A distributed blocking moving horizon estimation algorithm based on event-triggeredmechanism is proposed to address the problem of limited communication resources in the statemonitoring of renewable energy microgrid systems. Based on distributed moving horizonstimation, the algorithm incorporates an event-triggered mechanism to alleviate theommunication burden on the sensor network. Furthermore, the block concept is then applied todesign the disturbance sequences within the estimation window, which reduces the number ofoptimization variables and thus reduces the amount of online computation. Additionally, thefeasibility and stability of the algorithm under the maximum block length are analyzed, asufficient condition guaranteeing the existence of an equivalent solution to the optimizationproblem of the algorithm is established, and the results are generalized to the case of arbitraryblock of the disturbance sequence. Simulation results validate the effectiveness of the proposedalgorithm.

With the rapid development of the biopharmaceutical industry, the scale of animalexperiments and laboratory animal cage positions continues to reach new heights. The applicationof laboratory animal management systems is becoming inereasingly common. To ensure real-timedata in the system, the keeper needs to manually scan the QR codes of the cards on the cages atregular intervals. However, due to the large number of cage positions managed by the keeper.this task is time-consuming, laborious, and inefficient. To address this issue, the feedingmanagement module of a university's laboratory animal management system was taken as theresearch object. In this module, the photos of laboratory animal cage racks were taken, andintelligent recognition algorithms were used to locate and identify the cage positions and cages.To reduce the number of retakes of photos, this algorithm improved YOLOv8 to increase thepositioning accuracy and speed of cage positions and cages. After preprocessing and recognizingthe detected QR codes on cards, the usage details and location information of all cages were matchedto achieve batch recognition. Experimental results showed that the algorithm achieves a recognition rateof 95. 05%, and the recognition time is about 1/10 of the time required for manual processingmethods, significantly improving the work efficiency of keeper.

Alzheimer's disease is the most common neurodegenerative disorder, which typicallycauses irreversible damage to patients' memory, language, and cognitive abilities. Currently, thediagnosis of Alzheimer's disease mainly relies on medical examinations; however, these methodsare ineffective in preventing the disease. In the early stages, patients exhibit symptoms such asaphasia and reduced speech fluency, which provides a potential opportunity to use speechinformation for Alzheimer's disease recognition. To address the challenges in existing speechmodels, such as complex feature extraction and insufficient multimodal feature fusion, anAlzheimer's disease recognition model based on pre-training and feature fusion was proposed. Themodel uses fine-tuned pre-training models Wav2Vec 2.0 and BERT to extract speech and texteatures, respectively, and designs a multi-head attention-based feature fusion strategy toperform the multimodal feature fusion, ultimately achieving effective classification. Experimentalresults on two real-world datasets, NCMMSC2021 and ADReSSo, show that this model achievessignificant improvement in recognition accuracy compared to existing methods, demonstrating itssignificance for the early diagnosis and recognition of Alzheimer's disease patients.

To address the limitations of single-modal feature representation and the individualvariability of physiological signals, a multimodal recognition method fusing electroencephalogram(EEG) and eye tracking signals is proposed. To simulate real driving conditions, noise signals areinjected into the EEG and eye tracking signals. A cross-attention mechanism is employed to fusemultimodal information, while the domain generalization method DG-DANN and an encoder-decoder module are used to reconstruct userfeatures, enabling the model to learn discriminativefeatures independent of the individual experimental results demonstrate that the proposed methodachieves cross-userrecognition accuracies of 82.55% and 74.83% on a self-builtdataset and apublic dataset, respectively. Compared with single-modal models, the multimodal fusion modelshows accuracy improvements of 3.52% and 9.36%, validating the effectiveness of themultimodal fusion. Furthermore, validation experiments are conducted in real drivingenvironments, EEG and eye tracking data are collected while users perform predefined low,medium, and high cognitive load tasks during driving. Cognitive load levels are computedthroughout the driving sessions, further demonstrating the robustness and effectiveness of theproposed method in real driving scenarios.

The deterioration risk caused by concrete shrinkage is one of the key issues that need tobe addressed in concrete-filled steel tubes (CFSTs). By taking the Hongkou CFST Bridge as anexample, the particle swarm optimization-back propagation (PSO-BP) model of artificial neuralnetwork (ANN) was used to predict the self-shrinkage value of unconfined concrete beforeregulation in order to specify the expansion-compensated shrinkage regulation amount. Theresults show that the results predicted by the PSO-BP model are in good agreement with thosepractically measured, which can significantly reduce the experimental workload in expansiveagent proportioning. 9% expansive agent by mass of binder can effectively compensate for self-shrinkage before regulation, which can meet the requirements for shrinkage compensation inengineering applications. Through TGA and SEM analyses, the addition of expansive agentmakes concrete generate calcium alumina (AFt) and Ca(OH)₂ crystals at early ages, whichconstitute the intrinsic cause for generating slight expansion to compensate concrete shrinkage.

The river network in the eastern part of China is dense, and the dredging of riversproduces a large amount of silt every year. Silt has a high moisture content, low strength andhigh compressibility and cannot directly be applied to engineering construction. The traditionalcement curing method not only consumes a lot of resources but also causes serious pollution to theenvironment, which is not in line with the concept of energy conservation and emission reduction.Unburned geopolymer formed by industrial by-products or natural silicon-aluminum materialsunder the action of alkaline activators is a new green and low-carbon material with excellentphysical and mechanical properties and durability and has broad application prospects in the fieldof silt solidification. Based on the research results at home and abroad. this paper summarizes theresearch results on the mechanical properties and durability of alkali-activated geopolymersolidified silt and explores the factors affecting the properties and existing problems ofgeopolymer solidified silt from multiple perspectives in order to improve the application level ofgeopolymer in the field of silt solidification and help achieve the "double carbon" goal in China.

In recent years, with the rapid growth in demand for long-span bridges, a novelprefabricated ultra-high toughness composite bridge deck has emerged, utilizing ultra-hightoughness cementitious composite (UHTCC) as the surface layer and perfobond ribs (PBL) asshear connectors. Domestic scholars conducted experimental and theoretical research on itsflexural bearing capacity. However, existing theoretical models do not account for the localbuckling phenomenon observed in the steel compression zone during experiments. To addressthis, a theoretical calculation method for the flexural bearing capacity of box-type prefabricatedsteel-UHTCC composite bridge decks is proposed by considering elastic buckling effects. Inaddition, an existing finite element model is optimized and compared with the theoretical modeland experimental data for validation. The results indicate that the optimized finite element modelnot only improves computational efficiency but also ensures accuracy, aligning more closely withexperimental results. The theoretical model incorporating buckling effects can more preciselyevaluate the flexural bearing capacity of steel-UHTCC composite bridge decks, providing atheoretical foundation for the engineering application of this structure.

This study investigates the passivation and depassivation of steel bars in simulatedconcrete pore solutions under compressive stress. The passivation and depassivation behaviorunder sustained compressive loading were analyzed using electrochemical techniques includingopen circuit potential (OCP), linear polarization resistance (LPR), electrochemical impedancespectroscopy(EIS), and electrochemical noise(EN). The surface of passive films under differentcompressive stress conditions were characterized by scanning electron microscopy. Resultsdemonstrated that, in chloride-free concrete pore solution, steel subjected to low compressivestress rendered higher passive film resistance and higher charge transfer resistance, compared to the stress-free condition. However, as the compressive stress reaches the yielding state, the passivefilm resistance showed significant deterioration. Based on the microscopic characterization, wrinklingwas observed on the surface of passive film under low-level compression, while in the case ofyield-level compression, local delamination occurred. According to the electrochemical results andthe microscopic observation, this phenomenon is assumed to be caused by the "compaction effect"at lower compression levels that enhances passive film resistance, whereas higher stresses likelycause localized defects on passive film, thereby degrading the protectiveness. As such, in the caseof simultancous attack of compressive stress and chloride, the critical chloride concentration forsteel depassivation increased under relatively low compressive stress, whereas higher compressivestress reduced this threshold below the unstressed condition.

With concrete shrinkage tests and electric flux tests, the influence of stone powdercontent and methylene blue value on the drying shrinkage and resistance to chloride ionpenetration of manufactured sand conerete was analyzed, and the mechanism was studied incombination with pore structure analysis. The results show that as the stone powder contentincreases from 3% to 15%, the drying shrinkage of manufactured sand concrete continues todecrease, with a decrease of 22.5% to 26.7%. The effect of methylene blue value on the dryingshrinkage of manufactured sand concrete is not significant. With a higher stone powder content,the overall electrical flux of manufactured sand concrete reduces, and the resistance to chlorideion penetration is improved. The increase of methylene blue value leads to an increase in electricalflux and a decrease in resistance to chloride ion penetration of manufactured sand concrete. Whenthe stone powder content increases, the pore structure of the paste is refined. However, when themethylene blue value increases, the pore structure is roughened. Overall, when the methylene blue value is smaller than 1.4 g/kg and the stone powder content is between 10% and 15%, the dryingshrinkage of concrete is small and the resistance to chloride ion penetration is better.

Based on the state-space method and the Timoshenko beam theory, an analyticalmethod is proposed to analyze the static behavior of cracked cement concrete pavementstructures, Cracks in a pavement slab are modeled as three-directional linear springs inhorizontal, vertical, and rotational directions to account for the tensile-compressive, shear, andbending behaviors at the crack location. An improved Winkler foundation model is used tosimulate the interactions between the pavement slab and the subgrade soil, considering thevertical and horizontal friction resistance. Using a transfer matrix method and a joint couplingmethod, analytical solutions for the static responses of a cracked cement concrete pavement slabunder arbitrary horizontal and vertical loads are derived. The accuracy and validity of theproposed method are verified by comparing the results of this study with those of existingstudies. The effects of horizontal friction, horizontal force and bending stiffness of the spring atthe crack location on the static response of the pavement slab are further discussed. The resultsshow that the horizontal braking force causes the entire slab to rotate and that the horizontalfrictional effect and cracks significantly influence the responses of the slab, which cannot beneglected in the analysis. The proposed method can be applied to scenarios with arbitrary loadsand crack distributions.

Focuses on the wet washing treatment of secondary aluminum dross, aiming to optimizethe washing process parameters. The effects of washing time, washing temperature, solid-liquidratio, and washing speed on the removal rates of alkali metal elements (K and Na), as well as onthe alumina content, were investigated. The results indicated that when the washing temperaturewas 60 °C, washing time was 3 h, solid-liquid ratio is 1 : 3 g/mL, and rotay speed was 600 r/min, the removal rate of K and Na were optimal, reaching 50% and 70%, respectively, while thealumina content remained stable at 71%. Additionally, the F and CI contents in the secondaryaluminum dross after washing met the standards, with a CI removal rate as high as 99%.Furthermore, this study demonstrates that adding ammonium chloride to the washing solutioncan effectively inhibit the hydrolysis of aluminum nitride, while maintaining the removal rates ofK and Na, and significantly increasing the alumina content in the washed product to 76%. Thesefindings not only provide a reference for optimizing the process parameters of wet treatment ofsecondary aluminum dross but also offer scientific evidence for its harmless treatment andresource utilization, thereby enhancing the comprehensive utilization value of secondary aluminumdross.

Paclobutrazol residues in soil can change soil physicochemical properties and inhibit thegrowth of soil microorganisms and plants. Enzyme remediation has received widespread attentionin the removal of persistent organic pollutants due to its advantages such as environmentaladaptability and high stability. In this study, different enzymes were combined to investigatetheir degradation of PBZ in soil and further determine the optimal combination of enzymes and thebest reaction conditions. The results showed that laccase, amylase, peroxidase and lipase amongthe selected enzymes showed degradation ability against PBZ, with the degradation rates of35.47%，14.25%，24.51% and 11.80%, respectively. The best enzyme combination for PBZdegradation was selected as laccase + peroxidase by combining different enzymes, achieving acombined degradation efficiency of 65.06% and demonstrating synergistic degradation effects.The environmental factors affecting the degradation efficiency of enzymes were further optimized.and the highest degradation rate reached 88.42% under the conditions of 45% soil moisturecontent, soil pH of 5.5, temperature of 40 ℃ and air ventilation.

In order to investigate the characteristics and influencing factors of carbon dioxide(CO₂) exchange at the waterair interface of Shaoxing Lake in the Yangtze River Delta region.continuous multi-point monitoring of CO₂ exchange flux was conducted from May 2023 to April 2024 using the floating tank method in Yangjing Lake, Shaoxing City, Zhejiang Province. The characteristics and influencing factors of CO₂ exchange at the water-air interface of the lake were preliminarily analyzed. The results showed that the CO₂ exchange flux ranged from —35. 81 mg/(m²·h) to 128. 49 mg/(m²·h), with an average value of 21. 23 mg/(m²·h), indicating thatthe lake was overall source of atmospheric CO₂. The diurnal and seasonal variation characteristicsof CO₂ flux in the Lake were identified. Across different seasons, CO₂ flux showed a relativelyconsistent trend: gradually decreasing before noon and slowly increasing in the afternoon, mainlydue to the combined effects of aquatic plant photosynthesis and water temperature. Correlationanalysis between CO₂ flux and environmental factors in the lake revealed that flux was positivelycorrelated with temperature(T) and oxidation-reduction potential (ORP), negatively correlatedwith dissolved oxygen (DO) and pH, and showed no significant correlation with electricalconductivity(EC) or wind speed(WS).