Structural Complexity Research Articles

Assessment of Ichthyofaunal Diversity in Gavier Lake, Surat, India

Fish, with over 36,000 species globally, are the most diverse vertebrates, with nearly half residing in freshwater habitats. This study provides the first comprehensive documentation of the ichthyofauna of Gavier Lake, Surat, Gujarat, identifying 19 species across various families. The Cyprinidae family was the most represented, with five species, Siluridae with three and Channidae with two species. Wallago attu, a Near Threatened species according to the IUCN Red List, was also recorded. The lake’s diverse ecological features, including rich vegetation and bird habitats, provide vital resources for fish species, supporting an interdependent ecosystem. The structural complexity of submerged roots and aquatic plants enhances fish shelter, breeding, and foraging opportunities. Additionally, otters, apex predators in freshwater systems, indicate a healthy and abundant fish population. Other wildlife, such as snakes and butterflies, further highlights the lake’s ecological richness. This study underscores the importance of conserving Gavier Lake as a critical biodiversity hotspot and the need for ongoing conservation efforts to protect its unique aquatic ecosystem.

IRMAOC: an interpretable role mining algorithm based on overlapping clustering

The Industrial Internet motivates the research and development of Zero-Trust Architecture (ZTA). Role-Based Access Control (RBAC) as one of the key technologies for ZTA has become a hot topic. Role mining algorithms are crucial for RBAC and interpretable role mining receives wide attention due to its virtue of mining meaningful roles. However, the roles generated by existing algorithms have low interpretability and high time complexity, limiting their application in practice. This paper proposes an Interpretable Role Mining Algorithm Based on Overlapping Clustering (IRMAOC). It evaluates the interpretability of a role based on user similarity calculated on the permission and attribute of the role, and employs policy interpretability as the metric of a role set. IRMAOC creates a user association graph and clusters to generate candidate roles based on the graph. Then it remains the roles whose interpretability is higher than the preset threshold and re-clusters the users belonging to the other roles until the interpretability of all roles are higher than the threshold. Experimental results show that our algorithm significantly improves the interpretability of roles, reduces the Weighted Structure Complexity (WSC), and decreases time complexity compared to previous works.

Mechanical Properties of Polyethylene/Carbon Nanotube Composites from Coarse-Grained Simulations

Advanced nanocomposite membranes incorporate nanomaterials within a polymer matrix to augment the mechanical strength of the resultant product. Characterizing these membranes through molecular modeling necessitates specialized approaches to accurately capture the length scales, time scales, and structural complexities inherent in polymers. To address these requirements, an efficient simulation protocol is proposed, utilizing coarse-grained (CG) molecular dynamics simulations to examine the mechanical properties of polyethylene/single-walled carbon nanotube (PE/SWCNT) composites. This methodology integrates CG potentials derived from the statistical associating fluid theory (SAFT-γ Mie) equation of state and a modified Tersoff potential as a model for SWCNTs. A qualitative correspondence with benchmark classical all-atom models, as well as available experimental data, is observed, alongside enhanced computational efficiency. Employing this CG model, the focus is directed at exploring the mechanical properties of PE/SWCNT composites under both tensile and compressive loading conditions. The investigation covered the influence of SWCNT size, dispersion, and weight fraction. The findings indicate that although SWCNTs enhance the mechanical strength of PE, the extent of enhancement marginally depends on the dispersion, filler size, and weight fraction. Fracture strengths may be elevated by 20% with a minor incorporation of SWCNTs. Under compression, the incorporation of SWCNTs into the composites results in a transformation from brittle to tough materials. These insights contribute to the optimization of PE/SWCNT composites, emphasizing the importance of considering multiple factors to fine-tune the desired mechanical performance.

Advancing Room-Temperature Magnetic Semiconductors with Organic Radical Charge Transfer Cocrystals.

Developing purely organic room-temperature magnetic semiconductors has been a long-sought goal in the material community toward the simultaneous control of spin and charge. Organic cocrystals, known for their structural versatility and multifunctionality, are ideal candidates for these magnetoelectric coupling applications. However, organic room-temperature magnetic semiconductor cocrystals have rarely been reported, and their mechanisms remain poorly understood due to the complexity of cocrystal structures. Here, doping organic cocrystals with radicals offers a promising strategy for boosting their magnetism and conductivity while maintaining their cocrystal structures. The fluoranthene-7,7,8,8-tetracyanoquinodimethane radical (FA-HTCNQ•) is constructed through a simple, rapid, and eco-friendly solution-processing approach. The conductive FA-HTCNQ• exhibits excellent room-temperature ferromagnetism with the coercive fields of 96 Oe and the Curie temperature near 400 K, superior to its structural-identical undoped counterpart. Meanwhile, the room-temperature magnetoelectric coupling is demonstrated in the conductive FA-HTCNQ•. The stronger ferromagnetism and conductivity in organic cocrystals are attributed to the enhanced charge-transfer (CT) interactions induced by radicals, rather than the spin exchange interactions between these radicals alone. The research manifests the origin of ferromagnetism in organic cocrystals and provides a simple strategy to fabricate pure organic room-temperature magnetic semiconductor materials for future integrated magnetoelectric devices.

Silica Nanoparticle-Protein Aggregation and Protein Corona Formation Investigated with Scattering Techniques.

Protein-nanoparticle interactions and the resulting corona formation play crucial roles in the behavior and functionality of nanoparticles in biological environments. In this study, we present a comprehensive analysis of protein corona formation with superfolder green fluorescent protein (sfGFP) and bovine serum albumin in silica nanoparticle dispersions using small-angle X-ray scattering (SAXS) and dynamic light scattering (DLS). For the first time, we subtracted the scattering of individual proteins in solution and individual nanoparticles from the protein-nanoparticle complexes. This approach effectively isolated the contributions of specific components within the corona. Our form factor analysis revealed consistent core-shell sphere thicknesses but varied attractive interaction strengths of the nanoparticle complexes, influenced by the protein corona and the surface properties of silica and aminated silica nanoparticles. Interestingly, fractal analysis of nanoparticles showed a transition from surface to mass fractals for sfGFP samples at high protein:nanoparticle molar ratios of over 264,000:1. DLS analysis highlighted aggregation behaviors, including the increasing size of protein-nanoparticle complexes and significant aggregation of both free proteins and complexes at ∼264,000 molar ratio. Large polydispersity and heterogeneous protein aggregation were observed at these high molar ratios. Both SAXS and DLS revealed transitions and changes in protein-nanoparticle interactions at molar ratios of 4000 to 44,000, consistent with corona formation, while pronounced aggregation was observed at a molar ratio of ∼264,000. These findings advance our understanding of the structural complexities in protein-nanoparticle association and suggest further avenues for refining characterization techniques in protein corona research.

Intelligent Surveillance System for Real-Time Detection of Anomalous Activities in Video Streams

The increasing complexity of surveillance structures necessitates advanced techniques for monitoring big volumes of video statistics. This record opinions the utility of convolutional neural networks (CNN) and deep getting to know techniques for detecting suspicious pastime in video streams. The technique involves preliminary video statistics processing to extract key features, observed by training a CNN version to distinguish between normal and abnormal behaviours through recognizing spatial and temporal patterns within the video frames. techniques consisting of transfer gaining knowledge of and statistics augmentation are employed to enhance the model's generality and robustness. The effectiveness of this approach was validated via various checks, including experiments on datasets like u.s.a. Pedestrian and road, where the CNN-primarily based technique established high accuracy and go back quotes in figuring out suspicious activities. The scalability and actual-time processing abilities of the version make it adaptable to various tracking environments. These findings are great for the advancement of the surveillance era, offering a dependable technique for real-time detection of suspicious sports in video streams. The proposed CNN-based approach is promising for bolstering safety in public spaces, transportation structures, and important infrastructure, thus contributing to better safety features.

Residual Connection Networks in Medical Image Processing: Exploration of ResUnet++ Model Driven by Human Computer Interaction

Accurate identification and localisation of brain tumours from medical images remain challenging due to tumour variability and structural complexity. Convolutional Neural Networks (CNNs), particularly ResNet and Unet, have made significant progress in medical image processing, offering robust capabilities for image segmentation. However, limited research has explored their integration with human-computer interaction (HCI) to enhance usability, interpretability, and clinical applicability. This paper introduces ResUnet++, an advanced hybrid model combining ResNet and Unet++, designed to improve tumour detection and localisation while fostering seamless interaction between clinicians and medical imaging systems. ResUnet++ integrates residual blocks in both the downsampling and upsampling phases, ensuring critical image features are preserved. By incorporating HCI principles, the model provides intuitive, real-time feedback, enabling clinicians to visualise and interact with tumour localisation results effectively. This fosters informed decision-making and supports workflow efficiency in clinical settings. We evaluated ResUnet++ on the LGG Segmentation Dataset, achieving a Jaccard Loss of 98.17%. The results demonstrate its strong segmentation performance and potential for real-world applications. By bridging advanced medical imaging techniques with HCI, ResUnet++ offers a foundation for developing interactive diagnostic tools, improving clinician trust, decision accuracy, and patient outcomes, and advancing the integration of AI in healthcare workflows.

CDKD-w+: A Keyframe Recognition Method for Coronary Digital Subtraction Angiography Video Sequence Based on w+ Space Encoding

Currently, various deep learning methods can assist in medical diagnosis. Coronary Digital Subtraction Angiography (DSA) is a medical imaging technology used in cardiac interventional procedures. By employing X-ray sensors to visualize the coronary arteries, it generates two-dimensional images from any angle. However, due to the complexity of the coronary structures, the 2D images may sometimes lack sufficient information, necessitating the construction of a 3D model. Camera-level 3D modeling can be realized based on deep learning. Nevertheless, the beating of the heart results in varying degrees of arterial vasoconstriction and vasodilation, leading to substantial discrepancies between DSA sequences, which introduce errors in 3D modeling of the coronary arteries, resulting in the inability of the 3D model to reflect the coronary arteries. We propose a coronary DSA video sequence keyframe recognition method, CDKD-w+, based on w+ space encoding. The method utilizes a pSp encoder to encode the coronary DSA images, converting them into latent codes in the w+ space. Differential analysis of inter-frame latent codes is employed for heartbeat keyframe localization, aiding in coronary 3D modeling. Experimental results on a self-constructed coronary DSA heartbeat keyframe recognition dataset demonstrate an accuracy of 97%, outperforming traditional metrics such as L1, SSIM, and PSNR.

Pore Structure and Heterogeneity Characteristics of Deep Coal Reservoirs: A Case Study of the Daning–Jixian Block on the Southeastern Margin of the Ordos Basin

To clarify the micropore structure and fractal characteristics of the Danning–Jixian block on the eastern margin of the Ordos Basin, this study focuses on the deep coal rock of the Benxi Formation in that area. On the basis of an analysis of coal quality and physical properties, qualitative and quantitative studies of pore structures with different pore diameters were conducted via techniques such as field emission scanning electron microscopy (FE-SEM), low-pressure CO2 adsorption (LP-CO2A), low-temperature N2 adsorption (LT-N2A), and high-pressure mercury intrusion (HPMI). By applying fractal theory and integrating the results from the LP-CO2A, LT-N2A, and HPMI experiments, the fractal dimensions of pores with different diameters were obtained to characterize the complexity and heterogeneity of the pore structures of the coal samples. The results indicate that the deep coal reservoirs in the Danning–Jixian block have abundant nanometer-scale organic matter gas pores, tissue pores, and a small number of intergranular pores, showing strong heterogeneity influenced by the microscopic components and forms of distribution of organic matter. The pore structure of the Benxi Formation exhibits significant cross-scale effects and strong heterogeneity and is predominantly composed of micropores that account for more than 90% of the total pore volume; the pore structure is affected mainly by the degree of coalification, the vitrinite group, and the ash yield. Fractal analysis reveals that the heterogeneity of macropores is greater than that of mesopores and micropores. This may be attributed to the smaller pore sizes and concentrated distributions of micropores, which are less influenced by diagenesis, resulting in simpler pore structures with lower fractal dimensions. In contrast, mesopores and macropores, with larger diameters and broader distributions, exhibit diverse origins and are more affected by diagenesis, reflecting strong heterogeneity. The abundant storage space and strong self-similarity of micropores in deep coal facilitate the occurrence, flow, and extraction of deep coalbed methane.

Identification of Oligosaccharide Isomers Using Electrostatically Asymmetric OmpF Nanopore.

Glycans, unlike uniformly charged DNA and compositionally diverse peptides, are typically uncharged and exhibit rich stereoisomeric diversity in the glycosidic bonds between two monosaccharide units. This heterogeneity of charge and the structural complexity present significant challenges for accurate analysis. Herein, we developed a novel single-molecule oligosaccharide sensor, OmpF nanopore. The natural electroosmotic flow within OmpF provides robust driving force for unlabeled neutral oligosaccharides, accomplishing a detection concentration as low as 6.4 μM. Furthermore, the asymmetric constriction zone of OmpF was employed to construct a stereoselective recognition site, enabling sensitive identification of glycosidic bond differences in cell lysate samples. Assisted by machine learning algorithms, a recognition accuracy of 99.9% for tetrasaccharides differing in only one glycosidic bond was achieved. This nanopore sensor offers a highly sensitive analytical tool with a broad dynamic range for the chiral recognition of oligosaccharides at low concentrations, applicable to both low-abundance and practical samples.

Identification of Oligosaccharide Isomers Using Electrostatically Asymmetric OmpF Nanopore

Glycans, unlike uniformly charged DNA and compositionally diverse peptides, are typically uncharged and exhibit rich stereoisomeric diversity in the glycosidic bonds between two monosaccharide units. This heterogeneity of charge and the structural complexity present significant challenges for accurate analysis. Herein, we developed a novel single‐molecule oligosaccharide sensor, OmpF nanopore. The natural electroosmotic flow within OmpF provides robust driving force for unlabeled neutral oligosaccharides, accomplishing a detection concentration as low as 6.4 μM. Furthermore, the asymmetric constriction zone of OmpF was employed to construct a stereoselective recognition site, enabling sensitive identification of glycosidic bond differences in cell lysate samples. Assisted by machine learning algorithms, a recognition accuracy of 99.9% for tetrasaccharides differing in only one glycosidic bond was achieved. This nanopore sensor offers a highly sensitive analytical tool with a broad dynamic range for the chiral recognition of oligosaccharides at low concentrations, applicable to both low‐abundance and practical samples.

A review of pharmacological mechanisms, challenges and prospects of macromolecular glycopeptides.

Macromolecular glycopeptides are natural products derived from various sources, distinguished by their structural diversity, multifaceted biological activities, and low toxicity. These compounds exhibit a wide range of biological functions, such as immunomodulation, antitumor effects, anti-inflammatory properties, antioxidant activity, and more. However, limited understanding of natural glycopeptides has hindered their development and practical application. To promote their advancement and utilization, it is crucial to thoroughly investigate the pharmacological mechanisms of glycopeptides and address the challenges in natural glycopeptide research. This review uniquely focuses on the primary biological activities and potential molecular mechanisms of glycopeptides as reported in recent literature. Moreover, we emphasize the current challenges in glycopeptide research, including extraction and isolation difficulties, purification challenges, structural analysis complexities, elucidation of structure-activity relationships, characterization of biosynthetic pathways, and ensuring bioavailability and stability. The future prospects for glycopeptide research are also explored. We argue that ongoing research into glycopeptides will significantly contribute to drug development and provide more effective therapeutic options and disease treatment alternatives for human health.

Palladium-Catalyzed Oxidative Allene-Allene Cross-Coupling.

Direct cross-coupling reactions between two similar unactivated partners are challenging but constitute a powerful strategy for the creation of new carbon-carbon bonds in organic synthesis. [4]Dendralenes are a class of acyclic branched conjugated oligoenes with great synthetic potential for the rapid generation of structural complexity, yet the chemistry of [4]dendralenes remains an unexplored field due to their limited accessibility. Herein, we report a highly selective palladium-catalyzed oxidative cross-coupling of two allenes with the presence of a directing olefin in one of the allenes, enabling the facile synthesis of a broad range of functionalized [4]dendralenes in a convergent modular manner. Specifically, the selective allenic C-H activation of an allene with an allyl substituent as the assisting group gives rise to a vinylpalladium intermediate, which reacts with a less substituted allene in a carbopalladation, followed by a β-hydride elimination. The reaction sequence leads to a new C(sp2)-C(sp2) bond between two diene units. Remarkably, this protocol provides an unconventional strategy for the site-selective and stereoselective construction of C(vinyl)-C(vinyl) bonds without using any halogenated and organometallics olefin precursors. Furthermore, the practical transformations of the synthesized [4]dendralenes and late-stage modifications of biorelevant molecules demonstrate their potential in the total synthesis of natural products and drug discovery.

Quantitative Analysis of Morphology-Meaning Synergy in Chinese: Implications for Morphological Modeling and Genre Classification

The synergetic relationship between morphology and meaning in Chinese remains underexplored. Based on the Lancaster Corpus of Mandarin Chinese, we quantitatively analyze the structure of Chinese words by adopting the method of information theory, exploring the interaction between the structural complexity of words (SCW) and polysemy. We also assessed the model parameters’ effectiveness in distinguishing different genres and the textual characteristics reflected by these parameters. The results indicate that, first, an inverse correlation exists between SCW and polysemy (but the increase of one will not cause the infinite reduction of the other), and the model [Formula: see text] effectively captures this relationship. Second, parameters [Formula: see text] and [Formula: see text] reveal differences in lexical forms between ”narrative” and ”expository” texts, with higher values of [Formula: see text] and [Formula: see text] suggesting a tendency toward expository functions; parameter [Formula: see text] reflects distinctions in meaning clarity between the two types of texts, with higher values of [Formula: see text] suggesting a tendency toward narrative functions. Beyond elucidating the interaction mechanism between lexical forms and meanings, our study provides insights into the automatic recognition and classification of texts, as well as into the quantification of lexical morphology and the development of morphological models in other analytic languages.

De novo assembly of the complete mitochondrial genomes of two Camellia-oil tree species reveals their multibranch conformation and evolutionary relationships

Camellia-oil trees are economically valuable, oil-rich species within the genus Camellia, family Theaceae. Among these species, C. oleifera, a member of Section Oleifera in the genus, is the most extensively cultivated in China. In this study, we assembled the mitochondrial genomes (mitogenomes) of two Camellia species, namely C. oleifera and C. lanceoleosa. These two species are closely related and belong to the same genus and section, with C. oleifera being hexaploid and C. lanceoleosa being diploid. The mitogenome of C. oleifera is comprised of 1,039,838 base pairs (bp), and C. lanceoleosa is comprised of 934,155 bp. Both genomes exhibit a multipartite genome structure, which is supported by our PCR experiments. We conducted codon usage and RNA editing site analysis on these two mitogenomes, which showed highly consistent results. However, analysis of repetitive sequences and mitochondrial plastid sequences (MTPTs) revealed differences between the two mitogenomes. Phylogenetic analysis indicated that these two species clustered together, suggesting a close evolutionary relationship. The collinearity analysis results showed extensive genome rearrangements in the mitogenomes of Camellia species. We successfully assembled the mitogenomes of C. oleifera and C. lanceoleosa, marking a significant advancement in understanding camellia-oil tree mitogenomes. Unlike circular mitogenomes reported before, our research confirms multiple-branched configurations in these two species. This sheds light on mitogenome structural complexities and contributes to our understanding of evolutionary processes. Additionally, these results enrich Camellia genetic resources and expand our knowledge of mitogenome variation.

Multiscale structural complexity analysis of neuronal activity in the suprachiasmatic nucleus: insights from tetrodotoxin-induced disruptions

Abstract The suprachiasmatic nucleus in the hypothalamus is the master circadian clock in mammals, coordinating physiological processes with the 24-hour day-night cycle. Comprising various cell types, the SCN integrates environmental signals to maintain complex and robust circadian rhythms. Understanding the complexity and synchrony within SCN neurons is essential for effective circadian clock function. Synchrony involves coordinated neuronal firing for robust rhythms, while complexity reflects diverse activity patterns and interactions, indicating adaptability. Interestingly, the SCN retains circadian rhythms in vitro, demonstrating intrinsic rhythmicity. This study introduces the multiscale structural complexity method to analyze changes in SCN neuronal activity and complexity at macro and micro levels, based on Bagrov et al.’s approach. By examining structural complexity and local complexities across scales, we aim to understand how tetrodotoxin, a neurotoxin that inhibits action potentials, affects SCN neurons. Our method captures critical scales in neuronal interactions that traditional methods may overlook. Validation with the Goodwin model confirms the reliability of our observations. By integrating experimental data with theoretical models, this study provides new insights into the effects of TTX on neuronal complexities, contributing to the understanding of circadian rhythms.

Design of On-Site Calibration Device for Electricity Meter Based on Pulse Detection

At present, the error calibration of electricity meters in operation generally adopts an off-site method; that is, the electricity meter is taken out of operation and then calibrated in the laboratory. Off-site calibration, while beneficial, may not fully capture the operational error of the electricity meter due to potential differences in environmental conditions. An on-site calibration device for electricity meters based on pulse detection is designed, which obtains the error of the electricity meter under calibration by comparing the energy pulses of the standard electricity meter with those of the electricity meter under calibration. High-precision voltage and current sampling channels are designed, with a voltage measurement error of less than 0.02% and a current measurement error of less than 0.03%. In response to the non-synchronous sampling problem caused by frequency fluctuations in the on-site verification environment, a fast optimal frequency estimation algorithm is applied to accurately calculate the signal frequency within two cycles. The sampling time interval is adjusted to achieve lock-frequency synchronous sampling, and ensure the accurate calculation of electrical parameters. In order to reduce the complexity of the device circuit structure and equipment cost, a standard electric energy pulses generation method based on digital integration-to-frequency is proposed, which uses software to generate electric energy pulses, with a maximum output frequency of up to 10 kHz. Tests conducted in the laboratory on the developed on-site calibration device for electricity meters show that its accuracy is better than the 0.05 accuracy class, meeting the application requirements for on-site verification of electricity energy meters.

Local characterization of collagen architecture and mechanical properties of tissue engineered atherosclerotic plaque cap analogs.

Many cardiovascular events are triggered by fibrous cap rupture of an atherosclerotic plaque in arteries. However, cap rupture, including the impact of the cap's structural components, is poorly understood. To obtain better mechanistic insights in a biologically and mechanically controlled environment, we previously developed a tissue-engineered fibrous cap model. In the current study, we characterized the (local) structural and mechanical properties of these tissue-engineered cap analogs. Twenty-four collagenous cap analogs were cultured. The analogs were imaged with multiphoton microscopy with second-harmonic generation to obtain local collagen fiber orientation and dispersion. Then, the analogs were mechanically tested under uniaxial tensile loading until failure, and the local deformation (strain) and failure characteristics were analyzed. Our results demonstrated that the tissue-engineered analogs mimic the dominant (circumferential) fiber direction of human plaques. The analogs also exhibited a physiological strain stiffening response, similar to human fibrous plaque caps. Ruptures in the analogs initiated in and propagated towards local high-strain regions. The local strain values at the rupture sites were similar to the ones reported for carotid human fibrous plaque tissue. Finally, the study revealed that the rupture propagation path in the analogs followed the local fiber direction. STATEMENT OF SIGNIFICANCE: Many cardiovascular events are triggered by mechanical rupture of atherosclerotic plaque caps. Yet, cap rupture mechanics is poorly understood. This is mainly due to the scarcity of plaques for high-throughput testing and the structural complexity of plaques. To overcome this, we previously developed tissue-engineered cap analogs. The current study characterizes (local) structural and mechanical properties of these cap analogs. Our findings show that: (1) cap analogs closely mimic human fibrous caps, including fiber orientation and strain stiffening responses; (2) structural and mechanical properties of cap analogs are associated, which provides critical information for understanding plaque rupture; and (3) cap ruptures commonly start in and propagate towards high-strain areas, indicating the potential use of strain measurements for cap rupture risk assessment.

Identifying fracture-controlled resonance modes for structural health monitoring: insights from Hunter Canyon Arch (Utah, USA)

Abstract. Progressive fracturing contributes to structural degradation of natural rock arches and other freestanding rock landforms. However, methods to detect structural changes arising from fracturing are limited, particularly at sites with difficult access and high cultural value, where non-invasive approaches are essential. This study aims to determine how fractures affect the dynamic properties of rock arches, focusing on resonance modes as indicators of structural health conditions. We hypothesize that damage resulting from fracture propagation may influence specific resonance modes that can be identified through ambient vibration modal analysis. We characterized the dynamic properties (i.e., resonance frequencies, damping ratios, and mode shapes) of Hunter Canyon Arch, Utah (USA), using spectral and cross-correlation analyses of data generated from an array of nodal geophones. Results revealed properties of nine resonance modes with frequencies between 1 and 12 Hz. Experimental data were then compared to numerical models with homogeneous and heterogeneous compositions, the latter implementing weak mechanical zones in areas of mapped fractures. All numerical solutions replicated the first two resonance modes of the arch, indicating these modes are insensitive to structural complexity derived from fractures. Meanwhile, heterogenous models with discrete fracture zones succeeded in matching the frequency and shape of one additional higher mode, indicating this mode is sensitive to the presence of fractures and thus most likely to respond to structural change from fracture propagation. An evolutionary crack damage model was then applied to simulate fracture propagation, confirming that only this higher mode is sensitive to structural damage resulting from fracture growth. While examination of fundamental modes is common practice in structural health monitoring studies, our results suggest that analysis of higher-order resonance modes can be more informative for characterizing fracture-driven structural damage.

Pb9O4(BO3)2(NO3)4: A Lead Borate-Nitrate Containing an Anion-Centered [O8Pb18]∞ Chain with Large Optical Anisotropy.

In the field of structural chemistry, crystals containing the [OM4] tetrahedra (M = metal cation) structure have been well documented. However, compounds containing both [OPb4] tetrahedra and π-conjugated groups are less reported due to their structural complexity. In this work, a new lead borate-nitrate, Pb9O4(BO3)2(NO3)4, has been synthesized by a high temperature melt method. Notably, the structure of Pb9O4(BO3)2(NO3)4 contains special [O8Pb18]∞ chains formed by the [OPb4] tetrahedra. This compound possesses a large optical anisotropy with birefringence of Δn = 0.117 at 546 nm.