Further analysis is needed to comprehend how the trend of land subsidence in the Beijing plain area evolves following the implementation of a series of prevention and control measures. Based on the Sentinel-1A image data from 2017 to 2022, the PS-InSAR technique was employed to assess the current situation of land subsidence in the plain area of Beijing, and the geographical detector was utilized to analyze the main influencing factors of land subsidence and their interaction effects. The findings reveal the following: The main conclusions were as follows. The distribution of land subsidence in Beijing plain is uneven, and the maximum subsidence rate reaches 90 mm/a. The subsidence rate of non-funnel area shows a certain degree of slowing trend from 2020 to 2021, while the slowing trend of subsidence rate in funnel area is not obvious. Groundwater as the primary influencing factor of land subsidence, with the thickness of the compressible layer closely following. The interaction among all influencing factors demonstrates a factor enhancement relationship, with the interaction between groundwater and subway infrastructure exerting the most significant impact on land subsidence. This highlights that groundwater and urban construction jointly propel land subsidence in the Beijing plain area. These research findings provide a scientific foundation for the comprehensive assessment, precise prediction, and integrated prevention and control of land subsidence in the Beijing plain.

Aiming at the U-turn scenario of autonomous vehicles in two-way single lanes, a safety decision-making method was proposed by fuzzy reasoning, and a U-turn mathematical model was established based on the spatial distribution relationship of vehicles, seven key control points were determined, the search strategy of particle swarm optimization was improved, and an efficient and comfortable U-turn trajectory planning method was proposed. The safety decision-making method firstly establishes a membership relationship between the relative distance between the vehicle and the vehicle on the target lane and the minimum safety distance during steering when making a U-turn, and selects the time with higher safety to make a U-turn. The trajectory planning method combines the spatial distribution characteristics of vehicles, improves the constraints of particle swarm optimization, and proposes a new search strategy, which can quickly converge to the optimal extreme value and plan the optimal path of U-turn. The results show that the proposed decision-making and trajectory planning methods can complete the U-turn safely and efficiently.

With the rapid development of the energy industry and technological innovation, a large number of professional terms and expressions are constantly updated, and new words continue to emerge. However, traditional neologism discovery methods often rely on dictionaries or rules, and it is difficult to efficiently process and update a large number of specialized terms, especially in the rapidly changing energy field. Therefore, combined with the characteristics of text data in the energy field, a new word discovery method in ENFM(energy field combining N-Gram and multiple attention mechanism) was proposed. Firstly, the N-Gram model was used to process the text data in the field of energy, and the candidate list of new words was generated by statistics and analysis of word frequency. Subsequently, the ERNIE-BiLSTM-CRF model integrating multiple attention mechanism was introduced to further improve the accuracy and efficiency of neologism discovery. Compared with the traditional neologism discovery technology, the accurate identification and overall efficiency of neologism have been significantly improved. The accuracy rate, recall rate and F₁ value of neologism in the data set of policy text in the energy field are 95.71%, 95.56% and 95.63%, respectively. The experimental results show that this method can accurately identify new words in a large number of text data in the field of energy, effectively identify the specific words and expressions in the field of energy, and significantly improve the recognition ability of professional terms in the field of energy in Chinese word segmentation tasks.

The variation law of C₄AF and C₃A corrosion products and the formation rate coefficient of CaCO₃ of cement single ore were quantitatively analyzed by SEM, XRD and TG analysis and test methods. The experimental results showed that both C₄AF and C₃A produced a large number of flocculent phases after CO₂ corrosion, but C₃A produced more lumpy and flocculent phases after corrosion than C₄AF corrosion. The relative crystallinity of C₃AH₆ decreases and the relative crystallinity of aragonite increases in the later stage of corrosion reaction, and the quantitative analysis results show that the content of CaCO₃ in C₄AF is higher than that of C₃A, and the molar formation rate of CaCO₃ in C₄AF is 28.36 mol/d and that of C₃A sample is only 4.23 mol/d after 1 day of corrosion reaction. With the extension of the corrosion reaction time to 28 days, the molar formation rate of corrosion products of C₄AF and C₃A continued to decrease, which was 1.83 mol/d and 1.48 mol/d, respectively. The coefficient of corrosion product formation α rate of C₄AF was 32.62 after fitting, which was much higher than that of C₃A single ore (2.74). The corrosion resistance of C₃A ore in CCUS environment is stronger than that of C₄AF, which not only provides theoretical guidance for the development of high performance cement materials resistant to CO₂ corrosion, but also provides a basis for the application of cement in CCUS environment.

The degradation of mechanical properties of PE pipe materials caused by hot oxygen aging will inevitably reduce the service life of the pipe, which is a serious hidden danger to social and economic development and people’s life and property safety. A nonlinear ultrasonic evaluation method for thermo-oxygen aging pipes was proposed. The nonlinear ultrasonic detection signals of PE pipes were extracted by building a nonlinear ultrasonic detection system for water immersion, and a correlation model between the characteristic parameters of nonlinear ultrasonic detection and elongation at break and fracture stress was established. The nonlinear ultrasonic detection mechanism was revealed by observing the micro-structure changes of PE pipes after aging. The experimental results show that the elongation at break and profit at break of PE pipe materials decrease with the increase of the nonlinear coefficient. The surface micro-cracks, holes and folds formed after the aging of the pipe are the main reasons for the mechanical degradation of the pipe and the increase of the nonlinear ultrasonic detection coefficient. The nonlinear coefficient can be used to evaluate the mechanical degradation of the pipe caused by thermal oxygen aging.

To evaluate the micro-structural degradation characteristics and strength attenuation law of multi source solid waste solidified lake sediment under freeze-thaw cycle conditions, the macroscopic engineering characteristics of the solidified sediment material under freeze-thaw cycle conditions, such as unconfined compressive strength, volume deformation and permeability coefficient were observed through multiple repeated freeze-thaw cycle tests on samples of granulated blast furnace slag, desulfurization gypsum, and construction waste co solidified sediment. By combining micro testing methods such as X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, energy spectrum analysis, then the mineral composition, functional groups, surface morphology and elemental composition of solidified sediment materials during freeze-thaw cycles were systematically analyzed, revealing the micro mechanism of structural degradation of solidified sediment under freeze-thaw cycles. The results show that with the increase of freeze-thaw cycles, fibrous ettringite and columnar gypsum and other cementitious products fracture and overlapped with each other to form a network, and the internal pores increased. This may be the reason for the decrease in strength and increase in permeability coefficient of the solidified sediment. The obtained mechanical characteristics and degradation patterns of solidified sediment during freeze-thaw cycles can provide basic data for the application and promotion of this material in regions with significant freeze-thaw cycle characteristics such as northwest and northeast of China.

The length of the impactor is generally about one meter according to the current application of all kinds of impactors.As the existing impactor increases the distance between the stabilizer and the drill bit, it will cause the theoretical build slope and the lateral force of the drill bit to decrease when it is used in connection with the drill bit. This will in turn affects the drilling deviation section.In this regard, the multi-dimensional impactor with built-in drill bit can effectively solve this problem.Firstly, in order to maximize the performance of the multi-dimensional impactor with built-in drill bit and reduce the pressure loss, the PB (Plackett-Burman) screening test design was adopted to conduct screening tests on the internal parameters of the impactor. The effect of each parameter on the tool performance was as follows: Jet channel width > inlet area > Outlet area > length of oscillating cavity > width of double feedback channel > curvature radius of wall attached surface > wedge Angle. Then, BBD (Box-Behnken design) response surface method was used to provide an in-depth analysis of the top three significant impact parameters.The optimized combination of the internal structure of the multi-dimensional impacter with built-in drill bit was obtained as follows: the inlet area is 1 203.416 mm², the jet channel width is 14 mm, and the outlet area is 455 mm². Finally, the effectiveness of the optimization method was verified by the simulation of Fluent software, which met the design requirements.

Under the background of the energy transition, energy storage, as an important technical means to support a high proportion of renewable energy access and consumption, has gradually become an indispensable part of virtual power plants. Among them, energy storage selection is a key issue to ensure the safe and stable operation of the power grid and improve the scheduling efficiency of virtual power plants. Therefore, an energy storage selection method based on game combination weighting and GRA-MARCOS was proposed for virtual power plants. Firstly, the evaluation indicator system of energy storage adaptability was constructed on the basis of considering the technical, economic, security, and environmental protection indicators of energy storage. Secondly, based on game theory, the comprehensive subjective and objective weights of the indicators were obtained by combining the subjective weights from FAHP(fuzzy analytic hierarchy process) method with the objective weights derived from the CRITIC and MEREC methods. Finally, the utility function of each alternative energy storage technology was calculated using the MARCOS method improved by GRA(grey relational analysis), and this was used to rank and make selection decisions for energy storage. The validity and robustness of the proposed energy storage selection method were verified through examples and sensitivity analysis. This method provides a reasonable and effective decision-making scheme for energy storage selection in different scenarios of virtual power plants.

To explore the impact of the community built environment on the walking time of elderly people, and considering gender differences among the elderly group, a CatBoost model was constructed and the SHAP (Shapley additive explanations) explanation method was integrated. The relative importance and nonlinear relationships of the community built environment features with the walking time of elderly people of different genders were comparatively analyzed. The study findings indicate that the community built environment variables have a more significant influence on the walking time of the elderly compared to personal socioeconomic attributes. However, the impact varies between genders. Compared to elderly males, elderly females pay more attention to built environment variables closely related to social needs, such as NDVI and population density. In contrast, the walking time of elderly males is more closely associated with personal socioeconomic attributes, often linked to transportation convenience and travel efficiency.

sEMG (surface electromyography) signals are physiological signal closely related to human movement, and the analysis of sEMG signals play an important role in the field of human-machine interaction. Aiming at the difficulty of both efficiency and accuracy of electromyographic signal classification, an upper limb sEMG classification method was innovatively proposed, which combined feature screening with classifier hyperparameter optimization. BPSO (binary particle swarm optimization) algorithm was adopted to screen the features. PSO (particle swarm optimization) algorithm was further utilized to adjust the hyperparameters of the LSSVM (least-squares support vector machine). By collecting sEMG signals from four parts of the human upper body and extracting 48-dimensional features from them, classification experiments were conducted on four common movements of upper limb. The results show that the BPSO-PSO-LSSVM algorithm retains only the 21-dimensional features of the EMG data, and the average classification accuracy obtained reaches 97.54%. It is proved that this method can effectively screen out the optimal combination of features for upper limb motion classification and improve the accuracy of movement classification.