Adaptive Structure Research Articles

Investigating the Synergistic Effect of Decoration and Doping in Silver/Strontium Titanate for Air Remediation.

Strontium titanate (STO) and its variants have emerged as leading materials in photocatalysis, particularly for degrading nitrogen oxides (NOx), due to their non-toxic nature, structural adaptability, and exceptional thermal stability. Although the one-pot sol-gel method leads to high-quality photocatalysts, areas remain for improvement. This study examines the impact of ethanol as a cosolvent in STO synthesis, focusing on optimizing the water-to-ethanol volume ratio. The findings reveal that a 1:3 ratio significantly enhances macropore formation and photocatalytic efficiency, achieving 42% NOx degradation under LED within three hours. Furthermore, incorporating 8.0 wt.% Ag into STO substantially improves visible light absorption and enables complete NOx elimination, thanks to enhanced charge separation and localized surface plasmon resonance. Even at high temperatures (1100 °C), the Ag-STO photocatalyst maintains partial activity, despite exceeding silver's melting point. These results highlight the potential of STO-based materials for industrial applications, positioning them as a promising solution for effective NOx mitigation.

Adaptation of skin structures to environmental variations in anurans from southern and southwestern China.

Variations in skin structures can possibly reflect local adaptation to distinct environmental factors. As the primary interface with the surrounding environment, amphibian skin undergoes phenotypic innovations that play a key role in protection, water absorption, and respiration. However, the effects of environmental factors on skin structures have been examined in only a limited number of species. Here, we conducted a comparative analysis of the skin structures of 102 Chinese anuran species across varying geographical distributions and habitat types. Our results revealed that the total volume of granular glands and capillary density in the dorsal skin significantly increased with increasing latitude. We also found that the thickness of calcified layers in both dorsal and ventral skin was positively correlated with annual temperature and negatively correlated with humidity. Additionally, terrestrial species exhibited the largest dorsal granular gland, whereas arboreal species had the smallest one. Likewise, the largest dorsal mucous gland was observed in aquatic species, while the smallest was found in terrestrial species. These results highlighted the importance of understanding the relationship between skin phenotypes and environmental variables and thus providing conservation strategies based on the evolutionary adaptations in anurans. Our study can contribute to the broader knowledge of evolutionary biology in anurans by demonstrating how specific skin traits are linked to survival and fitness across various ecological contexts.

Methodological Approaches in Military Science Research

Scientific research is a prerequisite for progress in all areas of society, and military science is no exception. However, its specificities require careful analysis in order to identify specific methodological approaches to ensure the added value of the results obtained. Moreover, the need for continuous transformation and adaptation of military structures to the operating environment and to the specific characteristics of contemporary armed conflicts leads to an increased interest in identifying innovative solutions in this field. Academic research is the approach to provide feasible and viable solutions to these challenges. However, there are relatively few works that address the methodological peculiarities specific to the field of military science. For this reason, the present article analyses how to organize and conduct scientific studies in the military field, proposing a structured framework of methodological approach in 14 steps to ensure the logical coherence of the approach, while highlighting the specificities of the field under analysis.

Shape-Morphing in Oxide Ceramic Kirigami Nanomembranes.

Interfacial strain engineering in ferroic nanomembranes can broaden the scope of ferroic nanomembrane assembly as well as facilitate the engineering of multiferroic-based devices with enhanced functionalities. Geometrical engineering in these material systems enables the realization of 3-D architectures with unconventional physical properties. Here, 3-D multiferroic architectures are introduced by incorporating barium titanate (BaTiO3, BTO) and cobalt ferrite (CoFe2O4, CFO) bilayer nanomembranes. Using photolithography and substrate etching techniques, complex 3-D microarchitectures including helices, arcs, and kirigami-inspired frames are developed. These 3-D architectures exhibit remarkable mechanical deformation capabilities, which can be attributed to the superelastic behavior of the membranes and geometric configurations. It is also demonstrated that dynamic shape reconfiguration of these nanomembrane architectures under electron beam exposure showcases their potential as electrically actuated microgrippers and for other micromechanical applications. This research highlights the versatility and promise of multi-dimensional ferroic nanomembrane architectures in the fields of micro actuation, soft robotics, and adaptive structures, paving the way for incorporating these architectures into stimulus-responsive materials and devices.

Reveal mechanical and optical properties changes during prolonged skin stretching via in vivo continuous investigation

Skin expansion is a well-established technique in plastic surgery, and recent studies have highlighted its potential in promoting hair regeneration. This study aimed to explore how the mechanical and optical properties of skin change during a prolonged stretching process. A hybrid method was developed to assess, in vivo, the effects of an 8-day skin stretching protocol—previously used in hair regeneration research—on the dorsal skin of mice. This method combined mechanical and optical measurement systems. Tensile stress–strain curves were generated using a spring-based setup, while optical properties such as scattering and birefringence were analyzed with a polarimetry imaging system that incorporated the Mueller matrix (MM) and Mueller matrix polar decomposition (MMPD) methods. The results showed that Young's modulus increased from approximately 5 kPa on day 1 to 60–100 kPa by days 6–8, indicating collagen fiber straightening and increased stiffness. Optical analysis revealed greater anisotropy in both scattering and birefringence, as reflected by changes in MM elements and MMPD results. These changes suggest skin adaptation and regeneration, particularly within the first 24 h of stretching. Interestingly, alterations in optical properties closely mirrored changes in mechanical properties, pointing to a coordinated process of structural remodeling and functional adaptation in the skin. These findings offer valuable insights into skin remodeling and adaptation, which could guide future tissue engineering strategies.

Beware of the Dark Side: The Rise of Cybercrimes

This study paper offers a top to bottom assessment of the mind boggling hardships introduced by cybercrime in India, pushed by the country's quick digitalization in different areas. The expansion in cybercrime occurrences, enveloping monetary extortion, information breaks, and cyberattacks, has arisen as a critical concern for partners in both public and business areas. This report investigations the essential drives executed by the Indian government, policing, and business elements to counter these difficulties. It inspects the developing complexities of digital dangers, evaluates the viability of current relief methods, and proposes thoughts for making a more adaptable and powerful network protection structure. The report offers fundamental bits of knowledge to upgrade India's safeguard against the raising scene of computerized dangers by tending to the essential causes and advancing nature of cybercrime in the country. The discoveries look to further develop India's online protection foundation and reinforce strength against expanding digital dangers.

Multivariate Flexible Metal-Organic Frameworks and Covalent Organic Frameworks.

Precise control of the void environment, achieved through multiple functional groups and enhanced by structural adaptations to guest molecules, stands at the forefront of scientific inquiry. Flexible multivariate open framework materials (OFMs), including covalent organic frameworks and metal-organic frameworks, meet these criteria and are expected to play a crucial role in gas storage and separation, pollutant removal, and catalysis. Nevertheless, there is a notable lack of critical evaluation of achievements in their chemistry and future prospects for their development or implementation. To provide a comprehensive historical context, the initial discussion explores into the realm of "classical" flexible OFMs, where their origin, various modes of flexibility, similarities to proteins, advanced tuning methods, and recent applications are explored. Subsequently, multivariate flexible materials, the methodologies involved in their synthesis, and horizons of their application are focussed. Furthermore, the reader to the concept of spatial distribution is introduced, providing a brief overview of the latest reports that have contributed to its elucidation. In summary, the critical review not only explores the landscape of multivariate flexible materials but also sheds light on the obstacles that the scientific community must overcome to fully unlock the potential of this fascinating field.

Variant of Organizational and Functional Adaptation Structures of a Typical Situational Centre to the Emergence of Crisis Situations

Due to the growing number of crisis situations (CS) in the modern world, such as natural disasters, man-made accidents, pandemics, cyber-attacks, financial crises, wars, and other negative events, it is important for the state to develop and adapt the organizational and functional structures of information and analytical units, including situation centers (SC), to respond effectively to such cases, thereby ensuring the country’s resilience in the face of negative circumstances. The purpose of the study is to consider and analyze a possible option for adapting the organizational and functional structure of a typical SC for more efficient functioning in the context of a CS. The research methods used in the article include content analysis, analysis, induction and hypothetical method. The article highlights the organization of information interaction between state structures during full-scale conflicts, the ways of resolving the SC in such conditions and the measures taken to respond effectively in a timely manner. Particular attention is paid to approaches to information counteraction in crisis management. The authors analyze the experience of countries around the world in establishing and operating SC and developing appropriate organizational and functional structures. Based on the results of the analysis, it is concluded that today there is a tendency to establish and operate crisis centers in countries around the world. However, it has been found that no publication pays sufficient attention to approaches to adaptation (improvement) of the structures of crisis centers to the emergence of the CS. The study is of great importance, since the proposed option of adapting the organizational and functional structure of a typical crisis center to the emergence of a CS opens up new opportunities for understanding effective methods of countering a CS and promotes the development of crisis management strategies, in particular in the context of hybrid confrontation and full-scale war.

Key internal drivers for an SME’s dynamic ambidextrous growth strategy: A case study of a Norwegian seafood group

This qualitative, explorative case study presents a small and medium-sized enterprise (SME) in Norway that successfully pursued a dynamic ambidextrous growth strategy. The study applies a micro-foundation perspective and focuses on identifying and describing key internal drivers behind the group’s ambidextrous strategy. The empirical findings underscore ambidextrous owner-managers' pivotal role in facilitating strategic realignment, structural adaptation, and knowledge management to enable the case group’s long-term growth. This study contributes to the strategy literature by proposing a framework for theorizing how the identified key internal drivers can be employed to form a dynamic ambidextrous growth strategy in SMEs.

Bamboo fiber-enhanced UHPC: Early hydration and microstructural/mesoscale analysis

This study investigates the impact of incorporating bamboo fibers, a natural and renewable material, into ultra-high performance concrete (UHPC) on its early hydration, mechanical properties, internal relative humidity, autogenous shrinkage, and microstructure. The results show that adding bamboo fibers significantly improves various UHPC properties. Bamboo fibers regulate hydration and prolong the hydration process through their water absorption and retention abilities. With a fiber content of 1.0 vol percent (vol%), the compressive strength increases by 10.2 %, reaching 97.1 MPa, and at 2.0 vol%, flexural strength and toughness increase by 37.9 % and 739.5 %, respectively. Additionally, bamboo fibers help maintain internal relative humidity above 98 % during the first 7 days of curing and reduce autogenous shrinkage by up to 55.9 %. The fibers also optimize the microstructure by minimizing voids, with a 1.0 % fiber content reducing the void ratio to 1.64 %. This reduces large voids and improves the material's density. Overall, bamboo fibers show great promise in enhancing UHPC's structural performance and environmental adaptability while being a sustainable and cost-effective material.

Assessing the behavior of highly damped multilayered structures with controllable patches using condensation models

This study presents the finite element modeling of an adaptive sandwich structure that can be configured to exhibit various behaviors depending on the arrangement of the temperature fields in its core, which is made of a shape memory polymer with outstanding damping capacity. The sandwich structure is divided into patches corresponding to homogeneous temperature fields. Each patch is modeled as an equivalent thin plate using a condensation model and the effective properties are determined by preserving the real part of bending, shear (transverse) and extension (quasi-longitudinal) wavenumbers. The damping loss factor is estimated independently using the forced response from an analytical discrete model of the sandwich structure and the Power Input Method. Two different configurations of the sandwich structure are presented: in the first, the modal behavior of the sandwich structure is slightly affected; In the second, vibrations are reduced throughout the frequency range. The use of a suitable condensation model can be particularly interesting for solving optimization problems and determining optimal configurations. The behavior of the sandwich structure can be evaluated with reduced computational cost whenever an update of the temperature fields is required.

Interplay between short and long time scales in adapting, periodically driven elastic flow networks

Existing theories of structural adaptation in biological flow networks are largely concerned with steady flows. However, biological networks are composed of elastic vessels, and many are driven by a pulsatile or periodic source, leading to spatiotemporal variations in the pressure and flow fields on short time-scales within each vessel. Here, we investigate the mathematical problem of how long-term adaptation in elastic networks is impacted by short-term pulsatile dynamics at the level of individual vessels. Using a a minimal one-loop network, we show that pulsatility gives rise to resonances that stabilize the loop for a much broader range of metabolic cost functions than predicted by existing theories. Our paper emphasizes the importance of correctly capturing the interplay of the short and long time-scales for a more realistic treatment of adaptation in periodically driven elastic flow networks. Published by the American Physical Society 2024

Novel image-analytic approach reveals new insights in fine-tuning of slime mould network adaptation.

This study introduces a novel methodology to explore the network dynamics of Physarum polycephalum, an organism celebrated for its remarkable adaptive capabilities. We used two innovative techniques to analyse its growth behaviour and network modifications under stress conditions, including starvation and differential epinephrine exposures. The first method provided a quantitative assessment of growth and exploration over time. The second method provided a detailed examination of vein diameter and contraction patterns, illuminating the physiological adjustments P. polycephalum undergoes in response to environmental challenges. By integrating these approaches, we were able to estimate the total network volume of the organism, with a focus on the normalized estimated volume, unveiling insightful aspects of its structural adaptations. While starvation reduced the volume, indicating a significant structural compromise, low and high epinephrine concentrations maintained a volume-to-area ratio comparable with the control. Determining the fractal dimension of the networks over time revealed a fine-tuning of the network complexity in response to environmental conditions, with significant reductions under stress indicating a constrained network adaptation strategy. These methods, novel in their application to P. polycephalum, provide a framework for future studies and a basis for exploring complex network behaviours with potential applications in bioengineering and adaptive network design.

Unsupervised Structure-Adaptive Graph Contrastive Learning.

Graph contrastive learning, which to date has always been guided by node features and fixed-intrinsic structures, has become a prominent technique for unsupervised graph representation learning through contrasting positive-negative counterparts. However, the fixed-intrinsic structure cannot represent the potential relationships beneficial for models, leading to suboptimal results. To this end, we propose a structure-adaptive graph contrastive learning framework to capture potential discriminative relationships. More specifically, a structure learning layer is first proposed for generating the adaptive structure with contrastive loss. Next, a denoising supervision mechanism is designed to perform supervised learning on the structure to promote structure learning, which introduces the pseudostructure through the clustering results and denoises the pseudostructure to provide more reliable supervised information. In this way, under the dual constraints of denoising supervision and contrastive learning, the optimal adaptive structure can be obtained to promote graph representation learning. Extensive experiments on several graph datasets demonstrate that our proposed method outperforms state-of-the-art approaches on various tasks.

Governance In Smart Cities: Identifying And Analyzing Key Indicators Of Governance Structures

Background: Smart cities are characterized by integrating Information and Communication Technology (ICT) to address urban challenges, involving human and social capital as well as smart governance. Although there is no consensus on their definition, smart cities are generally seen as innovative projects to foster urban development through technology. However, effective governance in these cities poses significant challenges, requiring a multifaceted approach that involves collaboration between public and private sectors and civil society. This study seeks to identify key indicators for constructing effective governance frameworks in smart cities. Materials and Methods: Through a qualitative analysis of 70 articles from the ScienceDirect portal (2020-2024), this study explores the criteria and indicators of governance in smart cities. The articles were reviewed and filtered using a protocol with nine guiding questions, resulting in the extraction of 311 terms, which were then grouped into 33 indicators across five themes. These themes - Governance and Participation, Policies and Regulations, Strategies and Urban Planning, Technology and Innovation, and Indicators and Assessments - form the foundation for effective governance structures in smart cities. Results: The results highlight that governance in smart cities must be dynamic and adaptable, incorporating citizen participation, transparent public policies, open data practices, and efficient use of ICT. The findings contribute to a more comprehensive understanding of how governance structures can foster sustainability, inclusivity, and efficiency in smart urban environments. By synthesizing key governance indicators, this research provides a framework to guide the development of governance models that can support the evolving needs of smart cities. Conclusion: Effective governance in smart cities requires dynamic, adaptable structures that foster collaboration between public and private sectors and civil society. This study provides a framework to promote sustainability, inclusivity, and efficiency in smart urban environments.

A similarity-aware ensemble method for displacement prediction of concrete dams based on temporal division and fully Bayesian learning

Precisely predicting concrete dam displacements is crucial for assessing their structural behavior during operation. Many studies have testified that ensemble methods are more accurate and applicable in practice than individual predictive models. Nevertheless, the common way handling massive monitoring data is still conventional, that is, training and testing them as a whole, neglecting the internal law and pattern difference within data, which probably limits advancements in predictive effect. To this end, the patterns of monitoring data are identified and classified before model establishment, and a similarity-aware ensemble method (SAEM) using temporal division and fully Bayesian learning is presented for dam displacement prediction. Specifically, the unsupervised fuzzy C-means approach and sparrow search algorithm are fused for similar pattern clustering of environmental factors, thus achieving temporal division in displacement responses. Fully considering the adaptability of model structure and parameters to various data patterns, a non-parametric fully Bayesian Gaussian process regression (FBGPR) model is proposed by augmenting the standard GPR with Markov chain Monte Carlo simulation and Bayesian evidence evaluation theory. Different data clusters are then fed into FBGPR in chronological order, and the final results are derived through a grouping ensemble scheme. Multiple sets of monitoring data collected from a real-world dam project are employed for method verification. The results show that our proposed SAEM has better prediction accuracy compared to homogeneous clustering-based ensemble methods and commonly used individual models. The superior performance in two additional cases also verifies the adaptability and generalization ability of our method, which provides a new modeling tool for structural health monitoring of concrete dams.

Athlete's ECG Made Easy: A Practical Guide to Surviving Everyday Clinical Practice.

Electrocardiogram modifications in athletes are common and usually reflect structural and electrical heart adaptations to regular physical training, known as the athlete's heart. However, these electrical modifications sometimes overlap with electrocardiogram findings that are characteristic of various heart diseases. A missed or incorrect diagnosis can significantly impact a young athlete's life and potentially have fatal consequences during exercise, such as sudden cardiac death, which is the leading cause of death in athletes. Therefore, it is crucial to correctly distinguish between expected exercise-related electrocardiogram changes in an athlete and several electrocardiogram abnormalities that may indicate underlying heart disease. This review aims to serve as a practical guide for cardiologists and sports clinicians, helping to define normal and physiology-induced electrocardiogram findings from those borderlines or pathological, and indicating when further investigations are necessary. Therefore, the possible athlete's electrocardiogram findings, including rhythm or myocardial adaptation, will be analyzed here, focusing mainly on the differentiation from pathological findings.

Green self-templated synthesis of P-doped mesoporous carbon from dual sodium salts with improved average pore size for capacitive deionization

Mesoporous carbon materials hold significant potential in capacitive deionization (CDI) technology owing to expansive accessible surface area, more adaptable pore structure, excellent conductivity properties and effective surface modification and functionalization. While templating is a common method for synthesizing mesoporous carbons, the commonly used hard and soft templating approaches among them suffer from the challenges of expensive precursors and templating agents, complex procedures, secondary contaminants and difficulties in template removal. In contrast, the self-templating method not only circumvents the traditional template removal or combustion steps but also eliminates the use of organic solvents and hazardous chemicals, thus minimizing environmental impact. In this paper, we have successfully synthesized phosphorus-doped mesoporous carbon with a large average pore size (paver) using a green and convenient self-templating method employing dual sodium salts of sodium alginate (SA) and sodium phytate (SP). Compared to single SA (6.76 nm) or SP (3.74 nm) self-templated carbonization, the average pore size of carbon materials obtained from dual self-templated carbonization with a proportional mixture (12.97 nm) is significantly improved. The larger average pore size provides a greater surface area for electrode-electrolyte interactions, allowing for easier access and faster diffusion of ions into the pores, thereby enhancing the electrochemical and capacitive deionization properties of the material. The resulting material demonstrates promising electrochemical and desalination performance as evidenced by possessing a specific capacitance of 175.8 A/g and an adsorption capacity of 19.65 mg/g, underlining its potential for application in capacitive deionization utilizing mesoporous carbon materials with large average pore size.

Understanding species limits through the formation of phylogeographic lineages.

The outcomes of speciation across organismal dimensions (e.g., ecological, genetic, phenotypic) are often assessed using phylogeographic methods. At one extreme, reproductively isolated lineages represent easily delimitable species differing in many or all dimensions, and at the other, geographically distinct genetic segments introgress across broad environmental gradients with limited phenotypic disparity. In the ambiguous gray zone of speciation, where lineages are genetically delimitable but still interacting ecologically, it is expected that these lineages represent species in the context of ontology and the evolutionary species concept when they are maintained over time with geographically well-defined hybrid zones, particularly at the intersection of distinct environments. As a result, genetic structure is correlated with environmental differences and not space alone, and a subset of genes fail to introgress across these zones as underlying genomic differences accumulate. We present a set of tests that synthesize species delimitation with the speciation process. We can thereby assess historical demographics and diversification processes while understanding how lineages are maintained through space and time by exploring spatial and genome clines, genotype-environment interactions, and genome scans for selected loci. Employing these tests in eight lineage-pairs of snakes in North America, we show that six pairs represent 12 "good" species and that two pairs represent local adaptation and regional population structure. The distinct species pairs all have the signature of divergence before or near the mid-Pleistocene, often with low migration, stable hybrid zones of varying size, and a subset of loci showing selection on alleles at the hybrid zone corresponding to transitions between distinct ecoregions. Locally adapted populations are younger, exhibit higher migration, and less ecological differentiation. Our results demonstrate that interacting lineages can be delimited using phylogeographic and population genetic methods that properly integrate spatial, temporal, and environmental data.

HSMAOA: An enhanced arithmetic optimization algorithm with an adaptive hierarchical structure for its solution analysis and application in optimization problems

The Arithmetic Optimization Algorithm (AOA) has recently gained significant attention as a novel meta-heuristic algorithm. However, it faces challenges such as premature convergence and entrapment in local optima when addressing complex optimization problems. To overcome these limitations, this paper proposes an enhanced AOA, termed the Self-Adaptive Hierarchical Arithmetic Optimization Algorithm (HSMAOA). The proposed method integrates three key strategies: Firstly, a spiral-guided random walk mechanism is introduced to improve global search ability. Secondly, a novel adaptive hierarchy leader and follower mechanism is proposed, which establishes a complete multi-branch tree hierarchy with decreasing branching degrees within the population, thereby increasing information exchange among population individuals to escape local optima. Finally, a differential mutation strategy based on ranked selection is introduced to enhance candidate solution quality. HSMAOA's performance was evaluated on the CEC2022 test suite against some state-of-the-art algorithms. Results, including optimization accuracy analysis, convergence curves, and various statistical tests, demonstrate HSMAOA's superior optimization capability and robustness. In addition, tests on eight engineering structure optimization problems, including the pressure vessel design problem, the multiple disk clutch brake design problem, and the step-cone pulley problem, and so forth, further validate its effectiveness. Thus, HSMAOA shows strong competitiveness in complex optimization tasks and potential for a wide range of applications, and is an advantageous and promising alternative solution for optimization problems.