Inherent Structure Research Articles

Laser-Induced and MOF-Derived Metal Oxide/Carbon Composite for Synergistically Improved Ethanol Sensing at Room temperature

HighlightsMetal oxide and carbon hybrids (MOx/C) were micropatterned very rapidly and energy efficiently by direct laser writing.Metal-organic framework was the ideal precursor for fabricating homogeneous MOx/C hybrids due to regularly spaced metal ions and organic ligands.The fabricated sensor not only demonstrated broad-range gas sensing capability for ethanol gas (170-3,400 ppm) but also exhibited exceptional sensitivity, rapid response and recovery, selectivity, linearity, and thermal stability.

Comparison of Graph Distance Measures for Movie Similarity Using a Multilayer Network Model.

Graph distance measures have emerged as an effective tool for evaluating the similarity or dissimilarity between graphs. Recently, there has been a growing trend in the application of movie networks to analyze and understand movie stories. Previous studies focused on computing the distance between individual characters in narratives and identifying the most important ones. Unlike previous techniques, which often relied on representing movie stories through single-layer networks based on characters or keywords, a new multilayer network model was developed to allow a more comprehensive representation of movie stories, including character, keyword, and location aspects. To assess the similarities among movie stories, we propose a methodology that utilizes a multilayer network model and layer-to-layer distance measures. We aim to quantify the similarity between movie networks by verifying two aspects: (i) regarding many components of the movie story and (ii) quantifying the distance between their corresponding movie networks. We tend to explore how five graph distance measures reveal the similarity between movie stories in two aspects: (i) finding the order of similarity among movies within the same genre, and (ii) classifying movie stories based on genre. We select movies from various genres: sci-fi, horror, romance, and comedy. We extract movie stories from movie scripts regarding character, keyword, and location entities to perform this. Then, we compute the distance between movie networks using different methods, such as the network portrait divergence, the network Laplacian spectra descriptor (NetLSD), the network embedding as matrix factorization (NetMF), the Laplacian spectra, and D-measure. The study shows the effectiveness of different methods for identifying similarities among various genres and classifying movies across different genres. The results suggest that the efficiency of an approach on a specific network type depends on its capacity to capture the inherent network structure of that type. We propose incorporating the approach into movie recommendation systems.

Structurally Modified Polysaccharides: Physicochemical Properties, Biological Activities, Structure-Activity Relationship, and Applications.

Polysaccharides are an important class of biomolecules derived from several sources. However, the inherent structure of polysaccharides prevents them from exhibiting favorable physicochemical properties, which restricts their development in agriculture, industry, food, and biomedicine. This paper systematically summarizes the changes in the primary and advanced structures of modified polysaccharides, and focuses on the effects of various modification methods on the hydrophobicity, rheological properties, emulsifying properties, antioxidant activity, hypoglycemic, and hypolipidemic activities of polysaccharides. Then there is a list the applications of modified polysaccharides in treating heavy metal pollutants, purifying water resources, improving beverage stability and bread quality, and precisely delivering the drug. When summarized and reviewed, the information above can shed further light on the relationship between polysaccharide structure and function. Determining the structure-activity relationship provides a scientific basis for the direction of molecular modifications of polysaccharides.

Lobster-Inspired Chitosan-Derived Adhesives with a Biomimetic Design.

Polysaccharide-based adhesives, especially chitosan (CS)-derived adhesives, serve as promising sustainable alternatives to traditional adhesives. However, most demonstrate a poor adhesive strength. Inspired by the inherent layered structure of marine arthropods (lobsters), a core-shell structure (SiO2-NH2@OPG) with amine-functionalized silica (SiO2-NH2) as the core and oxidized pyrogallol (OPG) as the shell is prepared in this study. The compound is blended with CS to produce a structural biomimetic wood adhesive (SiO2-NH2@OPG/CS) with excellent performance. In addition to thermocompressive curing, this adhesive exhibits a water-evaporation-induced curing behavior at room temperature. With reference to the design mechanism of the lobster cuticle, this microphase-separated structure consists of clustered nanofibers with varying amounts of SiO2-NH2@OPG particles between the fibers. This intriguing microphase structure and its mechanical effects could offer a powerful solution for improving the functional modification of wood composites.

Development of Quasi-Two-Dimensional Perovskites and Their Application in Light-Emitting Diodes.

Quasi-two-dimensional (quasi-2D) perovskites have attracted much attention due to their outstanding properties, such as inherent quantum-well structure, strong dielectric and quantum confinement, large exciton binding energy, and high photoluminescence quantum yield. By virtue of these superior merits, quasi-2D perovskites have shown great potential for next-generation light-emitting diodes (LEDs). Herein, this review presents an overview of the basic properties of quasi-2D perovskites and their photoluminescence modulations by large organic cation engineering, monovalent cation engineering, halogen engineering, defect passivation engineering, and dimensionality engineering. Furthermore, the strategies of charge-transport layer optimization, interfacial engineering, light-outcoupling efficiency improvement, and operating stability improvement are summarized for fabricating high-performance quasi-2D perovskite LEDs (PeLEDs). Finally, the challenges and outlook for the future development of quasi-2D PeLEDs are unambiguously proposed.

Effect of high moisture extrusion on the structure and physicochemical properties of Tartary buckwheat protein and its in vitro digestion

Tartary buckwheat is rich in nutrients and its protein supports numerous biological functions. However, the digestibility of Tartary buckwheat protein (TBP) poses a significant limitation owing to its inherent structure. This study aimed to assess the impact of high moisture extrusion (HME, 60 % moisture content) on the structural and physicochemical attributes, as well as the in vitro digestibility of TBP. Our results indicated that TBP exhibited unfolded and amorphous microstructures after HME. The protein molecular weight of TBP decreased after HME, and a greater degradation was observed at 70 °C than 100 °C. In particular, HME at 70 °C caused an almost complete disappearance of bands near 35 kDa compared with HME at 100 °C. In addition, compared with native TBP (NTBP, 44.53 µmol/g protein), TBP subjected to HME at 70 °C showed a lower disulfide bond (SS) content (42.67 µmol/g protein), whereas TBP subjected to HME at 100 °C demonstrated a higher SS content (45.70 µmol/g protein). These changes endowed TBP with good solubility (from 55.96 % to 83.31 % at pH 7), foaming ability (20.00 %–28.57 %), and surface hydrophobicity (8.34–23.07). Furthermore, the emulsifying activity (EA) and in vitro digestibility are closely related to SS content. Notably, extruded TBP (ETBP) obtained at 70 °C exhibited higher EA and digestibility than NTBP, whereas ETBP obtained at 100 °C showed the opposite trend. Consequently, HME (especially at 70 °C) demonstrated significant potential as a processing technique for improving the functional and digestive properties of TBP.

T[formula omitted]LR-Net: An unrolling network learning transformed tensor low-rank prior for dynamic MR image reconstruction

The tensor low-rank prior has attracted considerable attention in dynamic MR reconstruction. Tensor low-rank methods preserve the inherent high-dimensional structure of data, allowing for improved extraction and utilization of intrinsic low-rank characteristics. However, most current methods are still confined to utilizing low-rank structures either in the image domain or predefined transformed domains. Designing an optimal transformation adaptable to dynamic MRI reconstruction through manual efforts is inherently challenging. In this paper, we propose a deep unrolling network that utilizes the convolutional neural network (CNN) to adaptively learn the transformed domain for leveraging tensor low-rank priors. Under the supervised mechanism, the learning of the tensor low-rank domain is directly guided by the reconstruction accuracy. Specifically, we generalize the traditional t-SVD to a transformed version based on arbitrary high-dimensional unitary transformations and introduce a novel unitary transformed tensor nuclear norm (UTNN). Subsequently, we present a dynamic MRI reconstruction model based on UTNN and devise an efficient iterative optimization algorithm using ADMM, which is finally unfolded into the proposed T2LR-Net. Experiments on two dynamic cardiac MRI datasets demonstrate that T2LR-Net outperforms the state-of-the-art optimization-based and unrolling network-based methods.

Detecting Weak Underwater Targets Using Block Updating of Sparse and Structured Channel Impulse Responses

In this paper, we considered the real-time modeling of an underwater channel impulse response (CIR), exploiting the inherent structure and sparsity of such channels. Building on the recent development in the modeling of acoustic channels using a Kronecker structure, we approximated the CIR using a structured and sparse model, allowing for a computationally efficient sparse block-updating algorithm, which can track the time-varying CIR even in low signal-to-noise ratio (SNR) scenarios. The algorithm employs a conjugate gradient formulation, which enables a gradual refinement if the SNR is sufficiently high to allow for this. This was performed by gradually relaxing the assumed Kronecker structure, as well as the sparsity assumptions, if possible. The estimated CIR was further used to form a residual signal containing (primarily) information of the time-varying signal responses, thereby allowing for the detection of weak target signals. The proposed method was evaluated using both simulated and measured underwater signals, clearly illustrating the better performance of the proposed method.

Modulation of characteristic zones in NiTi alloys fabricated via wire arc additive manufacturing

The application of additive manufacturing (AM) is gradually extending to different industrial sectors. Most additively manufactured metallic components typically exhibit an inherent columnar grain structure, the control of which has been a challenging issue. In this study, the microstructures of the characteristic zones (heat-affected zone and transition zone) were controlled by adjusting the deposition strategy and arc mode during wire arc additive manufacturing (WAAM) of NiTi alloy. Compared to the sample fabricated by cold metal transfer (CMT) mode, the oscillating (O) sample shows a higher tensile strength of 793.6 MPa, while the pulsed (P) sample shows a better elongation of 5.8%. The high strength of the O samples is mainly due to the significantly lower aspect ratio of its columnar grains, while fine equiaxed grain structure in the transition zone of the P samples is responsible for the better plasticity. Different microstructures in the characteristic zones can be tailored by combining the forced melt flow, pulse frequency and undercooling, which provides vast freedom for the associated mechanical properties control. Our work provides a new strategy to regulate the microstructure and thus mechanical properties of additively manufactured NiTi alloys.

Manifold fitting with CycleGAN

Manifold fitting, which offers substantial potential for efficient and accurate modeling, poses a critical challenge in nonlinear data analysis. This study presents an approach that employs neural networks to fit the latent manifold. Leveraging the generative adversarial framework, this method learns smooth mappings between low-dimensional latent space and high-dimensional ambient space, echoing the Riemannian exponential and logarithmic maps. The well-trained neural networks provide estimations for the latent manifold, facilitate data projection onto the manifold, and even generate data points that reside directly within the manifold. Through an extensive series of simulation studies and real data experiments, we demonstrate the effectiveness and accuracy of our approach in capturing the inherent structure of the underlying manifold within the ambient space data. Notably, our method exceeds the computational efficiency limitations of previous approaches and offers control over the dimensionality and smoothness of the resulting manifold. This advancement holds significant potential in the fields of statistics and computer science. The seamless integration of powerful neural network architectures with generative adversarial techniques unlocks possibilities for manifold fitting, thereby enhancing data analysis. The implications of our findings span diverse applications, from dimensionality reduction and data visualization to generating authentic data. Collectively, our research paves the way for future advancements in nonlinear data analysis and offers a beacon for subsequent scholarly pursuits.

Neo4jsbml: import systems biology markup language data into the graph database Neo4j.

Systems Biology Markup Language (SBML) has emerged as a standard for representing biological models, facilitating model sharing and interoperability. It stores many types of data and complex relationships, complicating data management and analysis. Traditional database management systems struggle to effectively capture these complex networks of interactions within biological systems. Graph-oriented databases perform well in managing interactions between different entities. We present neo4jsbml, a new solution that bridges the gap between the Systems Biology Markup Language data and the Neo4j database, for storing, querying and analyzing data. The Systems Biology Markup Language organizes biological entities in a hierarchical structure, reflecting their interdependencies. The inherent graphical structure represents these hierarchical relationships, offering a natural and efficient means of navigating and exploring the model's components. Neo4j is an excellent solution for handling this type of data. By representing entities as nodes and their relationships as edges, Cypher, Neo4j's query language, efficiently traverses this type of graph representing complex biological networks. We have developed neo4jsbml, a Python library for importing Systems Biology Markup Language data into a Neo4j database using a user-defined schema. By leveraging Neo4j's graphical database technology, exploration of complex biological networks becomes intuitive and information retrieval efficient. Neo4jsbml is a tool designed to import Systems Biology Markup Language data into a Neo4j database. Only the desired data is loaded into the Neo4j database. neo4jsbml is user-friendly and can become a useful new companion for visualizing and analyzing metabolic models through the Neo4j graphical database. neo4jsbml is open source software and available at https://github.com/brsynth/neo4jsbml.

Atomistic insights into sluggish crystal growth in CoNi-containing multi-principal element alloys

Understanding the sluggish kinetics is of great significance for improving the properties of multi-principal element alloys (MPEAs). In this paper, the crystal growth in undercooled CoNi, CoNiFe and CoNiPd alloys was studied by molecular dynamics (MD) to show atomistic insights into sluggish crystal growth kinetics. The added Fe and Pd lead to a decrease in the crystal growth velocity and more significant sluggish crystal growth kinetics was observed in CoNiPd. After minimizing the instantaneous potential energy of atoms adjacent to the Solid/Liquid (S/L) interface, it was found that the decreased crystal growth velocity as the number of principal elements could be accounted by the accompanying change of inherent structure. The exploration of bulk undercooled liquid showed that the diffusion kinetic in liquid does not play a critical role on the sluggish crystal growth kinetics. Besides, the investigation of atomic structure in front of the smooth S/L interface revealed that the sluggish crystal growth kinetics induced by properties of element was associated with the atomic spontaneous ordering degree.

Suction and anisotropy effects on the stiffness characteristics of a compacted lateritic clay from small to large strains

Lateritic clay has distinct properties from other clays due to its high sesquioxide content. Its stiffness characteristics have not been well understood, especially when the soil is unsaturated and anisotropic. This study investigated the stiffness characteristics of compacted lateritic clay through suction-controlled triaxial compression tests equipped with local strain measurements. Both vertically and horizontally cut specimens were tested to determine the evolution of stiffness anisotropy during shearing. Three suctions (0, 10, and 150 kPa) and two confining pressures (50 and 200 kPa) were considered. When strains are relatively small (e.g., less than 0.2%), the secant Young's modulus Esec of vertical specimens is consistently higher than that of horizontal specimens at all suctions and stresses due to the inherent anisotropic structure. The degree of anisotropy increases with increasing suction since suction enhances the stiffness more significantly in vertical specimens than in horizontal specimens. This behaviour may be due to an enhanced force chain in the vertical direction during shearing. As strains increase, the degradation of Esec normalized by the maximum Young's modulus E0 is almost independent of suction and anisotropy. Lateritic clay has a higher degradation rate than other clays with a similar plasticity index because of its aggregated microstructure.

Proton-Conductive COF Evenly Embedded Cellulose Aerogels toward Water Harvesting and Spontaneous Sustained Power Generation from Ambient Moisture and Human Respiration.

Herein, we develop a new intelligent moisture-sensitive hybrid aerogel by evenly embedding a proton-conductive covalent organic framework (COF-2SO3H) into a carboxylated cellulose nanofiber network (CNF-C) for water harvesting and spontaneous sustained electricity production from ambient humidity and human respiration. Our strategy first exploits the "suspending agent" role of CNF-C to stably disperse COF materials in water for forming uniform hierarchical hybrid structures. By utilizing the synergy of COF-2SO3H and CNF-C together with their inherent structure merits and surface group effects, the hybrid aerogel displays increased water uptake and ion conductivity. Upon asymmetric moisturization, it can create a self-maintained moisture gradient to engender a concentration difference for mobile Na+ and H+, resulting in efficient charge separation and diffusion. Thus, the hybrid aerogel-based coin-type generator achieves a continuous output voltage of ∼0.55 V for at least 5 h in ambient environments in contrast to that using pure CNF-C and carbon-based generators with transient voltage response. Intriguingly, the wearable generator with an aerogel in a mask is more sensitive to human respiration and achieves repeatable and reliable self-charge for persistent electricity along with an increased output voltage of up to 1.0 V and much faster self-charge (only 3 min), both of which surpass most reported moisture-enabled generators.

An efficient method for generalised Wiener series estimation of nonlinear systems using Gaussian processes

System identification of dynamical systems aims to predict the output of a system for a given input by inferring model details from data. This is particularly challenging for nonlinear vibrating systems with long memory, which often have complex dynamics for which the physics is not fully understood. However, in many engineering applications, it is important to capture as much of the nonlinearity in the response as possible. The Wiener series is a classical data-driven technique that decomposes the system response into a set of orthogonal functionals of increasing order. Unlike standard black-box algorithms, such as neural networks, the series is highly interpretable and can offer insight into the nonlinearities present. However, current methods require vast quantities of data to calculate higher order terms in the Wiener series. In this paper, a novel generalised formulation of the Wiener series is developed by framing the series explicitly as a QR decomposition. Then by placing a Gaussian process prior over the kernels, their inherent structure is exploited to significantly reduce the quantity of data required for inference. Initial investigations were carried out using two systems: first pre-defined kernels and second an idealised nonlinear oscillatory system with unknown kernels. It is demonstrated that the Gaussian Process Generalised Wiener Series (GPGWS) enables the calculation of up to and including the third order kernel for systems with long memory and improves the robustness to noise.

Seasonal Autoregressive Integrated Moving Average (SARIMA) for Melon (Cucumis melo) Yield from 2011 to 2020 Based on Planting Date Period

The main goal of time series modelling is to collect and analyze past values to develop appropriate models that describe the inherent structure and characteristics of the series about the planting date of melon. The data used in this study are the melon yield based on twenty-nine days of planting date in March from the first to the last planting date of March 2011 to 2020, to evaluate the prediction performances of the models, two indices: the root mean squared error (RMSE) and mean absolute percentage error (MAPE) were used to compare the forecasting capabilities of the SARIMA models, Excel was used first for preparing as CSV file and then changed to time series data to make the dataset of yield and R version 4.0.2 (the R Development Core Team) was used to perform ETS and SARIMA models For planting date time series based on yield, seasonal autoregressive integral moving average models (SARIMA) were constructed. The Student's t-test was one of the statistical tests used to evaluate the effectiveness and validity of the SARIMA models Through the use of SARIMA models, it is feasible to create synthetic records that maintain the statistical properties of the historical record. Finally, the results can be applied to different planting dates based on yield. Based on the Bayesian Information Criteria (BIC) values and the overall highest R2 values of 0.94, the optimal SARIMA (0,1,1) model was chosen. No limitations were found. It is the first time that this mathematical method was used to analyze time series data for 10 years of planting date of melon and it could be useful for agronomists and horticulturists to choose the best model.

Fault diagnosis of rolling bearing based on the three-dimensional coupled periodic potential-induced stochastic resonance

Abstract Conventional bistable and monostable stochastic resonance (SR) methods exhibit certain limitations in their capacity to enhance and extract incipient characteristics. Firstly, the inherent potential function structure, characterized by a singular stable-state paradigm, proves inadequate in accommodating the heterogeneous and multifaceted condition monitoring signals. Secondly, the interconnected dynamic characteristics of the mechanical signals remain unaccounted for. Furthermore, conventional SR methods persist in utilizing a fixed constant as the critical system parameter, thereby neglecting the synergistic interaction among monitoring signals, potential function structures, and scale factors. Owing to the rich dynamic characteristics of the three-dimensional multi-stable coupled periodic potential SR system, it demonstrates superior noise utilization compared to monostable and bistable systems. In view of this, the present formulates a three-dimensional spatial model employing a coupled periodic potential model with nonlinear coupling. Subsequently, a pioneering method for diagnosing rolling bearing faults is introduced, utilizing the framework of three-dimensional multi-stable coupled periodic potential-induced SR. Simulation and experimental results illustrate that this approach effectively enhances and extracts the subtle fault characteristics of rolling bearings, ensuring a clear distinction between the spectral peak at the bearing fault characteristic frequency and the spectral peak originating from the interference noise.

An Adaptive Neural Network Regression Method for Structure Identification

This article reports a study on a flexible neural network regression method within the functional analysis of variance framework that aims to adapt to the underlying structure of the target function. We develop a novel penalization scheme where a concept of node impurity is introduced in the neural network framework. The node impurity in neural networks represents the homogeneity of the effects of the inputs on the node. We first define the effect of individual input on node and in turn, measure the node impurity based on the effects of inputs on node. We adopt the sum of node impurities as a penalty function whose usage makes the connections from inputs to nodes sparse, which improves estimation accuracy by reducing unnecessary complexity and enables data-adaptive structure identification. Our method takes into account of a large parameter space of the networks ranging from a fully-connected structure to sparsely connected structures. Among possible node connection structures, an optimal model is selected based purely on observed data. Numerical studies based on simulated and real datasets show that the proposed method performs well in identifying the inherent structure of the regression function and produces good estimation accuracy. Supplementary materials for this article are available online.

Tea Polyphenols Inhibit the Occurrence of Enzymatic Browning in Fresh-Cut Potatoes by Regulating Phenylpropanoid and ROS Metabolism.

During fresh-cut processing, potatoes lose their inherent protective cellular structure, leading to enzymatic browning that compromises sensory and edible quality. Tea polyphenols (TPs), natural preservatives with potent reducing properties, are hypothesized to impact this browning process. However, their influence and regulatory mechanism on the enzymatic browning of fresh-cut potatoes remain poorly understood. This study used the "Holland Seven" potato as the research material to explore the effects of a treatment with different TP concentrations (0.1 g L-1, 0.2 g L-1, and 0.3 g L-1) on the browning phenomenon and quality of fresh-cut potatoes during storage. The results showed that appropriate concentrations of TP treatment had a good preservation effect on the appearance and edible quality of fresh-cut potatoes. Furthermore, exogenous TP treatment reduced the content of enzymatic browning substrates (caffeic acid, p-coumaric acid, and ferulic acid) by regulating phenylpropanoid metabolism. Meanwhile, TP treatment augmented the activities of antioxidative enzymes (superoxide dismutase, catalase, ascorbate peroxidase, and glutathione reductase), maintained higher levels of ascorbic acid (Vc), and reduced glutathione (GSH). Consequently, the TP treatment could inhibit enzymatic browning by regulating reactive oxygen species (ROS) metabolism and the Vc-GSH cycle in fresh-cut potatoes.

The regulation of glucose and lipid metabolism through the interaction of dietary polyphenols and polysaccharides via the gut microbiota pathway.

The interaction of polyphenols-polysaccharides-gut microbiota to promote health benefits has become a hotspot and direction for precise dietary intervention strategies and foundational research in biomedicine. Both dietary polyphenols and polysaccharides possess biological activities that regulate body health. Single components, due to their inherent structure and physicochemical properties, have a low bioavailability, thus are unable to exert their optimal effects. The compound structure formed by the interaction of polyphenols and polysaccharides can enhance their functional properties, thereby more effectively promoting health benefits and preventing diseases. This review primarily focuses on the roles played by polyphenols and polysaccharides in regulating glucose and lipid metabolism, the improvement of glucose and lipid metabolism through the gut microbial pathway by polyphenols and polysaccharides, and the mechanisms by which polyphenols and polysaccharides interact to regulate glucose and lipid metabolism. A considerable amount of preliminary research has confirmed the regulatory effects of plant polyphenols and polysaccharides on glucose and lipid metabolism. However, studies on the combined effects and mechanisms of these two components are still very limited. This review aims to provide a reference for subsequent research on their interactions and changes in functional properties.