Grid-like Structure Research Articles

An aperiodically intermittent control for finite-time and fixed-time synchronization of stochastic FCNN with switching parameters

The main objective of this paper is to design an aperiodically intermittent control to ensure the finite and fixed time synchronization of fuzzy cellular neural networks (FCNNs) involving switching parameters with threshold properties, time-dependent discrete, continuous-type delays, and stochastic disturbances during transmission. Cellular neural networks (CNNs), with their grid-like structure, excel in image-processing tasks. Incorporating the fuzziness in the CNNs may handle the uncertainties and incomplete information in an effective manner. Furthermore, this paper introduces the concept of user-controlled FCNNs, which retain the dynamic properties of conventional FCNNs while incorporating significant factors that could potentially degrade the performance of the considered model. Due to randomness, FCNNs are framed as a set of stochastic differential equations with memristors. Generally, a memristor functions as a non-volatile memory device, reserving past data and utilizing it in the event of transmission failure. Further, the synchronization process is an effective approach that helps to ensure the dynamical behaviors of two different models; hence, the paper formulates the synchronization problem for the FCNN model with control (response) and without control input (drive). In terms of control design, this paper also considers the aperiodically intermittent control (APIC) scheme that can drive the user-controlled FCNN solution trajectories onto conventional FCNN solution trajectories within a prescribed time. Additionally, the APIC scheme incorporates an adaptive mechanism that effectively manages switching behaviors and uncertainties. Furthermore, this study proposes a theoretical framework in the form of sufficient stability conditions that helps to ensure the synchronization of the drive-response model via the global asymptotical stability of the stochastic error model derived from the drive-response model under the APIC scheme in the mean square. The proposed stochastic FCNN model is numerically simulated, and the corresponding outcomes are graphically illustrated.

Material analysis on semi-permanent makeup needles

The cosmetic-tattoo industry is evolving every year and the microstructures of the equipment have the great potential for semi-permanent makeup applications. Present paper explores the materials and microparticles of semi-permanent makeup tattoo needles. The surface of the five-round-shader tattoo needle used in semi-permanent makeup process was examined by scanning electron microscopy, and its elemental composition was analyzed by energy-dispersive X-ray spectroscopy. The comparison of five-round-shader needles have undergone thorough observation: original five-round-shader needle and distorted five-round-shader needle. The diameter of the sharp and rounded needle tip was measured at approximately 6.80 μm, while the deformed needle tip was approximately 16 μm thick, about 2.5 times thicker than the rounded needle tip. Many rosette-shaped lead (Pb) particles and irregular clusters adhere to the welded areas and closely adjacent needle shaft surfaces. The lead particles have a diameter ranging from 4 μm to 5 μm and exhibit a grid-like structure with a consistent thickness of plate-like shape. The distorted structure of Pb in rosette-shaped formations is shown to have originated from the grinding and polishing processes during needle manufacturing. To produce sterilized tattoo needles, high-quality tattoo needle inspection processes are necessary to remove any unhygienic substances adhering to the needle surface.

In situ iron coating of amorphous boron and characterization of its energy release behavior

Boron was widely employed as metal fuel in solid propellants for its higher gravimetric and volumetric combustion enthalpies. However, the low melting point and high boiling point of B2O3 on the surface of boron particles significantly hinder its ignition and combustion performance, seriously preventing the full realization of its thermodynamic potential. To address this issue, B@Fe composite fuels with different iron contents from 1.59 % to 7.09 % were prepared via liquid phase reduction in this paper and the quasi-static high temperature oxidation, ignition and combustion reaction characteristics and corresponding mechanism were studied, in order to provide effective modification techniques in supporting the large-scale engineering application of boron as metal fuel. The study results show that B@Fe can effectively reduce the ignition temperature and maximum heat flow temperature of amorphous boron, and increase the maximum mass gain rate. The ignition temperature and maximum heat flow temperature have an exponential function with the iron coating content. Iron coated amorphous boron can effectively shorten the laser ignition delay time by more than 50 %, and significantly change the flame structure, flame evolution process and combustion duration. Both amorphous boron and iron-coated amorphous boron flame showed a multi-layer structure, including the outer green flame, the middle yellow flame and the central incandescent flame. However, with the increase of the iron coating content, the flame height increased from 5.5 cm to 6.3 cm, the flame structure changed from mushroom type to triangle type, and the central incandescent flame area increased significantly. The equivalent combustion duration increased from 975 ms to 1997 ms, and the flame temperature increased from 1288 to 1505 °C. The emission spectra of BO2 and BO lines are captured during combustion. B@Fe with the increase of iron content, a large number of small diameter micropores appear in the condensed combustion products(CCPs) of composite fuels, and a grid-like structure was formed. With the increase of iron coating content, the boron content of condensed combustion products decreases, and the oxygen content increases. The main components of CCP include B, B2O3, B6O, FeB, Fe2B and Fe3B. Based on the ignition combustion experiment of B@Fe composite fuel, ignition combustion analysis model of B@Fe composite fuel was established.

A robust deep learning attack immune MRAM-based physical unclonable function

The ubiquitous presence of electronic devices demands robust hardware security mechanisms to safeguard sensitive information from threats. This paper presents a physical unclonable function (PUF) circuit based on magnetoresistive random access memory (MRAM). The circuit utilizes inherent characteristics arising from fabrication variations, specifically magnetic tunnel junction (MTJ) cell resistance, to produce corresponding outputs for applied challenges. In contrast to Arbiter PUF, the proposed effectively satisfies the strict avalanche criterion (SAC). Additionally, the grid-like structure of the proposed circuit preserves its resistance against machine learning-based modeling attacks. Various machine learning (ML) attacks employing multilayer perceptron (MLP), linear regression (LR), and support vector machine (SVM) networks are simulated for two-array and four-array architectures. The MLP-attack prediction accuracy was 53.61% for a two-array circuit and 49.87% for a four-array circuit, showcasing robust performance even under the worst-case process variations. In addition, deep learning-based modeling attacks in considerable high dimensions utilizing multiple networks such as convolutional neural network (CNN), recurrent neural network (RNN), MLP, and Larq are used with the accuracy of 50.31%, 50.25%, 50.31%, and 50.31%, respectively. The efficiency of the proposed circuit at the layout level is also investigated for simplified two-array architecture. The simulation results indicate that the proposed circuit offers intra and inter-hamming distance (HD) with a mean of 0.98% and 49.96%, respectively, and a mean diffuseness of 49.09%.

Synergistic Catalysis of Water-Soluble Exogenous Catalysts and Reservoir Minerals during the Aquathermolysis of Heavy Oil.

Oil serves as the essential fuel and economic foundation of contemporary industry. However, the use of traditional light crude oil has exceeded its supply, making it challenging to meet the energy needs of humanity. Consequently, the extraction of heavy oil has become crucial in addressing this demand. This research focuses on the synthesis of several water-soluble catalysts that can work along with reservoir minerals to catalyze the hydrothermal cracking process of heavy oil. The goal is to effectively reduce the viscosity of heavy oil and lower the cost of its extraction. Based on the experimental findings, it was observed that when oil sample 1 underwent hydrothermal cracking at a temperature of 180 °C for a duration of 4 h, the amount of water added and catalyst used were 30% and 0.2% of the oil sample dosage, respectively. It was further discovered that the synthesized Mn(II)C was able to reduce the viscosity of oil sample 1 by 50.38%. The investigation revealed that the combination of Mn(II)C + K exhibited a significant synergistic catalytic impact on reducing viscosity. Initially, the viscosity reduction rate was 50.38%, which climbed to 61.02%. Subsequently, when catalyzed by the hydrogen supply agent isopropanol, the rate of viscosity reduction rose further to 91.22%. Several methods, such as freezing point analysis, thermogravimetric analysis, DSC analysis, component analysis, gas chromatography, wax crystal morphology analysis, and GC-MS analysis, were conducted on aqueous organic matter derived from heavy oil after undergoing different reaction systems. These analyses confirmed that the viscosity of the heavy oil was decreased. By studying the reaction mechanism of the model compound and analyzing the aqueous phase, the reaction largely involves depolymerization between macromolecules, breakdown of heteroatom chains, hydrogenation, ring opening, and other related consequences. These actions diminish the strength of the van der Waals force and hydrogen bond in the recombinant interval, impede the creation of a grid-like structure in heavy oil, and efficiently decrease its viscosity.

A comparative study of 3D-printed scaffolds fabricated from different hydroxyapatite sources for bone tissue engineering applications

As a naturally occurring mineral component of bone, hydroxyapatite (HA) is a preferred bone-repair material in clinical medicine. This adaptable biomaterial is developed from synthetic or biological sources. The goal of this study was to investigate the physicochemical and biological properties of scaffolds made from diverse hydroxyapatite sources. We prepared hydroxyapatite powders either from naturally occurring sources, using decellularized bone extracellular matrix (DBECM) and deproteinized bovine bone (DBB), or in the laboratory using the co-precipitation method. Although XRD analysis confirmed the pure phase of HA in all three powders, XRF data found some other trace elements in bone-derived biomaterials, such as magnesium and strontium. Further, FTIR spectra showed DBECM-associated macromolecule peaks. In the second phase of this study, the particles were mixed with sodium alginate to produce bioink for 3D printing via extrusion. SEM photographs of the printed scaffolds showed a grid-like porous structure with large, interconnected pores. Although all of the scaffolds retained their structural integrity after 28 days submerged in the culture medium, DBECM-based scaffolds degraded the most. The test of Young’s modulus and compressive strength of scaffolds revealed that all scaffolds are strong enough to be employed in non-weight-bearing applications. The biological characteristics of the scaffolds were then evaluated via cytotoxicity analysis, SEM examination of cell attachment, MTT assay, ALP activity assessment, immunofluorescence staining, and Alizarin red staining. The results indicated that all three scaffolds provided a surface that promoted adhesion and proliferation of rat bone marrow mesenchymal stem cells (rMSCs), as well as stimulated the production of mineralized extracellular matrices. Additionally, our findings showed that scaffolds made from DBECM powders offer the best environment for cell activity. This is most likely due to the presence of trace elements and protein macromolecules in DBECM powders that are not found in DBB and synthetic HA. In conclusion, our comparative study revealed that while all three scaffolds are suitable for bone tissue engineering, DBECM-based scaffolds outperform DBB and synthetic HA.

Research on the Vitality of Public Spaces in Tourist Villages through Social Network Analysis: A Case Study of Mochou Village in Hubei, China

The construction of tourist villages is an important implementation path for promoting the new urbanization strategy in China. The optimization of their spatial pattern and functional adjustment is a key way to achieve high-quality urban development. The purpose of this study is to determine the influencing factors of public space vitality in tourist villages from the perspective of human behavior activities and to provide design support strategies for enhancing the vitality of public spaces in tourist villages. Using Mochou Village as an example, physical and behavioral network models were used to conduct a quantitative study of the vitality characteristics, and Quantitative Analysis of Precedence (QAP) regression was used to investigate the influence factors. The results demonstrate that spatial characteristics, such as “small block size, high street density”, and grid-like street structure and squares, as well as factors such as store concentration, sight lines, street length, spatial openness, and street width, significantly impact the vitality of public spaces in tourist villages. The analysis of the characteristics of the vitality of public space networks in tourist villages and the discussion of the influencing factors of public space vitality in this study can provide guidance for evaluating the vitality of public spaces and designing public spaces with high vitality in tourist villages.

An Enzyme-responsive Porphyrin Metal-organic Framework Nanosystem for Targeted and Enhanced Synergistic Cancer Photo-chemo Therapy.

The clinical efficiency of photodynamic therapy (PDT) in combination with chemotherapy has proven to be a promising strategy for tumor treatment, yet is restricted by the high glutathione (GSH) concentration at the tumor site and nonspecific drug targeting. The goal of the current research was to create a biocompatible GSH-depleting and tumor- targeting nanoparticle (denoted as DOX/CA@PCN-224@HA) for the combined photodynamic and chemo photo-chemo) therapy. The nanoparticles were characterized by transmission electron microscopy (TEM). A UV-vis spectrophotometer was used to measure the drug loading efficiency (DE) and encapsulation efficiency (EE). The GSH-depleting ability was measured using Ellman's test. Confocal laser scan microscopy (CLSM) was used to assess the cellular uptake. MTT was adopted to evaluate the cytotoxicity of DOX/CA@PCN-224@HA against 4T1 cells. The altered PCN-224 showed excellent monodispersing with a dimension of approximately 193 nm ± 2 nm in length and 79 nm ± 3 nm in width. The larger and spindle grid-like structure of PCN-224 obtains better dual-drug loading ability (DOX: 20.58% ± 2.60%, CA: 21.81% ± 1.98%) compared with other spherical PCN-224 nanoparticles. The ultimate cumulative drug release rates with hyaluronidase (HAase) were 74% ± 1% (DOX) and 45% ± 2% (CA) after 72 h. DOX/CA@PCN-224@HA showed GSH-consuming capability, which could improve the PDT effect. The drug-loaded nanoparticles could accurately target 4T1 cells through biological evaluations. Moreover, the released DOX and CA display cooperative effects on 4T1 cells in vitro. DOX/CA@PCN-224@HA nanoparticles showed inhibition against 4T1 cells with an IC50 value of 2.71 μg mL-1. This nanosystem displays great potential for tumor-targeted enhanced (photo-chemo) therapy.

A three-dimensional printed Si/rGO anode for flexible Li-ion batteries

A 3D-printed Si/rGO anode with a porous grid-like structure was well designed and synthesized and exhibits an excellent electrochemical performance in flexible lithium-ion batteries.

Morphology and wall ultrastructure of a unique megaspore, Flabellisporites zhaotongensis Sui, McLoughlin et Feng sp. nov., from the upper Permian of Southwest China

A new lycopsid megaspore species with intricate ornamentation, Flabellisporites zhaotongensis Sui, McLoughlin et Feng sp. nov., is described from the Lopingian Xuanwei Formation of Yunnan Province, Southwest China, using scanning and transmission electron microscopy. This trilete megaspore is characterized by elevated membraneous flanges adorned with multifurcate spines, the longest of which are discrete ribbon-like appendages that basally converge to form a membrane around the equator; and evenly spaced single or bifurcate spines with sharp tips covering the remaining surfaces. Ultrastructural analysis reveals that the megaspore wall consists of four layers: (1) a thin and dense foot layer; (2) an inner spongy layer with small circular or elongate sporopollenin grains with parallel arrangement; (3) an outer, more porous, spongy layer consisting of larger and better interconnected sporopollenin units, that form a grid-like structure in certain cases; and (4) an electron-dense outermost layer forming the sculptural elements. Both morphological and ultrastructural characteristics indicate that the new megaspore species belongs to Isoetales. Our discovery represents the oldest occurrence of Flabellisporites, indicating the origin of this morpho-lineage before the end-Permian mass extinction, and enriches the known diversity of isoetalean lycopsids in the late Permian Cathaysian Flora.

A method for continuous sub-annual mapping of forest disturbances using optical time series

Forest disturbances have a major impact on ecosystem dynamics both at local and global scales. Accordingly, it is important to acquire objective information about the location, nature and timing of such events to improve the understanding of their impact, update forest management policies and disturbance mitigation strategies. To this date, remotely sensed data have been widely used for the detection of stand replacing disturbances (SRD) such as windthrows and wildfires. In contrast, less effort has been devoted to the detection of non-stand replacing disturbances (NSRD), typically characterized by slower and gradual temporal dynamics. To address this gap, we propose a method for the automated detection of both SRD and NSRD. The proposed method can detect both past and recent disturbances, with a monthly temporal resolution, in a near real-time fashion by processing new images as they are acquired. Differently from existing approaches that handle the time series as a one-dimensional (1D) temporal trajectory, the method analyzes the sequence of images by organizing them in a two-dimensional (2D) grid-like structure. This representation allows us to model both the intra- and inter-annual variations of the time series taking advantage of the annual cyclical nature of the plant phenology. The method has been tested on study areas attacked by bark beetles achieving a user’s accuracy and producer’s accuracy of 0.91±0.08 and 0.81±0.07 (with 95% confidence intervals) for the disturbed areas, respectively.

Development of grid-like structure hybridised advanced sheet moulding compound composite part with characterisation and prediction of the flexural stiffness

To increase the mechanical properties of sheet moulding compound (SMC) and extend the application on structural components, cost-effective towpreg-based grid-like structures with varying spacing, layers and layout were constructed, numerically studied on the tray-based 3D part and finally defined, which were co-moulded with SMC in the one-shot compression process. The hybridisation effect on the global and local elastic flexural property was experimentally characterised for the 3D part and 2D plate. For the 2D plate, an adapted analytical model was proposed treating the hybrid SMC as a laminate of different plies. A parametric study evaluated the influence of manufacturing process variables on predictive accuracy. Plateau-shaped out-of-plane waviness in towpreg was identified as the critical variable, and one adapted exponential power distribution was tailored to capture the waviness and integrated into the analytical model. Furthermore, demand for thinner towpreg was identified.

The effect of prototypicality on webpage aesthetics, usability, and trustworthiness

The user expects webpages of specific categories to have a look-and-feel specific to that category. For example, unlike university homepages, online-shop webpages typically feature relatively little text, a long grid-like structure listing products, and numerous functional elements for product search, filtering, and recommendation. Ensuring that a webpage meets user expectations makes it highly prototypical and improves the user impression of the webpage. Despite the potential impact on users, the concept of webpage prototypicality has not been fully explored or extensively employed in Human-Computer Interaction (HCI). This paper addresses this gap by conducting a user study with 1530 participants to investigate webpage prototypicality. The study revealed a strong correlation between prototypicality and webpage visual aesthetics, perceived pre-use usability, and trustworthiness. Notably, the direct effect of prototypicality on trustworthiness outweighed the indirect effects through aesthetics and usability. Overall, prototypicality, aesthetics, and usability collectively accounted for 29% to 68% of the variance in trustworthiness, depending on a webpage category. These findings underscore the importance of embracing prototypicality within the field of HCI, encouraging its wider adoption.

Effect of laser engraving on shear bond strength of polyetheretherketone to indirect composite and denture-base resins

Background/purposePolyetheretherketone (PEEK) is a highly sought-after thermoplastic due to its exceptional mechanical properties and biocompatibility. However, bonding PEEK to indirect composite resin (ICR) or denture-based resin (DBR) can be challenging. Laser engraving technology has shown potential to improve bonding for other materials; thus, this study aims to evaluate its effectiveness for PEEK. Materials and methodsThe experiment involved preparing ingot-shaped PEEK samples, which were then categorized into four groups based on the treatment method employed: without treatment, air abrasion, sulfuric acid etching, and laser engraving (LS). Subsequently, the samples were bonded to ICR or DBR, and their shear bond strength (SBS) was tested with or without thermocycling using a universal testing machine. Furthermore, the failure mode was observed, with statistical analyses conducted to compare the results. ResultsThe grid-like microslit structure of LS group displayed the highest SBS for bonding PEEK to ICR or DBR (P < 0.05). During the bonding of PEEK to ICR, resin residue and penetration into the microslits were frequently observed in the LS group, indicating cohesive failure. However, when PEEK was bonded to DBR, mixture failure was frequently observed without thermocycling. After thermocycling, only the LS group showed cohesive failure, while the majority of specimens exhibited mixture failure. ConclusionLaser engraving significantly improves the SBS between PEEK and both ICR and DBR. Furthermore, it was observed that resin had penetrated the microslits, indicating that laser engraving has great potential as a surface treatment method.

Oxidized Platinum Cocatalyst and Self-Assembled Graphene over Graphitic Carbon Nitride for Photocatalytic Hydrogen Evolution

The nanostructural design of graphitic carbon nitride (g-C3N4) plays a primary role in addressing the drawbacks posed by low surface area, which results in poor dispersion of accessible active sites in the reaction media of bare g-C3N4. Here, we report the solid-state structure transformation through solvothermal treatment of bulk g-C3N4 in an environmentally sustainable organic solvent. The grid-like structure of the monolayered g-C3N4 nanosheets was achieved using a facile solvothermal process. The formation of self-assembled graphene on g-C3N4 during the process facilitated charge transfer and separation on the photocatalyst, as confirmed by density functional theory calculations. The grid-like structure of the g-C3N4 contributed to the formation of a platinum oxide cocatalyst, while the formation of metallic platinum was observed in the bulk g-C3N4 sample. The platinum oxide cocatalyst enhanced the hydrogen evolution rate (HER) by inhibiting the reversible reaction pathway of hydrogen gas to protons. As a result, the platinum support on the grid-like structure of g-C3N4 and self-assembled graphene hybrid (hCN-G) achieve a photocatalytic H2 evolution rate 7.5 times that of bulk g-C3N4 (CN-b). The hCN-G contained 2.04 wt % graphene, which contributed significantly to the improvement in photocatalytic activity. The HER of hCN-G at the stationary point reached 16 832.9 μmol g–1 h–1 under 1 sun of simulated sunlight irradiation.

Urban structure reinforces attitudes towards tsunami evacuation

Evacuation is a critical life-saving action, especially in devastating natural hazards such as near-field tsunamis. However, the development of effective evacuation measures remains challenging to the extent that a successful example has been referred to as a ‘miracle’. Here we show that urban structures have the potential to reinforce attitudes towards evacuation and significantly influence the success of tsunami evacuation. Agent-based evacuation simulations revealed that a distinctive root-like urban structure formed in ria coasts reinforces positive evacuation attitudes by effectively gathering evacuation flows and leads to higher evacuation rates compared to typical grid-like urban structures, which can explain the regional differences in the number of casualties in the 2011 Tohoku tsunami. Although a grid-like structure reinforces negative attitudes under low evacuation tendencies, with leading evacuees, its dense feature helps to propagate positive attitudes and drastically improve evacuation tendencies. These findings pave the way for making successful evacuation inevitable through harmonised urban and evacuation plannings.

Why &lt;em&gt;Monopoly&lt;/em&gt; Monopolises Popular Culture Board Games

Why &lt;em&gt;Monopoly&lt;/em&gt; Monopolises Popular Culture Board Games

A novel fault evaluation method based on nonlinear vibration features and Euclidean distance measurement for grid-like structures

Complex grid-like structures such as steel truss bridges and steel truss roofs commonly exist in civil engineering applications. Since these structures are usually subject to dynamic operational forces, faults like chemical corrosion, fatigue crack, and bolt loosening arise, and seriously affect structural health. To consider some issues in existing frequency domain fault evaluation methods like applicability for complex structures, requirement of baseline data, neglect of nonlinear boundaries, and localization of local faults, a novel method using nonlinear vibration features and Euclidean distance measurement is presented in this paper. Firstly, complex grid-like structures are decomposed into a series of simple T-type substructures according to structural characteristics and load paths. Secondly, related T-type multi-degree-of-freedom model is built by simulating potential faults and nonlinear boundaries as related nonlinear damper-spring connections. Thirdly, exciting the model and extracting output responses only, novel and local fault features, which are functions of structural properties, nonlinear fault-induced loads and transmissibility functions, are defined. Then, utilizing defined features from the local substructure to be evaluated only, a novel fault index is defined with Euclidean distance measurement. Finally, corresponding evaluation method is developed, and its effectiveness and applicability are demonstrated by comparative studies on a lab bolted grid-like structure.

Removing grid structure in angle-resolved photoemission spectra via deep learning method

Spectroscopic data may often contain unwanted extrinsic signals. For example, in the angle-resolved photoemission spectroscopy (ARPES) experiment, a wire mesh is typically placed in front of the charge-coupled device to block stray photoelectrons but could cause a gridlike structure in the spectra during quick measurement mode. In the past, this structure was often removed using the mathematical Fourier filtering method by erasing the periodic structure. However, this method may lead to information loss and vacancies in the spectra because the grid structure is not strictly linearly superimposed. Here, we propose a deep learning method to overcome this problem effectively. Our method takes advantage of the self-correlation information within the spectra themselves and can greatly optimize the quality of the spectra while removing the grid structure and noise simultaneously. It has the potential to be extended to all spectroscopic measurements to eliminate other extrinsic signals and enhance the spectral quality based on the self-correlation of the spectra solely.

Three-dimensional numerical modeling of geogrid reinforced foundations

A novel three-dimensional numerical model for the biaxial geogrid reinforcement is proposed in this paper that can incorporate the direction-dependent strength and stiffness properties and the actual grid-like structure of the geogrid reinforcement, which are crucial for soil-geogrid interactions. First, the numerical model consisting of cable structural elements is validated against wide width tensile test results reported in the literature. Next, the geogrid reinforcement model is validated with published experimental and numerical results on reinforced foundations. Two different cases, the bearing capacity of foundation in geogrid-reinforced soil and the uplift capacity of anchor plates in geogrid-reinforced sand, are selected from the literature for validation. The numerical results reasonably agree with the experimental results for both the bearing and uplift cases. The results indicate that the proposed approach can predict the behavior of geogrid reinforcement and can be used for modeling and analysis of other geogrid-reinforced structures.