Dynamic Structure Research Articles

Soil Structure Interaction of R.C. Building with Basement Resting on Pile Raft Foundation

Abstract: Historically, Basements or underground stories were built in a castle to use as a dunged on oar store room. However, in modern construction, the restrain to go deeper below the grade level in term so basements which can be utilized for parking, shopping mall or combination of both. In such cases, dynamic soil properties have a significant effect of activating dynamic soil structure interaction phenomena on during earthquake. Here in present study an effort is made to study the behavior of a building by varying number so basements considering dynamic soil structure interaction. Issues like considering higher frequency modes, influence zone to be considered for dynamic soil structure interaction, behavior of building with basements under different water level conditions for two different types of layered soil and their comparison with fixed based structure is deal with. It is observed that dynamic soil structure interaction can significant change the behavior of the building and hence it is recommended to perform dynamic soil structure interaction for building with multiple basements. In addition, some important recommendations are provided at the end to serve as a guide for researchers and practicing engineers.

Detection of Phishing Websites by Using URL Analysis

Abstract: In recent years, with the increasing use of mobile devices, there is a growing trend to move almost all real-world operations to the cyberworld. Although this makes easy our daily lives, it also brings many security breaches due to the anonymous structure of the Internet. Used antivirus programs and firewall systems can prevent most of the attacks. However, experienced attackers target on the weakness of the computer users by trying to phish them with bogus webpages. These pages imitate some popular banking, social media, e-commerce, etc. sites to steal some sensitive information such as, user-ids, passwords, bank account, credit card numbers, etc. Phishing detection is a challenging problem, and many different solutions are proposed in the market as a blacklist, rule-based detection, anomaly-based detection, etc. In the literature, it is seen that current works tend on the use of machine learning-based anomaly detection due to its dynamic structure, especially for catching the “zero-day” attacks. In this paper, we proposed a machine learning-based phishing detection system by using eight different algorithms to analyse the URLs, and three different datasets to compare the results with other works. The experimental results depict that the proposed models have an outstanding performance with a success rate.

Adhesive Zwitterionic Poly(ionic liquid) with unprecedented Organic Solvent Resistance.

The resistance of adhesives to organic solvents is of paramount importance in diverse industries. Unfortunately, many currently available adhesives exhibit either weak intermolecular chain interactions, resulting in insufficient resistance to organic solvents, or possess a permanent covalent crosslinked network, impeding recyclability. This study introduces an innovative approach to address this challenge by formulating zwitterionic poly(ionic liquid) (ZPIL) derivatives with robust dipole-dipole interactions, incorporating sulfonic anions and imidazolium cations. Due to its unique dynamic and electrostatic self-crosslinking structure, the ZPIL exhibits significant adhesion to various substrates and demonstrates excellent recyclability even after multiple adhesion tests. Significantly, ZPIL exhibits exceptional adhesion stability across diverse non-polar and polar organic solvents, including ionic liquids, distinguishing itself from non-ionic polymers and conventional poly(ionic liquid)s. Its adhesive performance remains minimally affected even after prolonged exposure to soaking conditions. The study presents a promising solution for the design of highly organic solvent-resistant materials for plastics, coatings, and adhesives. This article is protected by copyright. All rights reserved.

Establishing an optimized ATAC-seq protocol for the maize

The advent of next-generation sequencing in crop improvement offers unprecedented insights into the chromatin landscape closely linked to gene activity governing key traits in plant development and adaptation. Particularly in maize, its dynamic chromatin structure is found to collaborate with massive transcriptional variations across tissues and developmental stages, implying intricate regulatory mechanisms, which highlights the importance of integrating chromatin information into breeding strategies for precise gene controls. The depiction of maize chromatin architecture using Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) provides great opportunities to investigate cis-regulatory elements, which is crucial for crop improvement. In this context, we developed an easy-to-implement ATAC-seq protocol for maize with fewer nuclei and simple equipment. We demonstrate a streamlined ATAC-seq protocol with four key steps for maize in which nuclei purification can be achieved without cell sorting and using only a standard bench-top centrifuge. Our protocol, coupled with the bioinformatic analysis, including validation by read length periodicity, key metrics, and correlation with transcript abundance, provides a precise and efficient assessment of the maize chromatin landscape. Beyond its application to maize, our testing design holds the potential to be applied to other crops or other tissues, especially for those with limited size and amount, establishing a robust foundation for chromatin structure studies in diverse crop species.

Rich-club organization of whole-brain spatio-temporal multilayer functional connectivity networks

ObjectiveIn this work, we propose a novel method for constructing whole-brain spatio-temporal multilayer functional connectivity networks (FCNs) and four innovative rich-club metrics.MethodsSpatio-temporal multilayer FCNs achieve a high-order representation of the spatio-temporal dynamic characteristics of brain networks by combining the sliding time window method with graph theory and hypergraph theory. The four proposed rich-club scales are based on the dynamic changes in rich-club node identity, providing a parameterized description of the topological dynamic characteristics of brain networks from both temporal and spatial perspectives. The proposed method was validated in three independent differential analysis experiments: male–female gender difference analysis, analysis of abnormality in patients with autism spectrum disorders (ASD), and individual difference analysis.ResultsThe proposed method yielded results consistent with previous relevant studies and revealed some innovative findings. For instance, the dynamic topological characteristics of specific white matter regions effectively reflected individual differences. The increased abnormality in internal functional connectivity within the basal ganglia may be a contributing factor to the occurrence of repetitive or restrictive behaviors in ASD patients.ConclusionThe proposed methodology provides an efficacious approach for constructing whole-brain spatio-temporal multilayer FCNs and conducting analysis of their dynamic topological structures. The dynamic topological characteristics of spatio-temporal multilayer FCNs may offer new insights into physiological variations and pathological abnormalities in neuroscience.

Tribological synergy of copper‐based metal–organic frameworks, carbon fibers and SiO2 in modifying the friction and wear properties of epoxy composites

AbstractCopper‐based metal–organic frameworks is anticipated to find applications in the field of friction materials owing to its plentiful surface‐active groups and distinctive framework structure. In this study, copper‐based metal–organic frameworks was synthesized through ultrasound‐assisted method and incorporated alone into carbon fiber reinforced epoxy composites or together with SiO2 nanoparticles towards the goal of improving the composites' friction and wear properties. It is revealed that copper‐based metal–organic frameworks alone cannot improve the friction and wear of carbon fiber reinforced epoxy composites simultaneously. The combination of copper‐based metal–organic frameworks and SiO2 nanoparticles was more effective in enhancing both the mechanical and tribological properties of studied composites. The composite material exhibits a friction coefficient of 0.104 under the specified test conditions of 10 MPa and 3.75 m/s. Additionally, the specific wear rate is impressively low, measuring only 5.64 × 10−7 mm3 N/m. Through morphological and chemical analysis on the worn surfaces and transfer films, the functioning mechanism of copper‐based metal–organic frameworks in modifying the mechanical and tribological properties was discussed. It was discovered that a tribological synergy exists between short carbon fibers, copper‐based metal–organic frameworks, and SiO2 fillers, leading to a shift in the wear mechanism of the composite materials from abrasive wear to adhesive wear. Concurrently, a shear‐prone transfer film forms on the surface of the counter steel surfaces. Findings of the present study pave a new route of designing high performance epoxy based tribo‐composites by using metal–organic frameworks.Highlights Cu‐BDC nanosheets were synthesized using ultrasound‐assisted techniques and incorporated as tribological fillers in EP composites. The combined use of Cu‐BDC, SiO2, and SCF fillers synergistically enhances the mechanical and tribological properties of EP composites. The composite material exhibits a friction coefficient of 0.104 under the specified test conditions of 10 MPa and 3.75 m/s, the specific wear rate is impressively low, measuring only 5.64 × 10−7 mm3N/m. During the sliding friction process of the composite materials, Cu‐BDC and SiO2 play a crucial role in reducing SCF pull‐out wear through their anchoring effect. The dynamic coordination bond structure of Cu‐BDC contributes to the formation of uniform and continuous transfer films on counter metal surfaces.

Bayesian inference and optimisation of stochastic dynamical networks

Network inference has been extensively studied in several fields, such as systems biology and social sciences. Learning network topology and internal dynamics is essential to understand mechanisms of complex systems. In particular, sparse topologies and stable dynamics are fundamental features of many real-world continuous-time (CT) networks. Given that usually only a partial set of nodes are able to observe, we consider linear CT systems to depict networks since they can model unmeasured nodes via transfer functions. Additionally, measurements tend to be noisy and with low and varying sampling frequencies. This paper applies dynamical structure functions (DSFs) derived from linear stochastic differential equations (SDEs) to describe networks of measured nodes. A numerical sampling method, preconditioned Crank–Nicolson (pCN), is used to refine coarse-grained trajectories to improve inference accuracy. The proposed method can handle sparsely sampled data and unmeasurable nodes. Monte Carlo simulations indicate that the proposed method outperforms state-of-the-art methods with various network topologies. The developed method can be applied under a wide range of contexts, such as gene regulatory networks, social networks and communication systems.

Proposing a physical mechanism to explain various observed sources of QPOs by simulating the dynamics of accretion disks around the black holes

We propose a mechanism to explain the low-frequency QPOs observed in X-ray binary systems and AGNs. To achieve this, we perturbed stable accretion disks around Kerr and EGB black holes at different angular velocities, revealing characteristics of shock waves and oscillations on the disk. By applying this perturbation to scenarios with varying alpha values for EGB black holes and different spin parameters for Kerr black holes, we numerically observed changes in the disk dynamic structure and its oscillations. Through various numerical models, we found that the formation of one- and two-armed spiral shock waves on the disk serves as a mechanism for generating QPOs. We compared the QPOs obtained from numerical calculations with the low-frequency QPOs observed in X-ray binary systems and AGN sources, finding high consistency with observations. We observed that the shock mechanism, leading to QPOs, explains the X-ray binaries and AGNs studied in this article. Our numerical findings indicate that QPOs are more strongly dependent on the EGB constant than on the black hole spin parameter. However, we highlighted that the primary impact on oscillations and QPOs is driven by the perturbation angular velocity. The results from the models showed that the perturbation asymptotic speed at V∞=0.2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$V_{\\infty }=0.2$$\\end{document} independently generates QPO frequencies, regardless of the black hole spin parameter and the EGB coupling constant. Therefore, for the moderate value of V∞\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$V_{\\infty }$$\\end{document}, a two-armed spiral shock wave formed around the black hole is suggested as a decisive mechanism in explaining low-frequency QPOs.

Transferable deep generative modeling of intrinsically disordered protein conformations.

Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. Molecular simulations are a valuable computational strategy for constructing structural ensembles of disordered proteins but are highly resource-intensive. Recently, machine learning approaches based on deep generative models that learn from simulation data have emerged as an efficient alternative for generating structural ensembles. However, such methods currently suffer from limited transferability when modeling sequences and conformations absent in the training data. Here, we develop a novel generative model that achieves high levels of transferability for intrinsically disordered protein ensembles. The approach, named idpSAM, is a latent diffusion model based on transformer neural networks. It combines an autoencoder to learn a representation of protein geometry and a diffusion model to sample novel conformations in the encoded space. IdpSAM was trained on a large dataset of simulations of disordered protein regions performed with the ABSINTH implicit solvent model. Thanks to the expressiveness of its neural networks and its training stability, idpSAM faithfully captures 3D structural ensembles of test sequences with no similarity in the training set. Our study also demonstrates the potential for generating full conformational ensembles from datasets with limited sampling and underscores the importance of training set size for generalization. We believe that idpSAM represents a significant progress in transferable protein ensemble modeling through machine learning.

An online model-free adaptive learning control solution for robotic arms

This paper focuses on the online control of a class of nonlinear dynamical systems, specifically robotic manipulators. Solutions utilizing Proportional–Integral–Derivative (PID) control schemes are employed to control the joints of robotic manipulators. However, the existing control strategies utilize fixed gains, which do not fully account for the inherent nonlinearity of the dynamical structure or the dynamics of reference-tracking error. Additionally, the individual joint’s dynamic performance is optimized independently from the performance of other joints. This work introduces an adaptive integral Reinforcement Learning algorithm to control a four-DoF robotic arm in real time. This is done using a model-free Value Iteration process implemented in a continuous-time mode. The solution does not assume any knowledge of the dynamics of the robot arm and does not require any initial admissible control strategy to proceed with the adaptive learning solution. The self-learning algorithm provides adaptable strategies to control the turntable, forearm, bicep, and wrist joints of the robotic arm. The performance of the adaptive learning solution is compared with those of Proportional–Integral–Derivative and high-order model-free adaptive control schemes to highlight its effectiveness.

Spatiotemporal diffusion metamaterials: Theories and applications

Diffusion metamaterials with artificial spatial structures have significant potential in controlling energy and mass transfer. Those static structures may lead to functionality and tunability constraints, impeding the application scope of diffusion metamaterials. Dynamic structures, adding the temporal dimension, have recently provided a new possibility for electric charge and heat diffusion regulation. This perspective introduces the fundamental theories and practical constructions of spatiotemporal diffusion metamaterials for achieving nonreciprocal, topological, or tunable properties. Compared with traditional static design, spatiotemporal modulation is promising to manipulate diffusion processes dynamically, with applications of real-time thermal coding and programming. Existing spatiotemporal diffusion explorations are primarily at macroscopic systems, and we may envision extending these results to microscale and other physical domains like thermal radiation and mass diffusion shortly.

The ecology of zebra finch song and its implications for vocal communication in multi-level societies.

Acoustic signalling is crucial in affecting movements and in social interactions. In species with dynamic social structures, such as multi-level societies, acoustic signals can provide a key mechanism allowing individuals to identify and find or avoid each other and to exchange information. Yet, if the spacing between individuals regularly exceeds the maximum signalling range, the relation between movements and signals becomes more complex. As the best-studied songbird in captivity, the zebra finch (Taeniopygia castanotis) is a species with individually distinct songs that are audible over just a few metres and a widely ranging dynamic multi-level social organization in the wild, raising questions on the actual role of its song in social cohesion and coordination. Here, we provide an overview of birdsong in social organizations (networks) and use the ecology of the zebra finch and male song to discuss how singing can facilitate social cohesion and coordination in species where the signal range is very short. We raise the question of the extent to which zebra finches are a representative species to understand the function of song in communication, and we broaden current views on the function of birdsong and its individual signature. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

A novel variant in the FLNB gene associated with spondylocarpotarsal synostosis syndrome.

Genetic disorders involved in skeleton system arise due to the disturbance in skeletal development, growth and homeostasis. Filamin B is an actin binding protein which is large dimeric protein which cross link actin cytoskeleton filaments into dynamic structure. A single nucleotide changes in the FLNB gene causes spondylocarpotarsal synostosis syndrome, a rare bone disorder due to which the fusion of carpels and tarsals synostosis occurred along with fused vertebrae. In the current study we investigated a family residing in north-western areas of Pakistan. The whole exome sequencing of proband was performed followed by Sanger sequencing of all family members of the subject to validate the variant segregation within the family. Bioinformatics tools were utilized to assess the pathogenicity of the variant. Whole Exome Sequencing revealed a novel variant (NM_001457: c.209C>T and p.Pro70Leu) in the FLNB gene which was homozygous missense mutation in the FLNB gene. The variant was further validated and visualized by Sanger sequencing and protein structure studies respectively as mentioned before. The findings have highlighted the importance of the molecular diagnosis in SCT (spondylocarpotarsal synostosis syndrome) for genetic risk counselling in consanguineous families.

A Label-Free Approach for Relative Spatial Quantitation of c-di-GMP in Microbial Biofilms.

Microbial biofilms represent an important lifestyle for bacteria and are dynamic three-dimensional structures. Cyclic dimeric guanosine monophosphate (c-di-GMP) is a ubiquitous signaling molecule that is known to be tightly regulated with biofilm processes. While measurements of global levels of c-di-GMP have proven valuable toward understanding the genetic control of c-di-GMP production, there is a need for tools to observe the local changes of c-di-GMP production in biofilm processes. We have developed a label-free method for the direct detection of c-di-GMP in microbial colony biofilms using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). We applied this method to the enteric pathogen Vibrio cholerae, the marine symbiont V. fischeri, and the opportunistic pathogen Pseudomonas aeruginosa PA14 and detected spatial and temporal changes in c-di-GMP signal that accompanied genetic alterations in factors that synthesize and degrade the compound. We further demonstrated how this method can be simultaneously applied to detect additional metabolites of interest from a single sample.

In situ electron microscopy revealing dynamic structure–function relationship of heterogeneous catalyst

In situ electron microscopy revealing dynamic structure–function relationship of heterogeneous catalyst

Liquid Dynamics in the Upper Respiratory–Digestive System with Contracting Pharynx Motions and Varying Epiglottis Angles

Swallowing disorders, or dysphagia, can lead to bolus aspiration in the airway, causing serious adverse health effects. Current clinical interventions for dysphagia are mainly empirical and often based on symptoms rather than etiology, of which a thorough understanding is still lacking. However, it is challenging to study the swallowing process that involves sequential structural motions and is inaccessible to standard visualization instruments. This study proposed an in vitro method to visualize swallowing hydrodynamics and identify the fundamental mechanisms underlying overflow aspirations. An anatomically accurate pharynx–epiglottis model was developed from patient-specific CT images of 623 µm isotropic resolution. A compliant half-pharynx cast was prepared to incorporate dynamic structures and visualize the flow dynamics in the mid-sagittal plane. Three locations of frequent overflow aspiration were identified: the epiglottis base, cuneiform tubular recesses, and the interarytenoid notch. Water had a consistently higher aspiration risk than a 1% w/v methylcellulose (MC) solution. The contracting–relaxing pharynx and flapping epiglottis spread the liquid film, causing a delayed esophageal entry and increased vallecular residual, which was more pronounced with the MC solution. Dispensing the liquid too slowly resulted in water aspiration, whereas this was not observed with the MC solution. An incomplete epiglottis inversion, such as horizontal or down-tilt 45°, aggravated the aspiration risks of water. This study suggests that it is practical to use anatomically accurate respiratory–digestive models to study the swallowing process by incorporating varying physiological details.

Aspartyl peptidase May1 induces host inflammatory response by altering cell wall composition in the fungal pathogen Cryptococcus neoformans.

The fungal cell wall is a dynamic structure, monitoring and responding to internal and external stimuli. It provides a formidable armor to the fungus. However, in a weakened state, the cell wall also triggers host immune attack when PAMPs, including glucan, chitin, and mannoproteins, are exposed. In this work, we found that the aspartyl peptidase May1 impairs the cell wall of Cryptococcus neoformans and increases the exposure of PAMPs in the acidic environment by reducing the chitosan level. Under acidic conditions, May1 is involved in the degradation of the chitin synthase Chs3, which supplies chitin to be deacetylated to chitosan. Consistently, the severe deficiency of chitosan in acidic pH can be rescued by overexpressing CHS3. These findings improve our understanding of cell wall remodeling and reveal a potential target to compromise the cell wall integrity in this important fungal pathogen.

Research on Energy Loss of Optimization of Inducer–Impeller Axial Fit Dimensions Based on Wave-Piercing Theory

With the development of modern fluid machinery, the energy density of pumps is gradually being improved, and at the same time, higher demands are being placed on the cavitation performance, hence the introduction of the inducer and centrifugal impeller to form a dynamic–dynamic series structure. However, there are strict constraints on the axial size of pumps in fields such as firefighting and aerospace. The traditional empirical formula no longer satisfies the need to fit the axial dimensions between the induced wheel and the impeller at high velocities. Therefore, based on the wave-piercing theory, the drag reduction coefficient is introduced to explore the optimal axial fit size from the perspective of energy characteristics. This paper focuses on the influence of the inducer’s wake on the energy characteristics of downstream impellers, and conducts the following research: by adjusting the axial matching dimensions between the upstream inducer and the centrifugal impeller in the initial model, ten sets of axial distance models with matching dimensions of KD are designed, and the drag reduction coefficient is embedded to determine the optimal axial distance. The results show that the optimal axial distance is 0.2D, which is far lower than the axial distance value of 0.42D obtained from the traditional empirical formula for axial matching dimensions. Meanwhile, this paper uses tangential velocity, the inlet flow angle of the impeller, entropy production theory, and other indicators to analyze the internal energy loss of the high-speed vehicular fire pumps one by one. All of them confirm that the impeller in the high-speed vehicular fire pump has the lowest energy loss and optimal performance at an axial distance of 0.2D. Specifically, at this axial distance, the head can reach 259 m, and the hydraulic efficiency is as high as 83.62%. Thus, the feasibility of determining the axial placement of the impeller using the drag coefficient is validated. This research provides new insights into determining the axial coordination dimensions between the inducer and the impeller.

Unveiling Dynamic Structure and Bond Evolutions in BiOIO3 Photocatalysts during CO2 Reduction

We have established a correlation between photocatalytic activity and dynamic structure/bond evolutions of BiOIO3‐based photocatalysts during CO2 reduction by combining operando X‐ray diffraction with photoelectron spectroscopy. More specifically, the selective photo‐deposition of PtOx species on BiOIO3 (010) facets could effectively promote the electron enrichment on Bi active sites of (100) facets for facilitating the adsorption/activation of CO2 molecules, leading to the formation of Bi sites with high oxidation state and the shrink of crystalline structures. With introducing light irradiation to drive CO2 reduction, the Bi active sites with high oxidation states transformed into normal Bi3+ state, accompanying with the expansion of crystalline structures. Owing to the dynamic structure, bond, and chemical‐state evolutions, a significant improvement of photocatalytic activity for CO evolution has been achieved on PtOx‐BiOIO3 (195.0 μmol g‐1•h‐1), much higher than the pristine (61.9 μmol g‐1•h‐1) as well as metal‐Pt decorated BiOIO3 (70.3 μmol g‐1•h‐1) samples. This work provides new insights to correlate the intrinsically dynamic structure/bond evolutions with CO2 reduction activity, which may help to guide future photocatalyst design.

Unveiling Dynamic Structure and Bond Evolutions in BiOIO3 Photocatalysts during CO2 Reduction.

We have established a correlation between photocatalytic activity and dynamic structure/bond evolutions of BiOIO3-based photocatalysts during CO2 reduction by combining operando X-ray diffraction with photoelectron spectroscopy. More specifically, the selective photo-deposition of PtOx species on BiOIO3 (010) facets could effectively promote the electron enrichment on Bi active sites of (100) facets for facilitating the adsorption/activation of CO2 molecules, leading to the formation of Bi sites with high oxidation state and the shrink of crystalline structures. With introducing light irradiation to drive CO2 reduction, the Bi active sites with high oxidation states transformed into normal Bi3+ state, accompanying with the expansion of crystalline structures. Owing to the dynamic structure, bond, and chemical-state evolutions, a significant improvement of photocatalytic activity for CO evolution has been achieved on PtOx-BiOIO3 (195.0 μmol g-1•h-1), much higher than the pristine (61.9 μmol g-1•h-1) as well as metal-Pt decorated BiOIO3 (70.3 μmol g-1•h-1) samples. This work provides new insights to correlate the intrinsically dynamic structure/bond evolutions with CO2 reduction activity, which may help to guide future photocatalyst design.