Intricate Structures Research Articles

Enhancing Communication in CPS Using Graph-Based Reply Relationship Identification in Multi-Party Conversations

To enhance communication and collaborative work efficiency in cyber–physical systems (CPSs) within the Industry 4.0 environment, this study investigates a graph-based machine learning approach aimed at optimizing information interaction during multi-party conversations. Devices within CPSs must efficiently exchange information in real time to synchronize operations and responses. This research treats these interactions as intricate graph structures and uses graph learning techniques to accurately identify communication links and dependencies among devices. This improvement leads to more accurate decision-making and smoother operations. Our methodology involves a real-time analysis of structural patterns and node attributes within conversations, improving information flow and comprehension. The empirical findings demonstrate that this approach significantly enhances production efficiency, system adaptability, and minimizes delays attributed to communication misunderstandings. Our method can effectively identify the communication relationships between devices, significantly improving the efficiency and accuracy of information transmission. This improved communication capability leads to an enhanced production efficiency of the entire system.

A bioinspired surface tension-driven route toward programmed cellular ceramics

The intriguing biomineralization process in nature endows the mineralized biological materials with intricate microarchitected structures in a facile and orderly way, which provides an inspiration for processing ceramics. Here, we propose a simple and efficient manufacturing process to fabricate cellular ceramics in programmed cell-based 3D configurations, inspired by the biomineralization process of the diatom frustule. Our approach separates the ingredient synthesis from architecture building, enabling the programmable manufacturing of cellular ceramics with various cell sizes, geometries, densities, metastructures, and constituent elements. Our approach exploits surface tension to capture precursor solutions in the architected cellular lattices, allowing us to control the liquid geometry and manufacture cellular ceramics with high precision. We investigate the geometry parameters for the architected lattices assembled by unit cells and unit columns, both theoretically and experimentally, to guide the 3D fluid interface creation in arranged configurations. We manufacture a series of globally cellular and locally compact piezoceramics, obtaining an enhanced piezoelectric constant and a designed piezoelectric anisotropy. This bioinspired, surface tension-assisted approach has the potential to revolutionize the design and processing of multifarious ceramic materials for structural and functional applications in energy, electronics and biomedicine.

An Antioxidant, Reusable Laser Printed and Recycled Cork Paper Packaging

AbstractIn the realm of food waste recycling, industrial suberoligneous by‐products like cork and peels, pose a challenge for value‐added conversion, primarily due to their intricate structures. This study introduces an innovative approach by utilizing recycled cork as a prime example of repurposing food waste to create a reusable and durable packaging paper. Notably, the manufacturing process of this cork‐based paper avoids the generation of waste liquids from lignin removal, effectively alleviating concerns related to liquid waste pollution. The resulting packaging paper demonstrates remarkable printability, readability, and erasability while offering reusability. Its performance can be fine‐tuned through pH value adjustments. Moreover, this packaging paper exhibits outstanding antioxidative properties and barrier properties, seamlessly integrating superb UV‐blocking proficiency and heightened visible transparency, rendering it highly appealing for sustainable food packaging applications.

Identification of multicomponent LOFAR sources with multimodal deep learning

ABSTRACT Modern high-sensitivity radio telescopes are discovering an increased number of resolved sources with intricate radio structures and fainter radio emissions. These sources often present a challenge because source detectors might identify them as separate radio sources rather than components belonging to the same physically connected radio source. Currently, there are no reliable automatic methods to determine which radio components are single radio sources or part of multicomponent sources. We propose a deep-learning classifier to identify those sources that are part of a multicomponent system and require component association on data from the LOFAR Two-Metre Sky Survey. We combine different types of input data using multimodal deep learning to extract spatial and local information about the radio source components: a convolutional neural network component that processes radio images is combined with a neural network component that uses parameters measured from the radio sources and their nearest neighbours. Our model retrieves 94 per cent of the sources with multiple components on a balanced test set with 2683 sources and achieves almost 97 per cent accuracy in the real imbalanced data (323 103 sources). The approach holds potential for integration into pipelines for automatic radio component association and cross-identification. Our work demonstrates how deep learning can be used to integrate different types of data and create an effective solution for managing modern radio surveys.

Exploring the potential of supramolecular hydrogels as advanced bioinks for bioprinting and biomedical applications

Supramolecular hydrogels have emerged as versatile bioinks in tissue engineering, providing a promising avenue for constructing intricate and functional biological structures. This paper explores the significance of employing supramolecular hydrogels as advanced bioinks for three-dimensional bioprinting and various biomedical applications. Supramolecular hydrogels possess distinct and tunable characteristics attributed to the dynamic nature of supramolecular host–guest interactions alongside interactions based on DNA and peptides, which increases their significance in tissue engineering. These interactions are essential for enhancing the mechanical properties, injectability, printability, post-printing stability, and biocompatibility of hydrogels. Gelation kinetics and rheological properties can also be manipulated to suit specific printing techniques. Furthermore, these supramolecular interactions facilitate the incorporation of bioactive molecules to regulate cellular behavior and tissue development. These diverse interactions observed in supramolecular hydrogels underscore their ability to emulate the dynamic and responsive nature of the cell’s extracellular matrix, which fosters cell growth, adherence, and differentiation. This review specifically highlights the cucurbit[n]uril and cyclodextrin-based host–guest supramolecular hydrogels, as well as peptide and DNA-based supramolecular structures as advanced bioinks and brief examples of their applications in various biomedical fields. These advanced bioinks would drive the development of intricate tissue constructs with enhanced biomimicry and therapeutic potential in regenerative medicine.

Social-STGMLP: A Social Spatio-Temporal Graph Multi-Layer Perceptron for Pedestrian Trajectory Prediction

As autonomous driving technology advances, the imperative of ensuring pedestrian traffic safety becomes increasingly prominent within the design framework of autonomous driving systems. Pedestrian trajectory prediction stands out as a pivotal technology aiming to address this challenge by striving to precisely forecast pedestrians’ future trajectories, thereby enabling autonomous driving systems to execute timely and accurate decisions. However, the prevailing state-of-the-art models often rely on intricate structures and a substantial number of parameters, posing challenges in meeting the imperative demand for lightweight models within autonomous driving systems. To address these challenges, we introduce Social Spatio-Temporal Graph Multi-Layer Perceptron (Social-STGMLP), a novel approach that utilizes solely fully connected layers and layer normalization. Social-STGMLP operates by abstracting pedestrian trajectories into a spatio-temporal graph, facilitating the modeling of both the spatial social interaction among pedestrians and the temporal motion tendency inherent to pedestrians themselves. Our evaluation of Social-STGMLP reveals its superiority over the reference method, as evidenced by experimental results indicating reductions of 5% in average displacement error (ADE) and 17% in final displacement error (FDE).

Application of Photogrammetric Computer Vision and Deep Learning in High-Resolution Underwater Mapping: A Case Study of Shallow-Water Coral Reefs

Abstract. Underwater mapping is vital for engineering applications and scientific research in ocean environments, with coral reefs being a primary focus. Unlike more uniform and predictable terrestrial environments, coral reefs present a unique challenge for 3D reconstruction due to their intricate and irregular structures. Traditional 3D reconstruction methods struggle to accurately capture the nuances of coral reefs. This is primarily because coral reefs exhibit a high degree of spatial heterogeneity, featuring diverse shapes, sizes, and textures. Additionally, the dynamic nature of underwater conditions, such as varying light, water clarity, and movement, further complicates the accurate geometrical estimation of these ecosystems. With the rapid advancement of photogrammetric computer vision and deep learning technologies, there are emerging methods that have potential to enhance the quality of its 3D reconstruction. In this context, this study formulates a coral reef reconstruction workflow that incorporates these cutting-edge technologies. This workflow is divided into two core stages: sparse reconstruction and dense reconstruction. We conduct individual summaries of the relevant research efforts in these stages and outline the available methods. To assess the specific capabilities of these methods, we apply them to real-world coral reef images and conduct a comprehensive evaluation. Additionally, we analyze the strengths and weaknesses of different methods and identify areas for improvement. We believe this study offers valuable references for future research in underwater mapping.

Exploration of shock–droplet interaction based on high-fidelity simulation and improved theoretical model

Shock–droplet interaction in the early stage involves intricate wave structures. Investigating this phenomenon is inherently challenging due to the fine spatial and temporal scales involved. Past research has suggested that the occurrence of cavitation, marked by a negative peak pressure, is linked to the focus of the reflected expansion wave. In this study, a high-fidelity compressible numerical approach is utilized to replicate the initial phase of shock–droplet interactions. The location of the negative peak pressure is meticulously documented and compared with experimental measurement and numerical results. Results indicate a strong alignment between the negative peak pressure positions identified through numerical simulations and the focal points identified in theoretical models for low gas–liquid wave velocity ratios. However, this alignment is notably disrupted when dealing with higher gas–liquid wave velocity ratios. Further enhancements are made to the theoretical model, enabling a more precise depiction of internal wave structures and focus points, particularly under conditions of high gas–liquid wave velocity ratios. The study delves into the various factors influencing internal pressure fluctuations within the liquid droplet, categorizing them into four phases: the shock wave effect, relaxation effect, fluctuation effect, and expansion wave effect. Analysing the pressure decrease portion reveals that while the converging of the reflected expansion wave leads to a substantial pressure drop, it accounts for only a fraction of the total pressure variation. Consequently, any model predicting negative peak pressure positions must comprehensively consider all contributing factors.

Topology Optimization of Self-supporting Structures for Additive Manufacturing via Implicit B-spline Representations

Owing to the rapid development in additive manufacturing, the potential to fabricate intricate structures has become a reality, emphasizing the importance of designing structures conducive to additive manufacturing processes. A crucial consideration is the ability to design structures requiring no additional support during manufacturing. This paper employs implicit B-spline representations for self-supporting structure design by integrating a topology optimization model with self-supporting constraints derived analytically from the implicit representation. This analytical derivation for detecting overhang regions enables accurate and efficient calculation of constraints, outperforming other B-spline-based methods. Compared to the traditional voxel-based methods, the implicit B-spline representation significantly expedites the optimization process by reducing the number of design variables. Additionally, several acceleration techniques are implemented to enhance the efficiency of our method, allowing simulations of 3D models with millions of finite elements to be completed within one and half an hour, excelling other B-spline-based methods and voxel-based methods. Various numerical experiments validate its excellent performance, confirming the effectiveness and efficiency of the proposed algorithm.

Electrowriting of SU-8 Microfibers.

As microfiber-based additive manufacturing (AM) technologies, melt electrowriting (MEW) and solution electrowriting (SEW) have demonstrated efficacy with more biomedically relevant materials. By processing SU-8 resin using MEW and SEW techniques, a material with substantially different mechanical, thermal, and optical properties than that typically processed is introduced. SU-8 polymer is temperature sensitive and requires the devising of a specific heating protocol to be properly processed. Smooth-surfaced microfibers resulted from MEW of SU8 for a short period (from 30 to 90 min), which provides the greatest control and, thus, reproducibility of the printed microfibers. This investigation explores various parameters influencing the electrowriting process, printing conditions, and post-processing to optimize the fabrication of intricate 3D structures. This work demonstrates the controlled generation of straight filaments and complex multi-layered architectures, which were characterized by brightfield, darkfield, and scanning electron microscopy (SEM). This research opens new avenues for the design and development of 3D-printed photonic systems by leveraging the properties of SU-8 after both MEW and SEW processing.

Broadband Sound Absorption and High Damage Resistance in a Turtle Shell-Inspired Multifunctional Lattice: Neural Network-Driven Design and Optimization.

Incorporating acoustic and mechanical properties into a single multifunctional structure has attracted considerable attention in engineering. However, effectively integrating these sound absorption properties and damage resistance to achieve multifunctional structural designs remains a great challenge due to imperfect design methods. In this study, the inherent mechanical properties of turtle shells by introducing dissipative pores are leveraged to present a lattice structure that possesses both excellent sound-absorbing and high damage-resistant characteristics. To achieve acoustic optimization design, a universal high-fidelity neural network correction model is proposed to address the impedance calculation challenge in complex structures. Building upon this foundation, a multi-cell combination design enables to achieve high absorption through optimization with a low thickness of 50mm, resulting in average sound absorption coefficients reaching 0.88 and 0.93 within the frequency ranges of 300-600Hz and 500-1000Hz, respectively. It is also found that the optimized structures exhibit exceptional damage resistance under varying relative densities via the coupling effect of the shell thickness on the acoustic and mechanical properties. Overall, this work introduces a novel paradigm for designing intricate multifunctional structures with acoustic and mechanical properties while providing valuable inspiration for future research on multifunctional structure design.

Spectrin condensates provide a nidus for assembling the periodic axonal structure.

Coordinated assembly of individual components into higher-order structures is a defining theme in biology, but underlying principles are not well-understood. In neurons, α/β spectrins, adducin, and actinfilaments assemble into a lattice wrapping underneath the axonal plasma membrane, but mechanistic events leading to this periodic axonal structure (PAS) are unclear. Visualizing PAS components in axons as they develop, we found focal patches in distal axons containing spectrins and adducin (but sparse actin filaments) with biophysical properties reminiscent of biomolecular condensation. Overexpressing spectrin-repeats - constituents of α/β-spectrins - in heterologous cells triggered condensate formation, and preventing association of βII-spectrin with actin-filaments/membranes also facilitated condensation. Finally, overexpressing condensate-triggering spectrin repeats in neurons before PAS establishment disrupted the lattice, presumably by competing with innate assembly, supporting a functional role for biomolecular condensation. We propose a condensation-assembly model where PAS components form focal phase-separated condensates that eventually unfurl into a stable lattice-structure by associating with subplasmalemmal actin. By providing local 'depots' of assembly parts, biomolecular condensation may play a wider role in the construction of intricate cytoskeletal structures.

Accurate and efficient integrative reference-informed spatial domain detection for spatial transcriptomics.

Spatially resolved transcriptomics (SRT) studies are becoming increasingly common and large, offering unprecedented opportunities in mapping complex tissue structures and functions. Here we present integrative and reference-informed tissue segmentation (IRIS), a computational method designed to characterize tissue spatial organization in SRT studies through accurately and efficiently detecting spatial domains. IRIS uniquely leverages single-cell RNA sequencing data for reference-informed detection of biologically interpretable spatial domains, integrating multiple SRT slices while explicitly considering correlations both within and across slices. We demonstrate the advantages of IRIS through in-depth analysis of six SRT datasets encompassing diverse technologies, tissues, species and resolutions. In these applications, IRIS achieves substantial accuracy gains (39-1,083%) and speed improvements (4.6-666.0) in moderate-sized datasets, while representing the only method applicable for large datasets including Stereo-seq and 10x Xenium. As a result, IRIS reveals intricate brain structures, uncovers tumor microenvironment heterogeneity and detects structural changes in diabetes-affected testis, all with exceptional speed and accuracy.

Role of inflammatory cytokine burst in neuro-invasion of Japanese Encephalitis virus infection: an immunotherapeutic approaches.

Japanese Encephalitis remains a significant global health concern, contributing to millions of deaths annually worldwide. Microglial cells, as key innate immune cells within the central nervous system (CNS), exhibit intricate cellular structures and possess molecular phenotypic plasticity, playing pivotal roles in immune responses during CNS viral infections. Particularly under viral inflammatory conditions, microglial cells orchestrate innate and adaptive immune responses to mitigate viral invasion and dampen inflammatory reactions. This review article comprehensively summarizes the pathophysiology of viral invasion into the CNS and the cellular interactions involved, elucidating the roles of various immune mediators, including pro-inflammatory cytokines, in neuroinflammation. Leveraging this knowledge, strategies for modulating inflammatory responses and attenuating hyperactivation of glial cells to mitigate viral replication within the brain are discussed. Furthermore, current chemotherapeutic and antiviral drugs are examined, elucidating their mechanisms of action against viral replication. This review aims to provide insights into therapeutic interventions for Japanese Encephalitis and related viral infections, ultimately contributing to improved outcomes for affected individuals.

Preparation of high-density green body based on binder jetting 3D printing using spheroidized SiC powder

Silicon carbide (SiC) ceramic stands as a critical material in the realm of high-performance engineering ceramics. However, Crafting SiC ceramics with intricate structures and superior mechanical properties presents notable challenges in shaping, sintering, and processing. Binder jetting (BJ) additive manufacturing has the outstanding advantages of high forming efficiency and no thermal deformation, especially suitable for printing complex structure SiC components.; however, BJ printed green bodies often exhibit low density, resulting in high residual silicon content and diminished strength of the final components. In this work, we introduce a novel strategy to fabricate high-strength SiC components with complex geometries by improving the density of printed SiC green bodies. This method involves modifying the morphology of SiC powder through the molten salt method to increase the density of BJ 3D printed green bodies from 1.24 ± 0.13 g/cm³ to 2.01 ± 0.03 g/cm³, followed by a reactive melt infiltration process. The resulting RB-SiC ceramics display exceptional flexural strength, averaging 280.60 ± 33.05 MPa, and an elastic modulus of 294.67 ± 4.90 GPa. These values represent some of the highest for flexural strength and elastic modulus reported among 3D-printed SiC composites. With its robust mechanical performance and streamlined fabrication process, this strategy holds substantial promise for the production of structural SiC ceramics.

Temporomandibular Joint Histology

Background: The temporomandibular joint plays a crucial role in daily oral functions like mastication. A comprehensive understanding of the histological components of these joints proves valuable in deciphering their intricate anatomical structures. Purpose of the study: This study aimed to compare the histochemical stains of hematoxylin-eosin and Goldner's trichrome applied to a temporomandibular joint sample using the Wistar Rat as a biomodel. Results: Complete sagittal slices of murine biomodel hemicranium were evaluated and the comprehensive histological technique involved various steps, including perfusion, post-fixation, decalcification, dehydration, clarification, inclusion, cutting, staining, and assembly. The temporomandibular joint specimens were stained using H&E and Goldner's trichrome. The histological description included structures such as the external auditory canal, mandibular fossa and condyle, pterygoid muscles, joint capsule, synovial membrane, articular disc, and articular surface. Special cell types like chondrocytes and synovial cells were also identified. Conclusion: Goldner's trichrome, an underutilized technique, exhibited superior chromatic richness in differentiating the cellular and tissue components of the temporomandibular joint. KEYWORDS: histology, joint capsule, synovial membrane, temporomandibular joint, temporomandibular joint disc, trichrome stain, Wistar rats.

Real-Time Phased Array Ultrasonic Monitoring of Debonding Growth Under Cycling Loading in CFRP

Carbon Fibre Reinforced Polymer (CFRP) composites are widely employed in diverse industries due to their specific stiffness and strength, and corrosion resistance. However, over time, service-induced damage can initiate a failure that can threaten the operation’s safety and reliability, especially in critical industries like aerospace applications. The challenge lies in identifying and monitoring the defects before they escalate into serious issues. Non-Destructive Testing (NDT) methods play a pivotal role in this scenario, aiming to detect and evaluate defects without causing harm to the material. Detecting flaws in composite materials such as CFRP remains a complex task. Traditional NDT methods often struggle to effectively pinpoint and assess defects within these intricate structures. However, recent advancements in ultrasonic testing, especially Phased Array Ultrasonic Testing (PAUT), show promise in examining the internal composition of CFRP composites with higher accuracy and reliability. This study aims to demonstrate the applicability of PAUT for real-time monitoring of debond growth under dynamic loading. The loading was applied to the test specimens up to one million cycles, and 64 elements 2 MHz linear phased array probe was attached to the specimen. The acquired signals were analyzed and could also be recorded for offline processing or archiving. To validate the credibility of the findings, the performance of PAUT was compared to Acoustic Emission (AE), a well-established method for detecting and analysing high-frequency signals emitted by materials undergoing deformation or damage. Encouragingly, both methods exhibited similar trends in capturing the growth of debond. Furthermore, to verify the final defect lengths, post-test ultrasonic C-Scans utilizing the through transmission technique were conducted, complemented by visual inspections. These assessments served to confirm the accuracy and reliability of the results obtained through Phased Array Ultrasonic Testing. Real-time monitoring of composite structures using phased array technology comprises a valuable tool for real- time assessment of debonding in the composite. As industries continue to rely on Carbon Fibre Reinforced Polymer composites for multifaceted applications, the utilization of advanced Non-Destructive Testing methodologies like Phased Array Ultrasonic Testing emerges as a cornerstone in guaranteeing their continued integrity and performance.

Continuous 3D printing of metal structures using ultrafast mask video projection initiated vat photopolymerization

Additive manufacturing (AM) has recently emerged as the go-to technology for producing high-quality metal components to address cutting-edge research and industrial challenges. However, existing metal AM technologies operate on a layer-by-layer basis, giving rise to undesirable "staircasing effects" that result in the anisotropic behavior. Additionally, conventional metal printing methods are constrained by printing times, expensive hardware, and limited resolution control. In response to these limitations, this work is dedicated to the development of a cost-effective, ultra-fast layerless metal AM process for crafting intricate 3D metal structures. Low viscous resin was developed to enable continuous printing of metal precursors via mask video projection-based vat photopolymerization, which enable tunable physical properties and superior surface quality. Based on experimental findings, high-density meso- and microscale metallic parts can be printed at speeds orders of magnitude faster than commercial metal AM technologies. Furthermore, printed metallic components display a uniform morphology and microstructure with consistent grain size distribution. A comparative analysis of the surface quality between layer-based and layer-less printed metal structures were conducted. This work has the potential to revolutionize not only the field of AM but also various industries reliant on high-quality metal components, such as aerospace, automotive, and medical devices.

More Accurately Estimating Aboveground Biomass in Tropical Forests With Complex Forest Structures and Regions of High‐Aboveground Biomass

AbstractAccurately estimating aboveground biomass (AGB) in tropical forests is vital for managing the threats posed by deforestation, degradation, and climate change. However, challenges persist in accurately estimating AGB in high AGB regions. This study aims to accurately estimate the AGB of regions with high AGB by using spatial statistical analyses based on AGB estimates made by machine‐learning fusion of multisource data. We hypothesize that incorporating dominant auxiliary factors in the analysis increases the estimation accuracy. This study focuses on tropical forests located in Longyan, Fujian Province, China, covering an area of 19,028 km2. Multisource data are used, including airborne laser scanning, the Shuttle Radar Topography Mission digital elevation model, the Landsat Operational Land Imager, and the National Forest Inventory. Based on GeogDetector's spatial covariance matrix and the spatial similarity principle, we identify key auxiliary factors (dominant tree species, canopy closure, and herbaceous cover) and investigated how auxiliary variables can improve estimation accuracy. Empirical Bayesian kriging regression prediction introduces the main auxiliary factors to refine AGB estimates. These refinements significantly enhance the accuracy of AGB estimates, particularly for high AGB, resulting in a 0.1 increase in R2, a 7.0% reduction in root mean square error, a 13.5% reduction in mean square error, and a 6.6% reduction in mean absolute error when compared with the AGB estimates obtained by using machine learning to fuse multisource data. Thus, incorporating spatial statistical analysis into the integration of multisource data and machine learning for AGB estimation can enhance the accuracy of high‐AGB estimates in intricate forest structures, resulting in precise AGB maps.

Advancing diagnostics and disease modeling: current concepts in biofabrication of soft microfluidic systems

Soft microfluidic systems play a pivotal role in personalized medicine, particularly in in vitro diagnostics tools and disease modeling. These systems offer unprecedented precision and versatility, enabling the creation of intricate three-dimensional (3D) tissue models that can closely emulate both physiological and pathophysiological conditions. By leveraging innovative biomaterials and bioinks, soft microfluidic systems can circumvent the current limitations involving the use of polydimethylsiloxane (PDMS), thus facilitating the development of customizable systems capable of sustaining the functions of encapsulated cells and mimicking complex biological microenvironments. The integration of lab-on-a-chip technologies with soft nanodevices further enhances disease models, paving the way for tailored therapeutic strategies. The current research concepts underscore the transformative potential of soft microfluidic systems, exemplified by recent breakthroughs in soft lithography and 3D (bio)printing. Novel applications, such as multi-layered tissues-on-chips and skin-on-a-chip devices, demonstrate significant advancements in disease modeling and personalized medicine. However, further exploration is warranted to address challenges in replicating intricate tissue structures while ensuring scalability and reproducibility. This exploration promises to drive innovation in biomedical research and healthcare, thus offering new insights and solutions to complex medical challenges and unmet needs.