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About the Journal
Governed by: Jiangsu Education Department
Sponsored by: National University
Published by: Editorial Office of Journal of Nantong University (Natural Science Edition)
Issues per year: 4
ISSN: 1673-2340
CN: 32-1755/N
Application of enzyme-responsive fluorescent probes in detecting drug-resistant bacteria
GUAN Jiayun;ZHANG Jingyi;WANG Li;YU Yanyang;WANG Qi;QIN Yuling;WU Li;With the overuse of clinical antibiotics, the types and varieties of drug-resistant bacteria are continually increasing. The timely and accurate identification of bacterial strains is crucial for the prevention and control of drug-resistant bacteria and the development of antibiotics. Fluorescent probes for detecting key bacterial enzyme metabolites,as a novel detection tool, can overcome the limitations of traditional detection methods and show great potential and advantages in detecting drug-resistant bacteria. This paper focuses on fluorescent probes for detecting key enzyme metabolites of clinically common drug-resistant bacteria, summarizing the applications and principles of enzyme-responsive fluorescent probe technology in detecting drug-resistant bacteria in recent years. The study reviews the relationship between bacterial resistance mechanisms and enzyme metabolites, highlighting the differences in enzyme metabolism between resistant and sensitive bacteria. Subsequently, based on the analysis of characteristic enzyme metabolites of various drug-resistant bacteria, the working principle of fluorescent probes for detecting drug-resistant bacteria is elucidated. Additionally, the study explores possible directions for optimizing fluorescent probes, providing new insights for faster and more accurate diagnosis and treatment of drug-resistant bacteria.
A prediction method for anti-cancer drug combinations synergy based on graph attention network
QIN Weiqi;BAO Xin;CHEN Xiao;QIU Jianlong;WANG Donglin;Affiliated Hospital of Shandong Medical College (Linyi Geriatric Hospital);Screening for synergistic anticancer drug combinations is essential for clinical treatment. However, the exponential rise in potential combinations renders traditional methods time-intensive and expensive, impeding the discovery of novel synergies. To overcome this, multi-scale feature fusion model based on graph attention network for anticancer synergistic drug combination prediction(MFGSynergy) is introduced, a graph attention network-based model to streamline anticancer drug combination screening. Initially, the model converts drug simplified molecular input line entry system(SMILES) into molecular graphs and fingerprint data while preprocessing cancer cell line data. It then employs a graph attention network(GAT) and multilayer perceptron(MLP) to extract features from both drug and cell line data, fusing these multi-source features to predict combination synergy. Evaluated on a public dataset, MFGSynergy outperforms Deep DDS, DeepSynergy, and six machine learning methods, achieving receiver operating characteri-stic area under the curve(ROC AUC), area under the precision-recall curve(PR AUC), accuracy(ACC), precision(PREC), true positive rate(TPR), and F1scores of 0.94, 0.94, 0.86, 0.87, 0.86, and 0.86, respectively, in five-fold cross-validation. Moreover, independent tests on unknown combinations validate its robust predictive power, underscoring MFGSynergy′s superior generalization.
Causal inference-based graph neural network method for predicting asphalt pavement performance
CHEN Kai;WANG Xiaohe;SHI Xinli;CAO Jinde;To enhance the prediction accuracy of asphalt pavement rutting, this study introduces an end-to-end multivariate time series prediction model that integrates graph neural networks(GNN) with causal inference methodologies.The proposed model aims to effectively capture long-term and short-term temporal patterns as well as interdependencies among multiple variables. The model comprises four modules: global feature extraction, local feature extraction,causal inference, and dual-channel graph convolution. The global feature extraction module employs attention mechanisms and gated recurrent units(GRU) to capture long-term temporal dependencies within variables. The local feature extraction module utilizes dilated convolutional neural networks(CNN) with various kernel sizes to extract short-term temporal patterns at different scales. In the causal inference module, relationships among variables are identified using transfer entropy based on information theory, resulting in a relationship coefficient matrix that quantifies complex dependencies among variables. The dual-channel graph convolution module extends traditional low-pass graph convolutional neural networks by integrating a high-pass filter, simultaneously capturing low-frequency and high-frequency components of node signals or features to potentially improve prediction accuracy. The proposed approach was evaluated using the RIOHTrack dataset from the Research Institute of Highway Track, with comparisons conducted against several benchmark models, including the classical statistical model VARIMA, shallow learning model SVR, deep learning model GRU, attention mechanism-enhanced GRU, and TE-GCN. Experimental results indicate that the proposed model achieves superior predictive performance across various categories of asphalt pavement structures. Compared to traditional statistical models, deep learning-based models are more effective and stable, and the GRU module enhanced with attention mechanisms can capture long-term dependencies, further enhancing predictive performance. Overall, the proposed model provides a potentially effective solution for predicting asphalt pavement rutting and may offer practical insights for future pavement structure design and maintenance planning aimed at extending pavement lifespan.
Analysis on spatial correlation characteristics of transportation carbon intensity of China based on complex network theory
XIE Qing;LIU Jiabao;It is of great significance to understand the variations and spatial correlation of carbon intensity in transportation industry for achieving the goals of energy conservation, emission reduction and "dual carbon". With the help of spatial econometric analysis and complex network theory, this paper quantifies the transportation carbon intensity of provinces in China from 2009 to 2021, analyzes its spatial and temporal characteristics by gini coefficient and kernel density estimation, and studies the structural evolution of spatial correlation networks for every provinces′ transportation carbon intensity by using improved gravity model and network characteristic indices. The results show that: 1)The carbon intensity of transportation in China is decreasing year by year, and the rate of decrease is fluctuating. 2)The unevenness of carbon intensity in transportation among various provinces is gradually decreasing, while the differences in carbon intensity within each region are distinct, with the greatest disparities occurring among the provinces in North China. 3) The spatial correlation of carbon intensity shows a complex network structure, and the center of the network gradually changes from Tianjin to Shanghai. 4) The eastern coastal regions are close to the center of this network, featuring complex relationships and abundant information resources. The results of this study can provide some references for competent departments, offering theoretical guidance for assessing the emission reduction intensity of various provinces and cities and for formulating targeted collaborative emission reduction plans.
A kind of observer-based consensus boundary coupling method for multi-agent systems
WAN Xinglong;YANG Chengdong;LI Zhengxing;ZHANG Ancai;QIU Jianlong;A spatial boundary coupling approach is developed for consensus control in multi-agent systems governed by partial differential equations, demonstrating enhanced flexibility and efficiency relative to conventional state coupling techniques. Two Neumann boundary coupling variants — isostatic and heterotopic — are analyzed, each facilitating distinct cooperative interactions among agents. A state observer is incorporated into the control framework to estimate system states, capturing dynamic behavior and attenuating the effects of information transmission delays. Lyapunov theory and integral inequalities are applied to establish exponential convergence of observation and consensus errors,yielding two sufficient conditions for achieving consensus under the respective coupling schemes. Validation is conducted through two examples, with experimental outcomes confirming effective consensus across both coupling configurations, highlighting the applicability of boundary coupling to multi-agent systems.
Event-triggered Containment Control of Multi-agent System under Multiple Attacks
BAO Yufan;CAO Yang;Multi-agent systems (MAS) have been widely used in many fields such as formation control and cooperative fault-tolerant control， and consensus problem has become a hot topic in MAS. Most of the research considers network attack models with single type of attacks， which can’t conform to practical scenarios. This paper aims to achieve the containment control of MAS under multiple network attacks. First， this paper introduces the telecommunication bandwidth and network attack problems faced by MAS and establishes a hybrid model of DoS attack and random False Data Injection attacks (FDI) for the sensor - to - controller (S/C) and controller - to - actuator (C/A) channels. Secondly， a distributed observer is designed to estimate agents’ state based on output signals， and an event-trigger mechanism is used to reduce the communication frequency. Thirdly， a stability analysis of the error system is conducted to prove that MAS under hybrid attacks can achieve containment control in different scenarios， it also reveals the influence of the duration and frequency of DoS attacks on the stability of MAS and determines the conditions for avoiding the Zeno phenomenon. Finally， the effectiveness of the control strategy is verified via simulation analysis.
Recent progress in circularly polarized organic light-emitting diodes based on chiral supramolecular assembly
CHU Xinpeng;ZHANG Yuxia;MA Yun;Circularly polarized organic light-emitting diodes (CP-OLEDs) have demonstrated significant application potential in areas such as 3D displays and optical information encryption， owing to their intrinsic capability to directly generate circularly polarized electroluminescence. However， achieving both high device efficiency and a high electroluminescent dissymmetry factor gEL remains a major challenge and a focal research topic in this field. With the rapid advancement of chiral supramolecular assembly， researchers have discovered that this system can construct long-range ordered helical nanostructures by precisely regulating intermolecular interactions， such as π-π stacking and hydrogen bonding. This approach facilitates the attainment of a high gEL value while maintaining favorable device efficiency. Consequently， chiral supramolecular assembly systems have emerged as one of the most effective strategies for realizing CP-OLEDs featuring both high device efficiency and elevated gEL values. Furthermore， the processes of chiral self-assembly or co-assembly frequently involve chiral transfer， chiral induction， and intermolecular F?rster resonance energy transfer. These processes are advantageous for inducing achiral dyes to achieve circularly polarized electroluminescence， thereby effectively reducing the synthetic complexity of chiral luminescent materials. This paper analyzes the influence of intermolecular forces on the chiral supramolecular assembly process from two perspectives: chiral supramolecular self-assembly and chiral supramolecular co-assembly. It investigates the role of highly ordered helical structures in the mechanisms of chirality transfer， chirality induction， and chirality amplification. Additionally， it summarizes the most recent research advancements in CP-OLEDs: Through precise regulation of intermolecular interactions such as π-π stacking and hydrogen bonding， chiral molecules can self-assemble into helical nanostructures or co-assemble with achiral luminophores to achieve chirality induction. Experiments demonstrate that the synergistic effect of lamellar ordering and energy transfer in chiral liquid crystal materials can significantly enhance device efficiency， and the helical pitch and polarization direction can be dynamically regulated by external fields. This review provides theoretical guidance for the future development of CP-OLEDs with both high gEL values and high efficiency.
Miniaturized phase shifter design using nematic liquid crystal
Lü Xianyang;DUAN Jinyuan;XIE Huilin;ZHU Jiaqi;Bassem MEDDEB;In order to address the limitations of traditional phase shifter designs， such as sophisticated circuit structures， high direct current power consumption， especially at high frequency band， the birefringence principle of liquid crystal materials was utilized and a miniaturized phase shifter design based on Nematic Liquid Crystal (NLC) material was proposed for operation at microwave frequency band. With the property of anisotropy， the axial orientation of liquid crystal molecules is initialized by using a thin film directional layer， and the long axial orientation of molecules is further modulated by applying bias voltages， hence tuning the permittivity of the material working as substrate. The local axial orientation of liquid crystal molecular clusters was successfully manipulated by varying external bias. We propose a novel transmission-line-based design framework employing liquid crystal materials exhibiting a dielectric anisotropy of Δε = 0.45. Three operational frequency band was observed with a maximum differential phase shift at the high frequency. When the operating frequency is 20 GHz， the phase shift range of the prototype reaches 33°. Results from measurement revealed the performance of the proposed design. Further optimization can be performed to expand range of the differential phase shift using the same manufacturing techniques.
Dual encoder-based reaction prediction for multilevel organic chemistry
ZHU Linxing;JIANG Shu;HUANG Jiashuang;DING Weiping;Prediction of forward reactions in organic chemistry as a critical Artificial Intelligence (AI) application has attracted more attention from researchers in recent years. The Simplified Molecular Input Line Entry System (SMILES) provides a method to linearize the chemical molecular formula. Thus， based on SMILES encoding， reaction prediction can be converted to the task of sequence-to-sequence generation in a neural machine translation (NMT). The traditional Transformer model focuses only on the inter-atomic attention weights in the chemical formula and ignores the global information within the molecule. Moreover， the traditional Transformer suffers from degradation of prediction and poor generalization when facing different representations of the same molecule. Addressing these points， we propose a reaction prediction method for multilevel organic chemistry based on dual encoders. Firstly， our model uses two encoders to process the information at the molecular level and the atomic level， respectively. Next， a molecular feature algorithm is proposed to obtain the interaction between molecules by averaging atom embeddings. Finally， a gating unit for automatic adjustment is utilized to perform multi-level feature fusion between the outputs of the atomic and molecular encoders. The fusion result is input to the decoder. Our results illustrate the method proposed obtains better results than the baseline on the three USPTO datasets and improves the generalization ability and long sequence processing ability of the model.
Human vision-inspired fuzzy convolutional neural network
GU Suhang;LI Chenyang;ZHU Peiyi;Convolutional neural networks (CNN) have demonstrated excellent data representation and classification capability in deep learning， and have achieved remarkable results in image classification. However， uncertainties in image data can propagate in the process of feature extraction in a layer-by-layer manner， which leads to the failure of accurate feature representation and affects the classification performance of the model. To address this problem， a human vision-inspired fuzzy convolutional neural network is proposed. The convolutional operation in the proposed network can mimic the processing mechanism of human vision， which consists of convolutional operations based on central vision and peripheral vision. The central vision-based convolutional operation can extract the main features of the target， while the peripheral vision-based convolutional operation can promote the representation ability of the target features by using fuzzy rule-based reasoning， which contributes to the accurate extraction of the target features. The analysis results show that the designed convolution operation based on peripheral vision is theoretically equivalent to NIN (Network In Network) and the interpretability of convolution process for the proposed model can be enhanced based on the fuzzy inference. The classification results on MNIST， 17flowers， and BreastMNIST datasets validate the effectiveness of the proposed method.
Application of Increasing PID Controlling Method in Temperature Controlling System
YAN Xiao-zhao,ZHANG Xing-guo(School of Mechanical Engineering,Nantong University,Nantong 226007,China)Temperature control is widely applied in scientific experiments and industrial processes.However,the temperature control system has characteristics of being nonlinear,time-varying and has hysteretic complicated large inertial system,and the effect of control is closely related to the algorithms adopted.In this paper,an experimental temperature control system is developed to meet the requirement of innovative ability training for mechatronic undergraduates.An increasing PID control algorithm is designed.The experimental results prove that the effect of the designed algorithm is better than the traditional PID algorithm.
Research and Development in Techniques of Dyeing Wastewater Treatment
JING Xiao-hui 1,YOUKe-fei 2,DING Xin-yu 2,CAI Zai-sheng 1(1.School of Chemistry and Chemical Engineering,Donghua University,Shanghai200051,China; 2.School of Chemistry and Chemical Engineering,Nantong University,Nantong226007,China)Reviewof the progress on treating methods of dyeing wastewater is presented,especially the advanced techniques are introduced,suchas membrane extraction,ultrasonic processes,high-energyphysical processes,advanced electrocatalytic oxidaˉtion processes and advanced photocatalytic oxidation processes.The treating trend for the dyeing wastewater is discussed.
The Application and the Development Foreground of Chitin and Chitosan
ZHANG Wei,LIN Hong,CHEN Yu-yue (School of Material Engineering,Soochow University,Suzhou 215021,China)The structure and the performance of chitin and chitosan are introduced and the application of chitin and chitosan in various fields is analyzed in this paper.The existing problems and the development foreground of chitin and chitosan are summarized in this paper.
Overview of SERS
LAN Yan-na,ZHOULing (Nantong Institute of Technology,Nantong226007,China)The principle of Raman spectrumis expounded first.Then the characteristics of SERS effect in experiment is summaˉrized and the mechanism of SERS is described.It's an accepted viewthat the mechanism of electromagnetic enhancement and the mechanismof chemical enhancement are both in existence.But which one is more important in different experiment depends on specific condition.
Application of ANSYS to Reinforced Concrete Beam
WANG Ya-ping 1,CHEN Jian-ping 2 ,CHEN Wu-zhou 3(1.Nantong Institute of Technology,Nantong226007,China;2.Nantong Architectural Design Institute of Industry,Nantong226001,China;3.Nantong Water Conservancy Construction Company,Nantong226005,China)In this article,taking features of reinforced concrete into account ,FEM software of ANSYS was used to calculate the beam' s deformation and the stress and strain of the normal section.At last,the answers to ANSYS(crack length,stress of reinforc-ing bar and concrete)and theoretical answers were compared in search of reasons and ways or measures that can amend it.
The Application and the Development Foreground of Chitin and Chitosan
ZHANG Wei,LIN Hong,CHEN Yu-yue (School of Material Engineering,Soochow University,Suzhou 215021,China)The structure and the performance of chitin and chitosan are introduced and the application of chitin and chitosan in various fields is analyzed in this paper.The existing problems and the development foreground of chitin and chitosan are summarized in this paper.
Application of Increasing PID Controlling Method in Temperature Controlling System
YAN Xiao-zhao,ZHANG Xing-guo(School of Mechanical Engineering,Nantong University,Nantong 226007,China)Temperature control is widely applied in scientific experiments and industrial processes.However,the temperature control system has characteristics of being nonlinear,time-varying and has hysteretic complicated large inertial system,and the effect of control is closely related to the algorithms adopted.In this paper,an experimental temperature control system is developed to meet the requirement of innovative ability training for mechatronic undergraduates.An increasing PID control algorithm is designed.The experimental results prove that the effect of the designed algorithm is better than the traditional PID algorithm.
Preparation of functionalized carbon nanomaterials and their energy storage applications
LI Qi;QIN Tian;GE Cunwang;Over the past decades, functional carbon nanomaterials(FCMs) have attracted much attention from the materials science community owning to their outstanding physical and chemical properties, such as high electronic conductivity/rapid mass transfer, plentiful active sites, good chemical stability, and robust mechanical stiffness. In view of the anisotropic and synergistic effects stemming from the functionalization as well as small size effect at the nanoscale,these multifunctional FCMs exhibit high potential especially in lithium-ion batteries, sodium-ion batteries, potassiumion batteries, lithium-sulfur batteries, organic solar cells, and supercapacitors. In this review, the functionalization strategies of carbon nanomaterials that have been developed over the last five years are comprehensively summarized and then application of FCMs in energy storage and conversion is introduced exhaustively. Finally, the pressing challenges and research directions are discussed according to the development trend.
Preparation of biochar and its application in environmental pollution management
WANG Jiayue;LING Qian;ZHANG Yunhao;WANG Xinyu;LIN Jiaqi;ZHANG Weitao;LIU Zhixin;WANG Xiangke;Biochar is a kind of environmentally friendly porous material which can be easily synthesized at low cost and in large scale. It has a wide range of applications in environmental pollution control and pollutants′ remediation and immobilization due to its large specific surface area and abundant surface functional groups. In this review, we mainly summarized the preparation of biochar, discussed the effect of preparation conditions on the properties of prepared biochar. The application of biochar in the removal of various pollutants from wastewater and soil improvement,the immobilization and elimination of different pollutants in soils, were reviewed in detail and the interaction mechanism was discussed. The removal of heavy metal ions was mainly attributed to the sorption of metal ions through the formation of surface complexes and part of metal ions could be reduced from high valence to low valence and then immobilized on biochar through adsorption-reduction-solidification strategy. The removal of organic pollutants from solution to biochar was mainly attributed to surface complexation, H bonding and π-π interaction on biochar surfaces.The organic pollutants could also be photocatalytic degraded by biochar or biochar-based materials under visible light irradiation. In conclusion, further research and discussion on the interaction mechanism of pollutant molecular with biochar at molecular level are helpful for the application of biochar in wastewater treatment and soil remediation. This review is of scientific significance for reducing the migration and transformation of pollutants in the environment and reducing the risk of environmental pollutants in the natural environment.
Modeling and simulation of proton exchange membrane electrolyzer system
WANG Huidong;YAO Haiyan;GUO Qiang;XIA Hongjun;Proton exchange membrane(PEM) electrolyzer converts electrical energy into chemical and heat energy,which is a green hydrogen production method, featuring fast response, high current density, compact structure, and other advantages. In the modeling of proton exchange membrane electrolysis water hydrogen production system,existing literature lacks a lumped parameter model that comprehensively describes the voltage and current changes of the electrolysis cell, as well as the temperature dynamics of each component of the system. This study establishes a steady-state voltage model of PEM electrolyzer and the thermal dynamic model of the system based on the basic principles of electrochemistry and the laws of thermodynamics. The simulation analysis was carried out based on MATLAB/Simulink software, and the simulation results were compared with the experimental data. The results showed that the voltage error is less than 0.02 V, and the temperature error is less than 1.6 K, which verifies the validity of the model. The established model can describe and predict the behavior of system parameters and provide support for system design and control. According to the efficiency model of PEM electrolyzer and the simulation results, the influence of different temperature and pressure on the performance of the electrolyzer was analyzed. It is concluded that increasing the temperature and decreasing the pressure can improve the efficiency of the electrolyzer, with temperature being the main factor. Using the simulation model, a feedforward PID controller was employed for temperature control, achieving an overshoot of less than 0.6 K and a settling time within 400 seconds. Comparison with a traditional PID controller demonstrates that the feedforward PID controller has advantages in terms of reduced overshoot and faster response.