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Self-healing materials can detect damage and repair it through intrinsic mechanisms， addressing critical issues such as performance degradation and shortened service life of polymer materials during operation. They represent a pivotal research direction in the field of polymer science. Self-healing technologies are broadly classified into two categories： extrinsic and intrinsic. Extrinsic self-healing relies on a synergistic carrier-healing agent-catalyst system. Although the healing process is straightforward， it suffers from inherent limitations including high raw material costs， complex fabrication processes， and limited healing cycles at the same damage site. In contrast， intrinsic self-healing constructs dynamic bond networks via rational molecular design， enabling repeated cyclic healing and overcoming the drawbacks of extrinsic approaches. Compared with rubbers and epoxy resins， polyurethane （PU） elastomers exhibit superior compatibility with dynamic bond networks due to their exceptional molecular designability， making them the preferred substrates for integrating self-healing functionality. Meanwhile， they combine high elasticity， excellent mechanical strength， and good weather resistance， and have found extensive applications in mechanical equipment， aerospace engineering， and other industrial sectors. Nevertheless， they remain susceptible to performance failure induced by external mechanical damage. This study uses a systematic analytical framework covering dynamic bond network design， performance regulation， and scenario adaptation. The self-healing mechanisms， synergistic effects， performance⁃optimization strategies， and application⁃scenario requirements of dynamic bond-based self-healing systems are also described. The applications of intrinsic self-healing PU elastomers in fields such as wearable electronics and adhesives are reviewed， and key challenges including the trade-off between conflicting properties and poor environmental adaptability are highlighted. This work aims to provide a valuable reference for the development of function-oriented self-healing PU elastomers.
Enhancing green practices and increasing fertilizer use efficiency are critical to addressing global food security challenges， driving increased research into coated fertilizers to sustainably boost crop productivity. A 15⁃year bibliometric analysis using CiteSpace was performed on Web of Science data to reveal research trends and hotspots. Publications on coated fertilizers have steadily increased （2010—2024）， led by institutions such as Shandong Agricultural University and the Chinese Academy of Sciences， across fields including agronomy and environmental science. Current research focuses on optimizing coatings to reduce costs and enhance biodegradability， with trends emphasizing efficiency， low carbon emissions， and intelligent delivery systems. Future research should emphasize long-term field trials， modeling， and environmental benefit assessment to support region-specific frameworks for emission reduction. This study provides a reference for future development directions of coated fertilizers.
As the core component of acid water treatment systems in the coal chemical industry， the operation stability of the condensate stripper is directly related to the safety of the device and the efficiency of resource recovery. In light of the serious corrosion problems in the tray and cylinder of a condensate stripper found after only one year of operation， the corrosion products were characterized by macroscopic and microscopic morphological observation， chemical composition analysis and phase composition tests. By combining these results with the physical properties of the reflux liquid of the stripping tower， a mechanism for tray corrosion failure is proposed. The results show that the corrosive medium in the reflux liquid is highly concentrated， with the concentration of cyanide ions being 1.78 mmol/L and the concentration of NH3-N reaching as high as 1 408 mmol/L. The trays have extensive perforations and dense corrosion pits， and the metal surface exhibits a typical scouring groove morphology. The corrosion products are mainly blue-green in color， and consist mainly of ferric ferrocyanide （Fe4［Fe（CN）6］3）. The main steps in the mechanism of tray corrosion failure were identified. The accumulation of an acidic medium caused by the reflux system leads to electrochemical corrosion， while the high-speed gas-liquid two-phase flow inside the tower continuously washes the metal surface. The combined effect of these two factors results in severe failure of the equipment within a short period of time. Based on the above analysis， targeted protective measures were proposed for process control and material protection. This work provides a reference for incorporating anti-corrosion features into similar equipment.
To meet the urgent demand for green economic reactions in the production of polyether polyols， a new type of stirred reactor with horizontal baffles has been developed. Using computational fluid dynamics （CFD） numerical simulation， the effects of varying the channel size δ of the horizontal baffles， the baffle number n， and the inlet flow rate Q A on the reactor flow field and residence time distribution （RTD） were studied. The results show that each impeller layer in the reactor can form an independent radial flow field， and the flow pattern in each chamber is the classic double circulation type. As δ decreases and n increases， the equivalent reactor number， N eq， gradually increases. When Q A increased from 0.011 m3/h to 5.65 m3/h， the normalized residence time θ remained stable， while N eq gradually increased and the rate of increase decreased. Under the optimized conditions of δ=30 mm， n=3， Q A=1.13 m3/h， the average residence time of the stirred reactor was 79.87 s， and N eq was 4.19.
The strict plate MESH model of the distillation column in the digital twin scenario suffers from problems such as large scale， sensitivity of convergence to initial values， and the need to reconstruct the equation system when switching operation regulations. In light of these problems， a modified AEM full⁃tower shortcut model （MAEM） including a condenser and a reboiler， along with its corresponding double⁃layer iterative flexible solution algorithm， is proposed based on the adapted Edmister model （AEM）. The MAEM model explicitly incorporates the strict MESH equations for the condenser， reboiler and feed plate， as well as the AEM model for the tower section， into a single equation set， effectively reducing the model size. In our double⁃layer solution algorithm， the inner layer updates the desorption factor and solves the material balance for the given tower plate temperature. The outer layer adjusts the tower plate temperature to meet any two specified requirements， thereby enhancing the numerical robustness under non⁃standard conditions. Using the aromatic hydrocarbon separation device， the wide boiling range system， and the gas fractionation process level device as typical examples， our MAEM model was compared with the original AEM model and the RadFrac strict model in Aspen Plus. The results show that the MAEM model can perform internal tower calculations by directly and flexibly setting unconventional operation parameters， while maintaining the same calculation accuracy and efficiency as the AEM model. Compared with the RadFrac model， the relative deviations of the key operational parameters of the MAEM model are all less than 5%， and the calculation time has been reduced by approximately 31.6% to 51%. Our MAEM model provides an efficient and robust framework for the deployment of digital twins in distillation processes， significantly enhancing the application potential of these digital twins.
Traditional liquid condensed⁃phase flame retardants struggle to prepare polyphenylene oxide/polystyrene （PPO/PS） composite foams with high expansion ratios and excellent flame-retardant properties. To address this problem this study investigates the effect of the synergistic flame-retardant mechanism of two solid phosphorus-based flame retardants on the flame-retardant performance of PPO/PS， fabricated using CO2/ethanol composite foaming technology. The results show that the phosphorus⁃based compound flame-retardant system exhibits a significant synergistic flame-retardant effect dominated by the gas phase and supplemented by the condensed phase. For unfoamed samples， the maximum limiting oxygen index （LOI） of the sample containing the compound flame retardant reaches 35.8%， which is 4.2% higher than that of the single flame-retardant system， whilst its heat release rate （HRR） remains relatively balanced. The compound flame retardant maintains moderate melt strength， enabling the preparation of composite foams with higher expansion ratios than those obtained with the single flame-retardant system via temperature-rise foaming technology. For a 12% flame retardant addition， the maximum expansion ratio of the compound flame⁃retardant system increased 16.8⁃fold. For foamed samples， the LOI values of composite foams with densities greater than 150 kg/m³ are all above 25%； the composite foam with a density of 300 kg/m³ passes the HF⁃1 grade in the horizontal burning test. Additionally， the compound flame retardant effectively reduces the HRR of the composite foam， thereby improving the dripping phenomenon.
In this study， 2‑methylimidazole and zinc nitrate hexahydrate were used as raw materials to prepare ZIF⁃8 at room temperature， followed by calcination to obtain micro/nano‑sized ZnO. The catalytic performance of the prepared ZnO in the degradation of rhodamine B （RhB） was investigated. The ZnO was doped with cerium in order to improve its photocatalytic capability. It was found that Ce doping slightly changes the band gap energy of ZnO. The factors affecting the photocatalytic activity of ZnO were systematically investigated， including the amount of doped Ce， the light source， pH and the substrate concentration. The experimental results show that the Ce‑doped ZnO exhibits the highest photocatalytic performance when the Ce dopant concentration is 5% of Zn， under xenon lamp irradiation， in a slightly alkaline solution with a relatively low substrate concentration.
Based on blood DNA methylation data obtained from the Illumina Methylation EPIC （850K） Bead Chip， this study aims to establish a highly stable and accurate age⁃prediction model has been developed using the principles of support vector regression （SVR） with a low-dimensional and high-correlation site selection approach. Four core CpG sites were selected through a systematic literature review and Pearson correlation analysis. The SVR model was developed in Matlab platform using the libsvm toolkit. To determine the optimal parameter combination， grid search combined with cross-validation was employed to optimize the penalty parameter C and the kernel parameter g. During data processing， DNA methylation β-values were normalized for model training and subsequently denormalized to obtain predicted age values. The results demonstrate that the constructed model achieved a coefficient of determination （R 2） of 0.885 29 and a mean absolute deviation （MAD） of 2.57 years on the test set， indicating high overall predictive accuracy. Although the prediction error increased with age—leading to a slight decrease in accuracy for older age groups—the error remained within an acceptable range. This study demonstrates that the SVR model can achieve high-precision age estimation using only four CpG sites， demonstrating robust generalization capability and practical utility. This method provides effective technical support for the physiological characterization of criminal suspects in forensic practice.
The textile and dyeing industry in the Yangtze River Delta region is highly concentrated and generates a large amount of wastewater， which increases the risk of illegal discharges. Therefore， accurately tracing the source of the dyeing wastewater has become an urgent need in the field of water environment governance and ecological protection. Aqueous fluorescence fingerprint technology enables rapid identification of the suspected pollution source by comparing the fingerprints of contaminated water samples with those of known pollution sources in the database. Its application potential in the field of water environment supervision is enormous. Taking the wastewater from two typical dyeing enterprises in the Yangtze River Delta region （dyeing wastewater 1 and 2） and the dye dispersant MF as the research objects， the aqueous fluorescence fingerprint patterns were determined， and the influence of environmental factors （pH and NO ） on the aqueous fluorescence fingerprint characteristics was investigated. The results show that in the aqueous fluorescence fingerprints of each sample， there are two fluorescence peaks， peak 1 and peak 2， whose ［excitation wavelength， emission wavelength］ positions are respectively located near ［280， 320］ nm and ［230， 340］ nm. In the three⁃dimensional fluorescence spectra of the dyeing wastewater 1 and 2， the C1 and C2 components may originate from the dispersant MF. The fluorescence intensity of peak 2 for wastewater 1 is more affected by pH and NO than that of the dispersant MF. This suggests that it may contain other fluorescent organic substances that are more sensitive to changes in environmental factors. The normal pH levels in surface water and the typical concentrations of NO in centralized surface water sources for drinking water have a relatively minor impact on the position of the fluorescence peaks， and do not affect the identification of the pollution source type. When the mass concentration of NO was 40 mg/L， the fluorescence intensity of peak 2 in the dyeing wastewater samples 1 and 2 decreased by 32.52% and 24.79%， respectively， compared to the case without adjusting the NO concentration， which might lead to an underestimation of the degree of pollution.
With the intelligent development of mechanical equipment， bearing fault diagnosis is facing more complex and changeable challenges. Traditional neural networks suffer from redundant features， limited feature recognition， and poor classification performance. To solve these problems， this paper proposes a SE-TCN⁃SVM bearing fault diagnosis model that combines a squeeze⁃and⁃excitation （SE） attention mechanism， a temporal convolutional network （TCN）， and a support vector machine （SVM）. By introducing the squeeze-and-excitation module， the model can adaptively enhance the weights of key channel features and optimize the modeling ability of TCN for the long-term dependence of vibration signals. The SVM classifier is used instead of Softmax to improve the robustness of sample classification by using its structural risk minimization characteristics. Experiments on bearing datasets from Case Western Reserve University and Jiangnan University show that the classification accuracy of SE⁃TCN-SVM reaches 98.92% and 96.88%， respectively. Compared with other benchmark models， it achieves better classification performance， faster training efficiency， and is suitable for different bearing data. This method enhances feature selection through the SE attention mechanism and improves generalization performance by combining SVM classifier. Our method provides a highly accurate， efficient， and adaptable solution for bearing fault diagnosis under complex working conditions.
To address the challenges of flow‑induced vibration monitoring and prevention in typical industrial pipelines， a method for predicting fatigue life based on the equivalent power spectral density is proposed. First， a fatigue monitoring model is established， and a pipeline vibration test bench is constructed through simulation. Using experimental results as examples， fatigue life estimates are calculated using both the time‑domain rain‑flow counting method and frequency‑domain methods， and their performances are compared. The results show that the frequency‑domain method based on equivalent acceleration power spectral density effectively reduces spectral leakage and provides better prediction of fatigue life for pipelines than traditional frequency‑domain approaches. On average， the prediction accuracy improves by 3.53%， and the mean prediction error is the lowest. This study offers an effective approach for predicting the remaining fatigue life of industrial pipelines and offers a novel method for fatigue damage assessment.
Web test case automatic repair technology plays a crucial role in the maintenance of web application test suites. As web applications are frequently updated， changes may introduce web elements in new versions， causing test cases to fail. A major issue with existing methods is the lack of effective representation of the test intent and analysis of the reasons for failure， resulting in a failure to guide and assist in test case repair at a macro level. This makes it impossible to effectively repair broken test cases caused by test flow changes or propagated breakages. Empirical analysis shows that test intent and causes of failure are very useful for test case repair. This paper proposes a test intent-driven web test repair approach named LetTe （guiding LLM to fix Web UI tests based on test intent）， which first parses the test intent and failure reasons of broken web test cases， and then guides the large language model （LLM） to fix them via prompt design and fine-tuning. LetTe’s repair logic simulates that of a human expert—given the test intent and reasons for failure， “think about” possible repair plans and then generates repair candidates via the corresponding chain-of-thought. This paper evaluates LetTe across seven Web applications collected from open-source websites and publicly available datasets. The experimental results show that our approach achieves a 75% correct repair rate， which is higher than that of all baseline methods.
Batch processes have multimode characteristics， and the existing methods of mode partition for batch processes ignore the causal relationship between the process characteristics and the mode centers， which directly affects the accuracy and reasonableness of the mode partition results and the prediction precision and generalizability of the models. A method for the online prediction of quality variables based on deep causal clustering-relevance vector machine （DCC-RVM） for multimode batch processes is proposed in this work. By combining density peak clustering， with the strong nonlinearity of the batch process data， the deep features of the process data are first extracted using a deep autoencoder. The process model for batch processes based on long short-term memory （LSTM） is then constructed， with the causal relationship between mode centers and the LSTM process model as a constraint， and the mode centers selection strategy is constructed to obtain reasonable mode centers； Subsequently， by considering the temporal features of the process data， the non-mode center samples are assigned to the corresponding modes based on the relative distance between samples， and the partition points and data sets are obtained for each mode； Finally， the quality variable prediction model for each mode is established using RVM， and the batch process quality variable is predicted online. The experimental results show that the mode partition by DCC is reasonable. The root mean square error （RMSE） and R 2 for predictiions using our DCC-RVM model are 0.010 4 and 0.999 5， respectively.
This paper investigates a sealed-bid auction model including a quadratic function form of the commission rate. Firstly， we provide the equilibrium bidding strategies for both first-price and second-price sealed-bid auctions. Secondly， we obtain the expected revenue of bidders and sellers for the two auction modes. Finally， we study the influence of the reserve price and the coefficient in the commission rate on the equilibrium bidding strategy， and the expected revenues of the bidder and the seller. The results indicate that the commission rate coefficient and reserve price have the same impact on the equilibrium bidding strategy and seller's expected revenue. Bidders are more willing to choose a first-price auction because their expected revenue is greater than that in a second-price auction. The commission rate in the form of a quadratic function proposed in this paper not only meets the requirements of real auctions but also has theoretical research value.
This paper investigates the Rotenberg equation with non-smooth boundary conditions in L 1 space. We prove that the main operator of the equation is densely defined and resolvent-positive. Furthermore， it is shown that the positive cone in the domain of its adjoint operator is cofinal within the positive cone of the entire adjoint space. Consequently， the spectral bound of the semigroup generated by the main operator is demonstrated to coincide with its growth order. Additionally， the continuous dependence of solutions on boundary parameters is rigorously established.
