A new anomaly sound detection algorithm was studied that combines attention mechanisms and domain generalization techniques to more accurately identify normal and abnormal sounds in mechanical equipment. Specifically, two neural networks were jointly trained using a sub-cluster Adacos loss function, with features modeled by a Gaussian mixture model (GMM). Anomaly scores were calculated using negative log-likelihood values, and a 90th percentile threshold was set for detection. The algorithm demonstrated strong performance across seven types of machines, including fans and bearings, achieving harmonic mean AUC(area under curve) and pAUC values of 76.69% and 87.99%, with the highest performance observed on valve data. Compared to two baseline systems, the algorithm improved AUC and pAUC by 24.08% and 20.68%, respectively. Ablation studies further confirmed the positive impact of the GMM, attention mechanism, and Scadacos loss function. When tested against eight other algorithms on the same dataset, the proposed method showed a 4.16% improvement in the harmonic mean of AUC and pAUC, highlighting its significant advantage in anomaly sound detection tasks.

Landslides are significant geological hazard, with most events directly related to rainfall. Consequently, rainfall monitoring is critical for landslide forecasting and prediction. A self-powered rainfall sensor based on triboelectric nanogenerator (TENG) technology was developed for landslide monitoring. The sensor consists of a transmission unit and a sensing unit, which enable rainfall measurement and self-powering through rain-induced contact electrification of nanomaterials. Experimental results indicate that the sensor maintains a measurement error of 5% within the 0~300 mL/24 h range, with a linearity of 3.4%. It is capable of being operated in environments where the temperature is below 100 ℃ and the relative humidity is under 90%. Additionally, the output of the sensor can achieve a maximum power of 203.5 nW when an external load resistance of 7.8×10⁷ Ω is applied. The performance of the sensor can meet the practical requirements for field rainfall monitoring of landslides and also show potentials for the development of related rainfall monitoring technologies.

Natural cooling is one of the more energy-efficient and widely used cooling methods in data center air conditioning systems. Based on the climatic characteristics of Suzhou, Beijing, Guiyang, Guangzhou and Urumqi, the natural cooling system model of data centers was established by TRNSYS, and then the energy saving effect of data centers under the same chilled water supply and return temperatures in different regions was studied. The results show that when the chilled water supply/return temperatures are 15/22 ℃, the longest time for complete natural cooling is in Urumqi, accounting for 67.4% of the year, and the shortest time is in Guangzhou, accounting for 10.2% of the year; the longest time for complete mechanical cooling is in Guangzhou, accounting for 63.0% of the year, and the shortest time is in Urumqi, accounting for 4.3% of the year; the longest time for part of natural cooling is in Guiyang, accounting for 31.8% of the year, and the shortest time is in Beijing, accounting for 4.3% of the year. The longest part of natural cooling time is in Guiyang, accounting for 31.8% of the year, and the shortest is in Beijing, accounting for 18.6% of the year. At full load, the lowest annual average system power usage effectiveness (PUE) was in Urumqi at 1.227, and the highest PUE was in Guangzhou at 1.299. The average annual PUE of the five cities decreases as the load factor increases, and the effect of air conditioning load on the average annual PUE becomes smaller as the load factor increases. The findings provide theoretical support for guiding the application of natural cooling technology in data centers in different regions.

The U-shaped geothermal well, as a method for extracting medium-deep geothermal energy, is considered significant for enhancing the energy utilization efficiency of geothermal reservoirs. Geological and reservoir data from the Huangling area in Shaanxi Province were used to establish a numerical model of a U-shaped geothermal well with a depth of 3 500 meters. Field experiment cases were simulated and validated to investigate the heat transfer characteristics of U-shaped geothermal wells and the impact of related factors on thermal extraction efficiency. The effects of well type, reservoir geothermal gradient, and operational conditions on thermal extraction efficiency were analyzed, and the optimization of well type and parameters was further evaluated. The results indicate that U-shaped geothermal wells achieve higher thermal extraction efficiency compared to other types, particularly in areas with high geothermal gradients and depths exceeding 3 000 meters. Within the parameter range of this study (injection flow rates of 40, 60, 80, 100, and 120 m³/h, and injection temperatures of 20, 25, 30, 35, and 40 ℃), it was found that a larger temperature difference between the inlet and outlet fluids of the U-shaped geothermal well leads to a higher heat transfer rate. The optimal injection flow rate and temperature for U-shaped geothermal wells in the study area were determined to be 100 m³/h and 20 ℃, respectively. The selection of the injection flow rate should consider the requirements for outlet temperature, thermal extraction power, and the power consumption of the circulating water pump. The research findings are expected to provide guidance for optimizing medium-deep geothermal extraction in the region.

To enhance the support for the nuclear safety review of domestic advanced pressurized water reactor (PWR), the reactor coolant pump (RCP) shaft seizure accident for this reactor type was conducted. The impact of the loss of offsite power (LOOP) assumption and moderator temperature feedback on the accident consequences was examined. The findings are as follows. The peak pressure in the reactor coolant system and the peak temperature of the hot spot cladding, recorded during the accident process, met the accident acceptance criteria, enabling the nuclear power plant to eventually stabilize. The LOOP assumption was found to be conservative. For this accident, however, the differences arising from this assumption manifested in the later stages and did not influence key outcome parameters such as system pressure peak, minimum departure from nucleate boiling ratio (DNBR), and peak temperature of hot spot cladding, which occurred early in the accident. Therefore, the inclusion or exclusion of the LOOP assumption was deemed acceptable. The introduction of moderator temperature feedback was considered an unconservative assumption. Nevertheless, even without this feedback, the peak pressure in the reactor coolant system and the peak temperature of the hot spot cladding still met the accident acceptance criteria, maintaining a substantial margin. Additionally, incorporating moderator temperature feedback brought the calculations closer to reality. Overall, the introduction of moderator temperature feedback in this accident was also judged to be acceptable.

In order to strengthen the heat-mass transfer performance of humid air-spray water outside staggered tube bundles (STB), an analytical model was constructed for heat-mass transfer performance of humid air-spray evaporative cooling in staggered tube bundles based on the coupled method of DPM(discrete phase model) and Wall film model. The verification results show that the error was less than 1.1% for simulation and parameter results. Meanwhile, the influences were studied for three key structural parameters on heat-mass transfer performance. The results show that the heat transfer performance is improved between tube wall and spray water with the increase of longitudinal and transverse spacing of tube bundles. However, the mass transfer performance decreases of humid air-spray water with the increase of transverse spacing. Meanwhile, Nusselt number increases by 33.3% with the increase of longitudinal spacing from 30 to 70 mm, increases by 73.5% with the increase of transverse spacing from 10 to 50 mm. Besides, the heat transfer performance proves to be better when contact area increases between tube bundle and spray water with larger pipe diameter. At a certain transverse and longitudinal spacing, the lowest humid air temperature and highest enthalpy are located on the maximum pipe diameter (24 mm), and its decreasing and increasing degrees are 11.2% and 35.6%, respectively. The above results can provide a theoretical basis for optimizing the structure of staggered tube bundles and improving the heat-mass transfer efficiency.

As essential components in power conversion modules, rectifiers are extensively utilized in power supply systems such as inverters, where their operational reliability directly influences the overall system performance. In order to enhance the reliability of rectifiers, it is critical to conduct lifespan predictions for sensitive components, particularly rectifier diodes. A predictive model was proposed that employs an improved grey wolf optimization (GWO) algorithm to optimize the hyperparameters of a simple recurrent unit (SRU) network. Initially, a power cycling accelerated aging test was performed on the diode, followed by an analysis of its characteristic parameters, with forward voltage drop identified as the primary aging indicator. Subsequently, the improved GWO algorithm was applied to optimize SRU hyperparameters—such as learning rate, number of hidden layers, and iteration count—thereby establishing a hybrid predictive model. Finally, the model was trained and validated using aging test data, with predictive accuracy compared against alternative models. The results show that the proposed model achieves superior predictive accuracy, and the data-driven predictive approach enhances the precision of diode lifespan estimation compared to conventional analytical modeling methods, thereby contributing to enhanced operational reliability of rectifiers.

In the event of a public health emergency, the selection of the optimal distribution locations of daily necessities is crucial. Accordingly, the emergency distribution of daily necessities in Changchun City was taken as an example for empirical research and an optimization algorithm was proposed for the location of emergency supplies distribution. Initially, the virtual material warehouses, distribution candidate locations, and service coverage areas were determined by using K-means clustering and the Thiessen polygon method. Then, an optimization model was established with the goals of maximum coverage and minimum cost, and an improved genetic algorithm based on adaptive elite retention strategy was proposed to solve it. Empirical research had shown that the clustering algorithm could obtain 873 virtual material warehouses and distribution candidates, while Thiessen polygons could determine the reasonable coverage range of candidates, and improved genetic algorithms could obtain better 30 material warehouses and distribution locations. Data analysis had also found that there was a time lag between the distribution of supplies and effective control of infection. Therefore, once the rate of new additions exceeds the normal value, emergency supplies should be increased in advance to control further outbreaks of possible infections.The research results are beneficial for enhancing the ability of urban crisis management and emergency response, ensuring timely and efficient supply of daily necessities.

To improve the short-term prediction accuracy of photovoltaic power generation models with multiple input features, a photovoltaic power prediction ensemble model LGGWO-TCN-MHSA based on optimizing TCN hyperparameters was proposed. The model integrated the levy gold grey wolf optimization (LGGWO), temporal convolutional network (TCN), and multi-head self-attention mechanism (MHSA). First, the Spearman correlation coefficient method extracted the main features that significantly affect photovoltaic power, which were then fed into the TCN prediction model. Then, the proposed multi-strategy LGGWO was applied to the TCN for hyperparameter optimization, which improved the model's prediction performance. Finally, the predicted values were input into the multi-head self-attention model to further boost prediction accuracy. The experiment was verified using original Australian photovoltaic data. By comparing with six groups of models including convolutional neural networks (CNN) and long short-term memory neural networks (LSTM), the mean absolute error (MAE) and root mean square error (RMSE) of the proposed model on the test data set were reduced by 2.03%~82.0% and 10.5%~80.1%, respectively. The results show that the proposed method has high prediction accuracy and good stability.

Noise signals are added to the signal during transmission, resulting in changes in the amplitude of the signal, thus affecting the reception sensitivity. In order to study the relationship between noise and sensitivity, two signal-to-noise ratio calculation methods were used, and digital filtering, pulse peak extraction and half-amplitude point calculation were processed on the signals, which in turn complete the judgment and sensitivity calculation of DME(distance measuring equipment) signals, and based on which the relationship between DME judgment and the size of signal-to-noise ratio was analyzed. The results show that when the signal-to-noise ratio is small and the signal strength is sufficiently small, the accuracy of the DME judgment is greatly affected by the noise signal, which will lead to the reduction of the ranging accuracy.