Network Structure Research Articles

Research on urban agglomeration spatial network structure in the middle reaches of the Yangtze River based on real-time traffic accessibility scenario analysis

ABSTRACT Investigating the spatial network structure of urban agglomerations based on traffic accessibility has an important practical significance in accelerating the process of regional integration. Taking urban agglomeration in the middle reaches of the Yangtze River (UAMRYR) as the case study, this study aims to examine the characteristics associated with the spatial network structure based on traffic accessibility and its changes under scenarios of different traffic accessibilities by adopting real-time traffic data, modified gravity model, and social network analysis (SNA). The main conclusions are as follows. Wuhan, Changsha, and Nanchang are the central cities in the spatial network; the spatial network structure of UAMRYR is imbalanced and in the agglomeration stage of regional economic development. With the development of traffic accessibility, resources first gather in the individual center cities and then spread to other cities, and the network tends toward equilibrium.

Cyanogel-Induced Facile Synthesis of Palladium Hydride for Electrocatalytic Oxygen Reduction.

Palladium hydride (PdHx) is one of the well-known electrocatalytic materials, yet its synthesis is still a challenge through an energy-efficient and straightforward method. Herein, we propose a new and facile cyanogel-assisted synthesis strategy for the preparation of PdH0.649 at a mild environment with NaBH4 as the hydrogen source. Unlike traditional inorganic Pd precursors, the unique Pd-CN-Pd bridge in Pdx[Pd(CN)4]y·aH2O cyanogel offers more favourable spatial sites for insertion of H atoms. The characteristic three-dimensional backbone of cyanogel also acts as a support scaffold resulting in the interconnected network structure of PdH0.649. Due to the incorporation of H atoms and interconnected network structure, the PdH0.649 achieves a high half-wave potential of 0.932 V, a high onset potential of 1.062 V, and a low activation energy, as well as a long-term lifetime for oxygen reduction reaction. Theoretical calculation demonstrates a downshift of the d-band centre of Pd in PdH0.649 owing to the dominant Pd-H incorporation that weakens the binding energies of the *OH intermediate species. Zn-air batteries (ZAB) based on PdH0.649 exhibits high power density, competitive open circuit voltage, and good stability, exceeding that of commercial Pt black.

Simultaneous optimization of network and fares for self-financing public transportation system

The public transportation system in densely populated areas is often managed as a self-financing system due to fare passenger revenues. In a self-financing system, the network structure and the monetary fare mutually interact; therefore, it is efficient to design both simultaneously. We developed an optimization model that endogenously determines the bus network and fares to maximize the total social surplus of the region. In this study, we conducted numerical applications of the model to a public transportation system around a railroad station in a metropolitan suburb area. The results showed that the optimal network topology changes with the scale of demand, and network shape significantly alters the optimal fare structure. Three types of virtual regions were examined to illustrate optimal fares.

The Structure of Lexical-Semantic Networks at Global and Local Levels: A Comparison between L1 and L2

The Structure of Lexical-Semantic Networks at Global and Local Levels: A Comparison between L1 and L2

Human-like intelligent automatic treatment planning of head and neck cancer radiation therapy.

Automatic treatment planning of radiation therapy (RT) is desired to ensure plan quality, improve planning efficiency, and reduce human errors. We have proposed an Intelligent Automatic Treatment Planning framework with a virtual treatment planner (VTP), an artificial intelligence robot built using deep reinforcement learning (DRL), autonomously operating a treatment planning system (TPS). This study extends our previous successes in relatively simple prostate cancer RT planning to head-and-neck (H&N) cancer, a more challenging context even for human planners due to multiple prescription levels, proximity of targets to critical organs, and tight dosimetric constraints.

We integrated VTP with a real clinical TPS to establish a fully automated planning workflow guided by VTP. This integration allowed direct model training and evaluation using the clinical TPS. We designed the VTP network structure to approach the decision-making process in RT planning in a hierarchical manner that mirrors human planners. The VTP network was trained via the Q-learning framework. To assess the effectiveness of VTP, we conducted a prospective evaluation in the 2023 Planning Challenge organized by the American Association of Medical Dosimetrists (AAMD). We extended our evaluation to include 20 clinical H&N cancer patients, comparing the plans generated by VTP against their clinical plans.

In the prospective evaluation for the AAMD Planning Challenge, VTP achieved a plan score of 139.08 in the initial phase evaluating plan quality, and 15 min of planning time with the first place ranking in the adaptive phase competing for planning efficiency while meeting all plan quality requirements. For clinical cases, VTP-generated plans achieved an average VTP score of 125.33±11.12, which outperformed the corresponding clinical plans with an average score of117.76±13.56.

We successfully integrated VTP with the clinical TPS to achieve a fully automated treatment planning workflow. The compelling performance of VTP demonstrated its potential in automating H&N RT planning.

AIGO-DTI: Predicting Drug-Target Interactions Based on Improved Drug Properties Combined with Adaptive Iterative Algorithms.

Artificial intelligence-based methods for predicting drug-target interactions (DTIs) aim to explore reliable drug candidate targets rapidly and cost-effectively to accelerate the drug development process. However, current methods are often limited by the topological regularities of drug molecules, making them difficult to generalize to a broader chemical space. Additionally, the use of similarity to measure DTI network links often introduces noise, leading to false DTI relationships and affecting the prediction accuracy. To address these issues, this study proposes an Adaptive Iterative Graph Optimization (AIGO)-DTI prediction framework. This framework integrates atomic cluster information and enhances molecular features through the design of functional group prompts and graph encoders, optimizing the construction of DTI association networks. Furthermore, the optimization of graph structure is transformed into a node similarity learning problem, utilizing multihead similarity metric functions to iteratively update the network structure to improve the quality of DTI information. Experimental results demonstrate the outstanding performance of AIGO-DTI on multiple public data sets and label reversal data sets. Case studies, molecular docking, and existing research validate its effectiveness and reliability. Overall, the method proposed in this study can construct comprehensive and reliable DTI association network information, providing new graphing and optimization strategies for DTI prediction, which contribute to efficient drug development and reduce target discovery costs.

Synergistic Engineering of Energy Band Alignment and Interfacial Electric Field Distribution over Bi-bismuth-Based Hetero-nanofibers for Boosting Visible-Light-Driven Photocatalytic Ammonia Synthesis and Antibiotic Removal.

Synergistic engineering of energy band alignment and interfacial electric field distribution is essential for photocatalyst design but is still challenging because of the limitation on refined regulation in the nanoscale. This study addresses the issue by employing surface modification and thermal-induced phase transformation in Bi2MoO6/BixOyIz hetero-nanofiber frameworks. The energy band alignment switches from a type-II interface to a Z-scheme contact with stronger redox potentials and inhibited electron traps, and the optimized built-in electric field distribution could be reached based on experimental and theoretical investigations. The engineered hetero-nanofibers exhibit outstanding visible-light-driven photocatalytic nitrogen reduction activity (605 μmol/g/h) and tetracycline hydrochloride removal rate (81.5% within 30 min), ranking them among the top-performing bismuth series materials. Furthermore, the photocatalysts show promise in activating advanced oxidants for efficient organic pollutant degradation. Moreover, the Bi2MoO6/Bi5O7I hetero-nanofibers possess good recycling stability owing to their three-dimensional network structure. This research offers valuable insights into heterojunction design for environmental remediation and industrial applications.

Research on disease diagnosis based on teacher-student network and Raman spectroscopy.

Diabetic nephropathy is a serious complication of diabetes, and primary Sjögren's syndrome is a disease that poses a major threat to women's health. Therefore, studying these two diseases is of practical significance. In the field of spectral analysis, although common Raman spectral feature selection models can effectively extract features, they have the problem of changing the characteristics of the original data. The teacher-student network combined with Raman spectroscopy can perform feature selection while retaining the original features, and transfer the performance of the complex deep neural network structure to another lightweight network structure model. This study selects five flow learning models as the teacher network, builds a neural network as the student network, uses multi-layer perceptron for classification, and selects the optimal features based on the evaluation indicators accuracy, precision, recall, and F1-score. After five-fold cross-validation, the research results show that in the diagnosis of diabetic nephropathy, the optimal accuracy rate can reach 98.3%, which is 14.02% higher than the existing research; in the diagnosis of primary Sjögren's syndrome, the optimal accuracy rate can be reached 100%, which is 10.48% higher than the existing research. This study proved the feasibility of Raman spectroscopy combined with teacher-student network in the field of disease diagnosis by producing good experimental results in the diagnosis of diabetic nephropathy and primary Sjögren's syndrome.

Lysine-Triggered Polymeric Hydrogels with Self-Adhesion, Stretchability, and Supportive Properties

Hydrogels, recognized for their flexibility and diverse characteristics, are extensively used in medical fields such as wearable sensors and soft robotics. However, many hydrogel sensors derived from biomaterials lack mechanical strength and fatigue resistance, emphasizing the necessity for enhanced formulations. In this work, we utilized acrylamide and polyacrylamide as the primary polymer network, incorporated chemically modified poly(ethylene glycol) (DF-PEG) as a physical crosslinker, and introduced varying amounts of methacrylated lysine (LysMA) to prepare a series of hydrogels. This formulation was labeled as poly(acrylamide)-DF-PEG-LysMA, abbreviated as pADLx, with x denoting the weight/volume percentage of LysMA. We observed that when the hydrogel contained 2.5% w/v LysMA (pADL2.5), compared to hydrogels without LysMA (pADL0), its stress increased by 642 ± 76%, strain increased by 1790 ± 95%, and toughness increased by 2037 ± 320%. Our speculation regarding the enhanced mechanical performance of the pADL2.5 hydrogel revolves around the synergistic effects arising from the co-polymerization of LysMA with acrylamide and the formation of multiple intermolecular hydrogen bonds within the network structures. Moreover, the acid, amine, and amide groups present in the LysMA molecules have proven to be instrumental contributors to the self-adhesion capability of the hydrogel. The validation of the pADL2.5 hydrogel’s exceptional mechanical properties through rigorous tensile tests further underscores its suitability for use in strain sensors. The outstanding stretchability, adhesive strength, and fatigue resistance demonstrated by this hydrogel affirm its potential as a key component in the development of robust and reliable strain sensors that fulfill practical requirements.

Deep Learning Model for Cosmetic Gel Classification Based on a Short-Time Fourier Transform and Spectrogram.

Cosmetics and topical medications, such as gels, foams, creams, and lotions, are viscoelastic substances that are applied to the skin or mucous membranes. The human perception of these materials is complex and involves multiple sensory modalities. Traditional panel-based sensory evaluations have limitations due to individual differences in sensory receptors and factors such as age, race, and gender. Therefore, this study proposes a deep-learning-based method for systematically analyzing and effectively identifying the physical properties of cosmetic gels. Time-series friction signals generated by rubbing the gels were measured. These signals were preprocessed through short-time Fourier transform (STFT) and continuous wavelet transform (CWT), respectively, and the frequency factors that change over time were distinguished and analyzed. The deep learning model employed a ResNet-based convolution neural network (CNN) structure with optimization achieved through a learning rate scheduler. The optimized STFT-based 2D CNN model outperforms the CWT-based 2D and 1D CNN models. The optimized STFT-based 2D CNN model also demonstrated robustness and reliability through k-fold cross-validation. This study suggests the potential for an innovative approach to replace traditional expert panel evaluations and objectively assess the user experience of cosmetics.

Semi-supervised method for visual detection of automotive paint defects

Automotive paint defect detection plays a crucial role in the automotive production process. Current research on visual defect detection methods is mainly based on supervised learning, which requires a large number of labeled image samples for model training. The labeling work is not only time consuming but also expensive, seriously hindering the testing and application of these models in practice. To address this issue, this study proposes a new method for automotive paint defect detection based on a semi-supervised training strategy. First, a semi-supervised automotive paint defect detection framework, which can use labeled and unlabeled samples to reduce the cost of data labeling effectively, is presented. Then, a spatial pyramid pooling fast external attention module that introduces an external attention mechanism is proposed to improve the traditional YOLOv7 network structure, called YOLOv7-EA, to obtain good detection performance. This network acts as a detector to generate high-quality pseudo labels for the unlabeled samples, providing additional data to train the model; meanwhile, it performs the final detection task. Lastly, a Wise-intersection over union loss function that considers the quality of the anchor box is introduced to reduce the interference of low-quality samples and improve the convergence speed and detection accuracy of the model. Using this method, we can accomplish the task of automotive paint defect detection with a small number of labeled image samples. Experimental results on the automotive paint defect dataset show that mean average precision (mAp)@.5, mAp@.75, and mAp@.5:.95 are superior to other methods under the condition of 10% and 15% labeled data, achieving good defect detection performance.

Smart Building Material with Near‐Infrared‐Triggered Reversible Color Switching

Smart materials and devices play an increasingly important role in improving modern life due to their richer functions, enhanced controllability, and improved biocompatibility. Herein, calcium silicate hydration (C–S–H) is integrated with layered [Co(NCS)2(pyrazine)2]n metal–organic frameworks (Co–MOF) and polymeric skeleton to fabricate the smart building material (M–C–S–H). C–S–H was anchored on the polymeric skeleton to reinforce the network structure for enhanced mechanical strength and toughness. Simultaneously, the highly porous skeleton structure enables the uniform distribution of layered Co–MOF, which could maintain its molecular form to endow the M–C–S–H with the photoresponsive color‐switching performance. The M–C–S–H as a smart building material exhibits the color change between light blue and black blue upon near‐infrared illumination on/off within a wide temperature range to achieve personalized wall and building decoration. Taking advantage of the low cost, rapid response, outstanding reversibility, and good color stability, M–C–S–H could provide lively, dynamic, and changeable decorating effects for walls and buildings.

Earthworm-Inspired Ultra-Durable Sliding Triboelectric Nanogenerator with Bionic Self-Replenishing Lubricating Property for Wind Energy Harvesting and Self-Powered Intelligent Sports Monitoring.

Triboelectric nanogenerators (TENGs), a promising strategy for harvesting distributed low-quality power sources, face inevitable bottlenecks regarding long-term abrasion and poor durability. Herein, both issues are addressed by selecting an earthworm-inspired self-replenishing bionic film (ERB) as the tribo-material of sliding-freestanding TENGs (SF-TENGs), it consists of an interconnected 3D porous network structure capable of storing and releasing lubricant under cyclic mechanical stimuli. Thanks to the superiority of self-replenishing property, there is no need for periodic replenishment and accurate content control of lubricant over the interfacial-lubricating SF-TENGs based on dense tribo-layers. Additionally, an SF-TENG based on ERB film (ERB-TENG) demonstrates remarkable output stability with only a slight attenuation of 1% after continuous operation for 100000 cycles. Moreover, the ERB-TENG displays a distinguished anti-wear property, exhibiting no distinct abrasion with an ultra-low coefficient of friction (0.077) and maintaining output stability over a prolonged period of 35 days. Furthermore, integration with an energy management circuit enables the ERB-TENG to achieve a 39-fold boost in charging speed. This work proposes a creative approach to enhance the durability and extend the lifespan of TENG devices, which is also successfully applied to wind energy harvesting and intelligent sports monitoring.

Production and characterization of bacterial cellulose by Rhizobium sp. isolated from bean root

Bacterial cellulose (BC) is a natural polymer renowned for its unique physicochemical and mechanical attributes, including notable water-holding capacity, crystallinity, and a pristine fiber network structure. While BC has broad applications spanning agriculture, industry, and medicine, its industrial utilization is hindered by production costs and yield limitations. In this study, Rhizobium sp. was isolated from bean roots and systematically assessed for BC synthesis under optimal conditions, with a comparative analysis against BC produced by Komagataeibacter hansenii. The study revealed that Rhizobium sp. exhibited optimal BC synthesis when supplied with a 1.5% glucose carbon source and a 0.15% yeast extract nitrogen source. Under static conditions at 30 °C and pH 6.5, the most favorable conditions for growth and BC production (2.5 g/L) were identified. Modifications were introduced using nisin to enhance BC properties, and the resulting BC-nisin composites were comprehensively characterized through various techniques, including FE-SEM, FTIR, porosity, swelling, filtration, and antibacterial activity assessments. The results demonstrated that BC produced by Rhizobium sp. displayed properties comparable to K. hansenii-produced BC. Furthermore, the BC-nisin composites exhibited remarkable inhibitory activity against Escherichia coli and Pseudomonas aeruginosa. This study contributes valuable insights into BC’s production, modification, and characterization utilizing Rhizobium sp., highlighting the exceptional properties that render it efficacious across diverse applications.

Predicting the properties of perovskite materials by improved compositionally restricted attention-based networks and explainable machine learning

Understanding the unique properties of perovskite materials is crucial in advancing solar energy technologies. Factors like heat of formation and bandgap significantly influence the light absorption capability and stability of perovskite solar cells. However, it is time-consuming and labor-intensive to obtain the properties of perovskites using traditional experimental or high-throughput computational methods. As a prospective method, machine learning can find regularities in the given training data and give accurate prediction results. In this article, we use deep learning models based on attention mechanisms and elemental features to predict the heat of formation and bandgap of perovskite materials. Random Forest and Gradient Boosted Regression Tree models have also been used for interpretable predictions of properties. The compositionally restricted attention-based network was improved by introducing a densely connected network and optimizing the network structure to increase data processing capabilities. The experiment results show that the mean absolute errors of the heat of formation and bandgap on the test sets are decreased by 5.77% and 3.37% respectively. The optimized model also shows better performance when used for classification tasks. In addition, we use the gradient boosting regression tree model and the shapley additive explanations tool to conduct an interpretable analysis, explaining the impact of different features on the predictions of the properties.

Threshold network GARCH model

Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its variations have been widely adopted in the study of financial volatilities, while the extension of GARCH‐type models to high‐dimensional data is always difficult because of over‐parameterization and computational complexity. In this article, we propose a multi‐variate GARCH‐type model that can simplify the parameterization by utilizing the network structure that can be appropriately specified for certain types of high‐dimensional data. The asymmetry in the dynamics of volatilities is also considered as our model adopts a threshold structure. To enable our model to handle data with extremely high dimension, we investigate the near‐epoch dependence (NED) of our model, and the asymptotic properties of our quasi‐maximum‐likelihood‐estimator (QMLE) are derived from the limit theorems for NED random fields. Simulations are conducted to test our theoretical results. At last we fit our model to log‐returns of four groups of stocks and the results indicate that bad news is not necessarily more influential on volatility if the network effects are considered.

Liquid‐Metal‐Loaded Hydrogel with Antibacterial Effect and Sustained Release of Growth Factor for Wound Healing

AbstractMultifunctional medical hydrogels play an important role in wound healing because of their excellent biocompatibility, convenient operation, and drug release capacity. So far, many efforts have been exploited to developing new‐generation hydrogels with distinctive capabilities in promoting wound healing. Herein, the liquid‐metal (LM)‐loaded hydrogels are reported with antibacterial effect under an 808 nm near‐infrared laser irradiation for wound healing therapeutic strategies. The addition of LM droplets enables the hydrogel to control the temperature rise and plays an antibacterial effect with the release of gallium3+ under laser irradiation. Benefiting from the porous network structure of hydrogels, the encapsulated growth factors can be released stably at the wound site. Under the synergistic effect of LM and growth factors in the wound tissue, the hydrogel displays antibacterial features and promotes the regeneration of epithelial tissue and neovascularization, thereby further accelerating wound healing. This hydrogel has proven biosafety and low toxicity in vitro and in vivo. With these advantages, such hydrogel is a promising candidate to be developed as a new medical product for future clinical medicine.

Team leadership and leadership roles

Although leadership theory recognises the importance of leaders in a team, research has mostly focused on various personal characteristics of leaders or leadership style, while less attention has been paid to the social relationships in which leaders are embedded. We therefore examine both internal and external social ties of leaders with the aim of identifying variations in the network patterns of team leaders. We hypothesise that leaders' relationship patterns lead to different network structures, which in turn create differences in leadership roles. By examining the social networks of team leaders from Slovenian and Finnish organisations between 2009 and 2012, we empirically identify variations in leaders' social networks. The results suggest four distinct social structures within which team leaders perform their leadership roles. We have shown that leadership roles can be distinguished from teams and team leaders based on the characteristics of social networks.

Enhancing Osteogenic Potential in Bone Tissue Engineering: Optimizing Pore Size in Alginate‐Gelatin Composite Hydrogels

Bone tissue engineering is a complex process where the scaffold plays a significant role as it contributes to phenotypically stable tissue formation and stem cell differentiation. These properties can be reached with a composite scaffold of alginate‐gelatin three‐dimensional network structure. The primary purpose of this study was to design a three‐dimensional and porous alginate‐gelatin hydrogel and evaluate the effects of pore size on cell morphology, cell adhesion, and proliferation under different osteogenic microenvironments. For this purpose, hydrogels with various concentrations of alginate and gelatin were prepared by mixing 4% (w/v) alginate and 6% gelatin (4A6G), or 3% (w/v) alginate and 5% (w/v) gelatin (3A5G) and cross‐linked with 2% (w/v) CaCl2. The pore size of these hydrogels was optimized using a very simple method based on different freezing and thawing cycles. The scanning electron microscopy (SEM) analysis showed porous structures with different pore sizes, ranging from 340 µm ± 30 µm with the 4A6G hydrogel to 635 µm ± 25 µm with the 3A5G hydrogel. The stiffness measurement showed a significant difference, with approximately 26.3 kPa ± 0.6 KPa for the 4A6G hydrogel and 21.6 kPa ± 0.2 KPa for the 3A5G hydrogel. Cell interaction with both types of hydrogel showed higher cell adhesion and proliferation rates in the hydrogel with larger pore size. Bone tissue formation, as evaluated by RT‐PCR, ALP (alkaline phosphatase) activity, and ARS (Alizarin Red S) staining, demonstrated strong osteogenic potential of the 3A5G hydrogel compared to the 4A6G. In summary, the 3A5G hydrogel (3% (w/v) alginate and 5% (w/v) gelatin) can be considered a potential material for bone tissue regeneration construction due to its biodegradability and favorable bone‐forming properties.This article is protected by copyright. All rights reserved.

ОДНОВИМІРНА ЗГОРТКОВА МОДЕЛЬ НЕЙРОННОЇ МЕРЕЖІ ДЛЯ ОБРОБЛЕННЯ СИГНАЛІВ З АМПЛІТУДНОЮ МОДУЛЯЦІЄЮ БАГАТЬОХ СКЛАДОВИХ

The processing of radio signals using artificial neural networks (ANNs) has great potential for research, which can be explained by the adaptability of ANNs to various transmission conditions and the ability to detect abstract patterns of changes in signal parameters. The article reviews the works of other authors devoted to different ways of using ANNs for processing radio signals. Taking into account the information in the reviewed works, the research task was formed, which consists in developing an optimized ANN model for radio signal processing. Signals with amplitude modulation of many components (AMMC) were chosen to form training samples for ANN. The choice of modulation type is justified by greater energy efficiency compared to other widely used digital modulation types, such as quadrature amplitude modulation. Mathematic basis of AMMC signal generation is described. The process of finding the coordinates of three component 8-AMMC signal constellation is explained, the formation of signals in the time plane based on the found coordinates is explained as well as their discretization and the addition of white noise. An iterative algorithm for generating initial data for ANN based on the described ratios is proposed. The general structure of one-dimensional convolutional neural network is considered. Functions of individual neurons, connections between them, the formation of layers and the convolution operation are described mathematically. On the basis of the previously given ratios, a final display of the network was formed. Specific dimensions and activation functions for layers are selected. The use of convolutional layers is justified by time invariance. Based on the reviewed mathematical models, selected activation functions and dimensions, a neural model was formed. The process of validating the effectiveness of the formed neural model is described, which is based on comparing the symbolic error probabilities of the proposed and reference models at different signal-to-noise ratios. The validation results are presented. The advantages of the obtained model over the previously proposed purely recurrent model and the AMMC reference receiver are explained.