in genomic studies. With scRNA-seq datasets growing in size and complexity, XgCPred offers a scalable and potent solution for cell type identification, potentially enhancing our understanding of cellular biology and aiding in the precise detection of diseases. XgCPred is a useful tool in genomic research and tailored therapy because it solves current constraints on computing efficiency and generalizability.

Thermal regulation potential of urban green spaces in a changing climate: Winter insights

Global warming affects both summer and winter temperatures, altering the ecological and social dynamics of urban green spaces not only during the summer months but also during the winter. Strategies to mitigate climate change impacts often emphasize increasing urban vegetation cover, but the effectiveness of these green spaces in regulating the local microclimate depends on various factors, including the vegetation structure. These effects remain largely underexplored during the winter season. To investigate the thermal regulation capacity of urban green spaces we measured air temperature and humidity in 36 parks with different sizes and vegetation structures in Munich, Germany, in their non-green surroundings, as well as in a nearby forest during the 2022–23 winter. We then analyzed the relationship between the local microclimatic differences and the vegetation structure derived from mobile laser scans. In comparison with the nearby forest, we measured a winter urban heat island effect of 1.8 °C in Munich. The urban microclimates in winter were mainly influenced by the urban landscape of Munich, namely the distance to the city center with increasing air temperature closer to the center. Urban green spaces in Munich provided small but consistent local cooling and humidifying effects throughout the winter. These cooling effects largely depended on the green space size but partially also on the vegetation structure. We found a significant relationship between the microclimatic difference, the vegetation density, and the vertical homogeneity of vegetation. The canopy cover, however, could not significantly predict the cooling effect in winter. We conclude that increasing the structural complexity of urban green spaces through management decisions could improve their cooling effect and ecological value, even during winter months. To maximize the ecological and climatic benefits of urban green spaces, a nuanced understanding and management of urban microclimates is needed across all seasons.

Multi-Objective Unsupervised Feature Selection and Cluster Based on Symbiotic Organism Search

Unsupervised learning is a type of machine learning that learns from data without human supervision. Unsupervised feature selection (UFS) is crucial in data analytics, which plays a vital role in enhancing the quality of results and reducing computational complexity in huge feature spaces. The UFS problem has been addressed in several research efforts. Recent studies have witnessed a surge in innovative techniques like nature-inspired algorithms for clustering and UFS problems. However, very few studies consider the UFS problem as a multi-objective problem to find the optimal trade-off between the number of selected features and model accuracy. This paper proposes a multi-objective symbiotic organism search algorithm for unsupervised feature selection (SOSUFS) and a symbiotic organism search-based clustering (SOSC) algorithm to generate the optimal feature subset for more accurate clustering. The efficiency and robustness of the proposed algorithm are investigated on benchmark datasets. The SOSUFS method, combined with SOSC, demonstrated the highest f-measure, whereas the KHCluster method resulted in the lowest f-measure. SOSFS effectively reduced the number of features by more than half. The proposed symbiotic organisms search-based optimal unsupervised feature-selection (SOSUFS) method, along with search-based optimal clustering (SOSC), was identified as the top-performing clustering approach. Following this, the SOSUFS method demonstrated strong performance. In summary, this empirical study indicates that the proposed algorithm significantly surpasses state-of-the-art algorithms in both efficiency and effectiveness. Unsupervised learning in artificial intelligence involves machine-learning techniques that learn from data without human supervision. Unlike supervised learning, unsupervised machine-learning models work with unlabeled data to uncover patterns and insights independently, without explicit guidance or instruction.

Assessing Relations Among Visual Variables in Hotel Lobbies Using Deep Learning

To understand the relationships between cognitive variables like visual complexity, coherence, and colour contrast in interior spaces, direct numerical analysis is crucial. Conventional approaches are limited due to the brain's struggle to process visual information without cognitive manipulation. However, advancements in artificial intelligence enable direct examinations. This study used a convolutional neural network to assess the intensity of colour contrasts in images of 5-star hotel lobby interiors with high levels of coherence and complexity. The results indicated that visually complex lobby interiors have less warm-cool and dark-light contrast but more pronounced complementary contrast than coherent lobby interiors. Additionally, a negative correlation was identified between complementary and warm-cool contrasts across all perspectives. These results underscore the potential influence of specific colour contrast types on the cognitive experience of interiority.

A mission planning method for deep space detectors using deep reinforcement learning

Mission planning for deep space detectors is a pivotal step in the successful execution of detection missions. Traditional planning approaches, which typically compartmentalize mission planning and data transmission scheduling, exhibit limitations in adapting to uncertainties and are often inadequate in responding to unforeseen opportunity targets. This paper introduces a mission planning method for deep space detectors designed to address these challenges. Firstly, a Markov Decision Process (MDP) model is formulated, integrating mission planning and data transmission scheduling, with a consideration for planning balance between detection missions and opportunity targets. Subsequently, a Planning Balance with Proximal Policy Optimization (PB-PPO) algorithm is proposed. The proposed algorithm, based in the Proximal Policy Optimization (PPO) algorithm, integrates an orthogonal initialization algorithm to afford improved control over parameter updates. Furthermore, a dynamic learning rate strategy is implemented to accelerate convergence speed. Experimental results show that, the PB-PPO achieves rewards that are 4.42%, 6.78%, 18.32%, and 26.81% higher than those obtained by compared algorithms. Additionally, the PB-PPO demonstrates the capability to address the planning balance between detection missions and opportunity targets, ensuring stable reward growth even when planning a significant number of opportunity targets. In summary, the PB-PPO integrates of MDP, deep reinforcement learning, and innovative strategies to make it a robust solution for detector mission planning in the complex and dynamic environment of deep space.

A Sociotechnical Readiness Level Framework for the Development of Advanced Nuclear Technologies

The Technology Readiness Level (TRL) scale was initially developed by the National Aeronautics and Space Administration in the 1970s and is now widely used in space, nuclear, and other complex technology sectors in the United States and beyond. The TRL scale is particularly useful for determining where the extrapolation of untested subsystems or features could produce technical risk, cause expensive redesigns, or act as a roadblock to technology development. In this paper, we propose the development of a sociotechnical readiness level or SRL, premised on the understanding that the successful development and eventual use of a technology requires achieving not only full technological readiness but also anticipating, prioritizing, and addressing societal concerns that may arise during the course of development of a technology. Failures to anticipate and address societal factors in the early stages of technology development have led to high-profile delays, and in some cases, ultimate failures of nuclear technology projects. The sociotechnical readiness scale, which conceptually draws on the design research and science and technology studies scholarship, centers on the principles of equity and environmental justice in technology design and emphasizes the need for social engagement during the process of technology development. Nowhere is such an approach to technology development more vital or needed than for the long-term management of spent nuclear fuel.

Assessment of the endurance limit of light structural metal components using the fatigue surface development method

The paper presents a numerical method developed by the authors. The method consists in obtaining a three-dimensional component fatigue surface model according to the results of tests corresponding to two to four values of the operational factor, making it possible to plot the fatigue limit – operational parameter curve. A description of the algorithm of the method is presented, its validity is confirmed. The results show good agreement with outside empirical data for other values of the operational parameters. A model of the fatigue surface is constructed. The model specifies the calculation of strength of aircraft elements operating in rarified atmosphere (e.g. engine compartments of space complexes and upper stages of carrier rockets). The model increases the endurance calculation accuracy and enables structural mass optimization.

NSGAN: a non-dominant sorting optimisation-based generative adversarial design framework for alloy discovery

The design and discovery of new materials is fundamental to advancing scientific and technological innovation. The recent emergence of the materials genome concept holds great promise in revolutionising materials science by enabling the systematic utilisation of data for efficient prediction and optimisation of ‘superior’ materials. However, the materials genome approach can be stymied by the vast complexity of design spaces, which often demand substantial computational resources and sophisticated data processing capabilities. To address these challenges, this work introduces a generative design framework called the non-dominant sorting optimisation-based generative adversarial networks (NSGAN). Capitalising on the synergies of genetic algorithms (GA) and generative adversarial networks (GANs), NSGAN provides a robust and efficient approach for tackling high-dimensional multi-objective optimisation design problems. To validate the efficacy of the proposed framework, we applied the model to a comprehensive dataset of aluminium alloys. Additionally, an online tool was created as a supplementary resource, offering a brief introduction to this innovative method for the wider scientific community. This study explores the potential of a predictive and data-driven approach in material design, indicating a promising pathway for widespread applications in the field of materials science.

Role of metaheuristic algorithms in healthcare: A comprehensive investigation across clinical diagnosis, medical imaging, operations management, and public health

Abstract Metaheuristic algorithms have emerged in recent years as effective computational tools for addressing complex optimization problems in many areas, including healthcare. These algorithms can efficiently search through large solution spaces and locate optimal or near-optimal responses to complex issues. Although metaheuristic algorithms are crucial, previous review studies have not thoroughly investigated their applications in key healthcare areas such as clinical diagnosis and monitoring, medical imaging and processing, healthcare operations and management, as well as public health and emergency response. Numerous studies also failed to highlight the common challenges faced by metaheuristics in these areas. This review thus offers a comprehensive understanding of metaheuristic algorithms in these domains, along with their challenges and future development. It focuses on specific challenges associated with data quality and quantity, privacy and security, the complexity of high-dimensional spaces, and interpretability. We also investigate the capacity of metaheuristics to tackle and mitigate these challenges efficiently. Metaheuristic algorithms have significantly contributed to clinical decision-making by optimizing treatment plans and resource allocation and improving patient outcomes, as demonstrated in the literature. Nevertheless, the improper utilization of metaheuristic algorithms may give rise to various complications within medicine and healthcare despite their numerous benefits. Primary concerns comprise the complexity of the algorithms employed, the challenge in understanding the outcomes, and ethical considerations concerning data confidentiality and the well-being of patients. Advanced metaheuristic algorithms can optimize the scheduling of maintenance for medical equipment, minimizing operational downtime and ensuring continuous access to critical resources.