Satellite remote sensing technology, leveraging its high spatial resolution and short revisit cycle, offers an effective approach for water quality monitoring. This study constructed a machine learning model based on Extreme Gradient Boosting (XGBoost) researching on reservoirs in Gu'an Hot Spring Park. Using domestic remote sensing images from the Gaofen (GF), Ziyuan (ZY), Beijing (BJ), and Jilin (JL) satellite series, the model retrieved key water quality parameters including chlorophyll-a, pH, ammonia nitrogen, and dissolved oxygen. The reflectance was derived from remote sensing images after preprocessing steps such as radiometric calibration and atmospheric correction, while synchronized measured data were obtained from buoy-based online monitoring stations. Band combinations with high correlation (coefficient>0.6) selected through Pearson correlation analysis were used as model inputs. The dataset was split into training, validation, and test sets in a 7:1:2 ratio, and model performance was evaluated using R², RMSE, and MAPE. The results demonstrated that the XGBoost model outperformed comparative models—Random Forest, AdaBoost, Deep Neural Networks, Convolutional Neural Networks, and Ordinary Linear Regression—in retrieving all four water quality parameters, exhibiting excellent fitting capability on the training set and strong generalization ability on the test set. High-spatial-resolution water quality distribution maps generated by the model revealed a spatial pattern of “poor at the edges and good in the center” within the study area, with water quality in the southern reservoir being superior to that in the northern reservoir. This research provides effective technical support for precise monitoring and management of water quality in small and medium-sized water bodies.

To scientifically evaluate the severity of dam-break consequences and determine risk management priorities, and a comprehensive assessment system for dam-break consequences and its corresponding indicator grading standards, based on grey system theory, fuzzy mathematics, and matter-element analysis theory, a grey-fuzzy matter-element analysis method applicable for prioritizing the consequences of dam-break events within dam groups was proposed. This method effectively integrated multi-dimensional evaluation indicators, including life loss, economic loss, environmental impact, and social impact. The severity levels of dam-break consequences across the dam group were classified through correlation degree analysis. Theoretical analysis and case study verification demonstrated that the proposed method could provide a scientific basis for decision-making regarding the reinforcement priorities for dangerous reservoirs.

With the advancement and implementation of China's “dual carbon goals”, the development of the electric vehicle (EV) industry has been rapidly progressing. Permanent magnet synchronous motor (PMSM) is widely used in the EV sector due to its high operational performance, high power density, and compact size. However, in automotive applications, factors such as limited installation space, poor heat dissipation conditions, complex operating conditions, and high-speed motor operation may lead to localized damage of the permanent magnets, resulting in partial demagnetization and degraded motor control performance. Therefore, researching, analyzing, and detecting localized demagnetization faults of permanent magnets is of significant importance. This paper takes a 10-pole PMSM as an example, replacing the magnet blocks to simulate localized demagnetization fault. Subsequently, a mathematical model of partial demagnetization is established, and theoretical analysis is conducted using Fourier transform to obtain fault characteristics during localized demagnetization. Finally, trial is conducted on a hardware-in-the-loop simulation platform to detect localized demagnetization faults, verifying the effectiveness and accuracy of the improved FFT method.

The correct activation and deactivation of hard pressure plates in relay protection devices is a crucial aspect of ensuring the safe and stable operation of the power grid. Addressing the issues of low efficiency, error-prone manual inspection, and inability to monitor in real-time associated with traditional manual inspection methods, a systematic solution for online monitoring of hard pressure plates has been proposed based on the technical plan of State Grid Jibei Electric Power Company. The research focuses on the acquisition principles, system architecture, and implementation strategies of two technical routes: “dual pressure plate” and “magnetic induction”. A hierarchical system architecture of “acquisition-aggregation-station control-master station” has been constructed. The technical solution is elaborated, which involves unified modeling of hard pressure plate status information based on the DL/T 860 standard and uploading it to the new generation of centralized control system and relay protection information master station through multiple paths. Application practice has proven that this solution can achieve real-time perception and remote monitoring of hard pressure plate status, significantly improving inspection efficiency. It provides a solid data foundation for applications such as error prevention in secondary equipment operation and remote intelligent inspection, significantly enhancing the level of intelligent operation and maintenance of substations.

To meet the demands of large open spaces and flexible layouts in residential buildings, the special-shaped column structure system has undergone significant development in China, from initial research to mature application. However, existing studies have mainly focused on reinforced concrete materials, with insufficient exploration of new composite special-shaped columns. Therefore, this paper proposes a new L-shaped aluminum alloy square tube-wood-concrete composite special-shaped column structure. Through axial compression tests, it is found that the failure mode is mainly strength failure. Then, based on the test results of the special-shaped columns, a finite element model is established for simulation analysis. Finally, the test results are compared with the finite element results to verify the accuracy of the finite element model. Using the verified finite element calculation model, the influence of the wall thickness and the slenderness ratio of the aluminum alloy square tube on the axial compression performance of the column is analyzed. The research results show that with the increase of the wood content, the bearing capacity first increases and then decreases, and when the wood content is 24%, the synergy effect of the three materials is better. The bearing capacity is the largest when the wall thickness of the aluminum alloy is 6mm, which is 21.9% higher than that of the specimen with a 3mm wall thickness. Compared with other parameters, the slenderness ratio has the greatest influence on the bearing capacity of the L-shaped composite special-shaped column.

A numerical simulation was conducted on 67 reinforced concrete columns, incorporating cementitious grout replacement, under small eccentric compression using ABAQUS finite element software. The study investigated the effects of replacement form, concrete strength, grout strength, and replacement rate on the bearing capacity of these columns. The results indicated that as the cementitious grout strength and replacement rate increased, the bearing capacity of the reinforced column also increased. However, as concrete strength increased, the rate of bearing capacity improvement gradually diminished, with a maximum increase of 13.6%. When the replacement thickness was held constant, applying four-side replacement reinforcement allowed the reinforcement material to provide additional constraints, and restricted the initiation and development of cracks, thereby enhancing the column’s bearing capacity. Additionally, when the replacement rate was constant, using the opposite-side replacement reinforcement method effectively increased the local stiffness of the column, resulting in a more uniform force distribution and improving its load-carrying capacity.

This research aims to explore the scientific application of waterproofing and seepage prevention technologies in construction projects, providing references for enhancing the durability and safety of buildings. Starting from the problems such as structural damage, functional failure and economic loss caused by building leakage, the necessity of applying this technology is clarified. By adopting the classified research method, the causes of leakage in areas prone to leakage such as basements, roofs, exterior walls, and kitchens and bathrooms are analyzed. It details the key points of suitable waterproofing and anti-seepage technologies, proposes solutions, and strictly controls details across design, material selection, construction, and management stages, emphasizing inspection and acceptance to prevent leakage risks. Based on engineering cases, it can be known that the reasonable application of this technology can reduce the leakage rate by more than 60% and significantly improve the structural stability. However, there are still problems such as uneven material quality, non-standard construction, and lack of later maintenance. Based on this, solutions such as optimizing material selection, strengthening construction control, and establishing maintenance mechanisms are proposed. At the same time, it is pointed out that the research does not cover the technical adaptability in special climate regions, and further research can be expanded in the future.

To uncover the mechanism underlying the influence of coarse particle content on the shear behavior of construction waste roadbed fillers, facilitate the high-efficiency utilization of construction waste, and advance green and sustainable development in the transportation sector, this study investigates the effects of varying coarse particle contents (40%, 60%, and 80%) on the mechanical behavior of construction waste roadbed fillers during concrete direct shear tests via Particle Flow Code (PFC2D) numerical simulation rooted in the discrete element method (DEM). The reliability of the numerical model is validated through comparison with experimental data obtained from indoor large-scale direct shear tests.Results demonstrate that both the peak strength and residual strength of the soil exhibit an increasing trend with the rise in coarse particle content, attaining their maximum values at a coarse particle content of 80%. Mesoscopic analysis reveals that changes in coarse particle content significantly influence the distribution of inter-particle contact force chains. This research indicates that coarse particle content ultimately governs the macroscopic mechanical response of the soil by regulating the transmission paths of the mesoscopic force chain network, thereby elucidating the mechanism by which coarse particle content affects the mechanical properties of the soil. The findings provide a reference for theoretical research and engineering practice concerning the engineering characteristics of coarse-grained soils.

In order to solve the problems of collapsibility and large deformation of highway loess roadbed, taking the project from Taiyangshan Development Zone to Pengyang (Ningxia-Gansu Border) of the Yin chuan-Kunming Express way as an example, based on the analysis of the engineering geological characteristics of collapsible loess roadbed in the field area, dynamic compaction method is applied to strengthen collapsible loess. Based on the static penetration test, the influence of the number of hits and the distance between ramming points on the reinforcement effect is evaluated. The combined method of field shallow plate load test and laboratory test is used to test the treated subgrade. The results show that when the number of tamping hits is 10, the strengthening strength and depth of soil are improved significantly. The strengthening point spacing has little effect on the strength of the center of the tamping point, and the strength of the other points increases with the decrease of the tamping point spacing. After treatment, the characteristic value of the loess subgrade bearing capacity increases between 96.3% and 128.8%, and the load settlement curve presents two stages of nonlinear accelerated deformation stage and failure stage, with no obvious proportion limit. The compressive modulus of treated loess increases by 40% on average, and the distribution of compressive modulus is relatively uniform in the sampling depth range, with the compressive modulus ranging from 12 to 16 MPa.

Combined with the actual construction project, this paper makes a statistical analysis on the surface diseases of K98+680-K151+208 of G0121 Beijing-Qinhuangdao Expressway in Tangshan City, Hebei Province, such as transverse cracks, longitudinal cracks, mapping cracks, small bare cracks and fatigue cracks, as well as the construction process, quality control points and main material technical indicators of 1 cm special modified asphalt binder micro-surface. The main technical points of 1 cm micro-surface material requirements, mix ratio design, milling, purging, viscous oil spreading, mixing, transportation, spreading, compaction, open traffic are summarized. The aim is to promote the application of 1cm special modified asphalt cement micro-cladding construction technology in highway repair and maintenance, improve the pavement damage index PCI index, further improve the pavement technical condition index PQI index, and provide construction reference and technical basis for the pavement repair construction of highway maintenance engineering in China.

At present, the supply and demand matching of scientific and technological achievements faces the problem of low accuracy. In order to effectively improve the transformation efficiency of scientific and technological achievements, a model of supply and demand matching of scientific and technological achievements based on ERNIE is constructed. First, the ERNIE model is used for in-depth knowledge mining of scientific and technological achievements and enterprise requirements. Word vector representation is input into Transformer structure to capture deep semantic information of text. Multi-head attention mechanism is added to enhance the model’s processing of complex features and significantly improve the accuracy of semantic understanding. Secondly, the semantic representation ability of the model is improved by adding global average pooling and multi-layer feedforward neural network. Finally, the Sentence-BERT model, twin network architecture and contrast loss function are introduced to optimize the sentence embedding representation, and the matching similarity is calculated with cosine similarity. According to the evaluation index, the training accuracy of the model is 2 percentage points higher than that of other models, and the actual accuracy, loss rate and F1 value are increased by nearly 1 percentage point, reaching 94.5%.

With the aging population increasing in China, the number of elderly people living alone has risen, highlighting home safety concerns. A multimodal home safety monitoring system based on YOLOv8-pose was proposed. The system integrated hardware, software, and server components to achieve comprehensive monitoring and health management for elderly individuals through multimodal data collection, model detection, and big data analysis. Hardware based on Raspberry Pi 4B was used for data acquisition. A management platform was built using a front-end and back-end separated architecture. The server-side deployed a posture detection model trained on YOLOv8-pose, enabling accurate behavior recognition and real-time alerts. A dataset of daily activity patterns was established using big data analysis techniques. When abnormalities were detected, alerts were sent to family members and community staff via a mini-program and web interface in no time, addressing emergency response challenges. Experiments showed that the human pose recognition accuracy of the detection model reached as high as 96.3%. The system also demonstrated excellent performance in overall cost control, providing practical references for the improvement of China’s smart elderly care system.

To address the efficiency and accuracy issues in complex data processing for college physics experiments, this study takes the measurement of sound velocity in air as a typical case and applies the principles of the standing wave method (resonance interference method) and the phase method. By utilizing python libraries such as NumPy, SciPy and Matplotlib, we realize automated experimental data processing, precise evaluation of uncertainty and visual analysis. This method not only guarantees the accuracy of results, but also reveals the inherent laws and statistical characteristics of data through intuitive graphs. It effectively improves students’ data processing capabilities, deepens their conceptual understanding of uncertainty, and provides a new technical approach for data analysis in physics experiments.