China's deep coalbed methane resources have enormous potential. As a clean and high-quality energy, it is of great significance to ensure China's energy security and green and low-carbon development. The development of middle-shallow coalbed methane in China has basically been industrialized, but the development scale is far from the national planning goal. With the new progress made in the exploration and development of deep coalbed methane in recent years, it has provided new impetus for the rapid development of the coalbed methane industry. But when it comes to deep exploration and development, it faces a series of new geological, engineering, and other challenges that need to be tackled. In view of this, based on the latest national oil and gas resource evaluation data, the current situation of coalbed methane exploration and development in China was summarized, and the latest progress in deep coalbed methane exploration and development in typical blocks in China was evaluated. The new breakthroughs in geological evaluation, drilling, fracturing, and extraction of deep coalbed methane in recent years were summarized, and it is pointed out that there are problems in the current exploration and development of deep coalbed methane in China, such as low resource evaluation level, low adaptability of key technologies, and few mature demonstration projects that can be promoted and referenced.On this basis, four countermeasures and suggestions for the development of deep coalbed methane industry were proposed: strengthening resource exploration, strengthening key technical research, speeding up the construction of high-efficiency development demonstration zones, strengthening the co-exploration and co-exploitation of deep coal measures gas, so as to provide reference for promoting the exploration and development of deep coalbed methane in China.

The development of a high-grade waterway network in the Upper Yangtze River will play a crucial role in promoting the economic and social development in the Chengdu-Chongqing region. Waterway improvement through underwater blasting to removal the obstructing rocks is still one of the most effective method in mountainous river management, but its impact on aquatic organisms, particularly on passively drifting fish eggs and larvae, has raised significant ecological concerns. Long-term observational data of drifting larvae in the Upper Yangtze River were analyzed, focusing on their temporal distribution patterns and spatial variations in species composition and abundance. The propagation mechanisms of underwater blasting shock waves were examined, and the primary factors affecting shock wave intensity were identified. Current research on blast-induced fish damage was reviewed, with special attention paid to the effects of shock waves on eggs and larvae. Based on the documented drift patterns of eggs and larvae and the characteristics of shock waves, comprehensive ecological protection measures were proposed. These measures included temporal and spatial avoidance strategies, optimized blasting techniques, and the use of bubble curtain technology. These findings provide valuable insights for achieving sustainable development that balances waterway construction with ecological conservation in the Upper Yangtze River.

In order to investigate the heat and mass transfer of liquid droplet impinging on a heated surface with different wettability, VOF(volume of fluid) numerical simulation method was used to analyzed the mechanisms of wall wettability and surface temperature on droplet morphology and heat transfer characteristics. The results show that the hydrophilic wall is favorable for droplet spreading, while the hydrophobic wall is favorable for droplet rebound. With the increase of contact angle, the maximum spreading factor of droplets decreases, the time to reach the maximum spreading factor is shortened, and the average heat flux of the wall surface decreases. Surface temperature has less influence on the droplet spreading stage, with the increase of surface temperature, the droplet phase transition rate accelerates, the average heat flux of the wall surface increases, and the Leidenfrost phenomenon occurs when the surface temperature exceeds the critical temperature.

Clouds play a crucial role in the atmospheric dynamics of the Earth, and precise segmentation of ground-based cloud images is essential for improving the accuracy of weather forecasting. In response to issues such as varying data quality, low data volume, and different capture angles in existing open-source cloud image datasets, a labeled standard ground-based cloud image dataset (Cloud-GT) was constructed using manual annotation and transfer learning methods. The color channel component threshold segmentation method was employed to eliminate sunlight interference. Furthermore, an improved U2Net-based ground-based cloud image segmentation technique was proposed. The model introduced channel attention modules and depth-wise separable convolution modules in the feature extraction unit, which greatly reduces the network model parameters while improving the effective feature extraction of ground-based cloud maps within the network. Finally, comparing and analyzing the method with classical segmentation networks, experimental results indicated that the method achieved classification pixel accuracy, mean class pixel accuracy, average intersection over union, intersection over union, and F₁ score of 84.03%, 90.88%, 84.13%, 74.12%, and 89.59%, respectively. In comparison with U2Net, UNet, and FCN, the method demonstrated a significant improvement in performance. In conclusion, the method not only substantially reduced the model parameters but also effectively enhanced segmentation accuracy, which provides the possibility of practical application.

In the southern Great Xing'an Range, the Xishala area exposes a substantial amount of Mesozoic strata of Xinmin Formation (J₂x) and Manketouebo Formation (J₃m). However, due to the presence of rhyolite in the rock association, it is easy to confuse the stratigraphic division and lead to disputes of the stratigraphic attribution of rhyolite. Therefore, zircon U-Pb dating and geochemical characteristic analysis of the rhyolites were conducted, and stratigraphic sections of the Xinmin and Manketouebo Formations were compared to explore the formation age, tectonic setting, and stratigraphic attribution of the rhyolites. The results indicate that the geochemical characteristics of the riolites in Xishala area are high SiO₂ content (The value of mean is 75.74%.), rich alkali content [mean(Na₂O+K₂O)=7.69%] and low Mg and Ca content, belonging to peraluminous and high-K calc-alkaline series. The Chondrite-normalized REE pattern is right-dip type, with relative enrichment of LREE and relative depletion of HREE, the value of (La/Yb)_Nis between 10.61~14.21 and strong negative Eu anomaly (The value of δEu is between 0.44~0.53.). LILE(large ion lithophilic elements) such as Rb, Ba and K are relatively enriched, while HFSE(high field strength elements) such as Nb, Ta and Ti are relatively depleted, indicating that the magma originated from crustal materials. The LA-ICP-MS zircon U-Pb dating results show that the age of the rhyolite is (167.7±2.6) Ma, belonging to the Middle Jurassic, and it formes in the extensional tectonic action after the closure of the Mongolian-Okhotsk ocean. Based on the comparative study of regional geology and sectional rock assemblage types, the rhyolite belongs to the Manketouebo Formation.

The Kezirto pluton is located in the alkaline intrusive rock belt on the northern margin of the Tarim Basin. The lithology is alkaline granite. The petrogeochemical characteristics were studied in detail. The U-Pb isotope age of the pluton was measured by LA-ICP-MS method, which provides a basis for the study of the characteristics, age, genesis and formation environment of the pluton. At the same time, the nature of late Paleozoic magmatic activities and their tectonic settings in the area were studied, which can better guide the exploration work in the area and have important theoretical and practical significance. The Kezirto granite pluton was divided into two parts, north and south. The total rare earth element content is relatively high. The ∑REE of the northern pluton was 261.723×10^-6~834.783×10^-6, and the ∑REE of the southern pluton is 422.174×10^-6~575.86×10^-6. HFSE(high field strength elements) are obviously enriched relative to LILE(large ion lithophile elements), and elements such as Ba, Sr, P, and Ti are obviously depleted, with obvious negative Eu anomalies. The chondrite-normalized rare earth element diagram shows a slightly right-inclined seagull-like shape, and light rare earth elements are slightly enriched compared to heavy rare earth elements. The Kezirto pluton is A1-type granite. The LA-ICP-MS U-Pb isotope ages of the north and south plutons are (273.8±2.9) Ma and (274.8±1.8) Ma, which are contemporaneous plutons. The formation of the Kezirto pluton is due to the underplating of mantle-derived magma, which leads to the remelting of ancient rocks in the lower crust. After that, it undergoes fractional crystallization. During the formation process, it is contaminated by the crust to different degrees. The pluton has experienced the tectonic environment of deep mantle plume and intracontinental rift.

In recent years, important progress has been made in the lithologic trap exploration of the Lower Cretaceous Yageliemu Formation in the Yakela fault convex and its surrounding areas in the Tarim Basin, in order to clarify the sedimentary facies distribution law of the clastic rock reservoir of Yageliemu Formation in this area, and promote the efficient exploration and development of the clastic rock reservoir. Based on an integrated analysis of core samples, well logging data, and 3D seismic surveys, the sedimentary facies types and spatial distribution patterns of the Cretaceous Yageliemu Formation in the Yakela fault convex and its surrounding areas in the Tarim Basin were investigated. Furthermore, under the framework of source-to-sink system theory, the controlling effects of source area characteristics on fan delta development were systematically examined. The results show that fan delta group deposits are developed in the Yageliemu Formation in the Yakela fault convex and its surrounding areas. During the deposition period of the Yageliemu Formation, the ancient uplift in the Yakela fault convex area was obviously segmented, with a banded uplift in the NEE direction, with two bulges in the east and west, and a low terrain in the middle. Based on the analysis of source-sink system, it is clear that the sediment source of the fan delta group is from the weathered denudation area of the ancient uplift, and the multi-branch ancient gullies provide sediment transport channels for the fan delta Group. It can be seen that paleogeomorphology and gully development characteristics control the sour-sink system of Yageliemu formation in the Yakela fault convex and its surrounding areas, forming a sedimentary pattern with multiple sources supply. The western ancient uplift is mainly characterized by high uplift and large gully area,the eastern section is mainly characterized by low uplift and small gully area. The development scale of gullies in provenance area controlled the distribution scale of deltaic sediments around ancient uplift. The gentle slope fan delta sedimentary system developed in the south of the ancient uplift, and the steep slope and gentle slope fan delta sedimentary system developed in the north. The analysis of source-sink system reduces the uncertainty of sedimentary facies study of Yageliemu Formation and can provide more geological basis for oil and gas exploration.

Tahe Oilfield was one of the important oilfields in western China. The monitoring and development of remaining oil reserves were paid significant attention to and were regarded as one of the current challenges in oil and gas exploration and development. Well-to-surface time-frequency electromagnetic detection technology, due to its sensitivity to oil-gas-water interfaces, was increasingly applied in oilfield development. However, as time-frequency electromagnetic detection technology was introduced relatively late in oil and gas detection, the related detection mechanisms and high-resolution signal extraction and interpretation were still under research. Through the analysis of well-to-surface time-frequency electromagnetic exploration principles, a technical sequence for differential apparent resistivity, differential phase processing, and imaging analysis of well-to-surface time-frequency electromagnetic data was established, starting from the definition of regional apparent resistivity. The differential processing results of time-frequency electromagnetic data collected from the actual production well TX1 in Tahe Oilfield demonstrated that the high-density sampling provided by multi-pole transmission and multi-frequency reception of well-to-surface time-frequency electromagnetic detection technology offered high-resolution reservoir detection datasets. The new differential data processing technology could greatly enhance the effective identification capability of deep oil-gas-water interfaces. The successful trial of this method provided a promising technical means for oil-gas-water detection in oilfield development. Additionally, through comparative analysis with seismic exploration results, it was found that well-to-surface electromagnetic exploration had unique advantages in identifying complex underground structures and could provide complementary information to seismic exploration.

ABR(auditory brainstem response) is an objective method for detecting hearing loss, which is widely used in clinical practice, and its waveform characteristics are influenced by stimulus parameters. There are stimulus artifacts in ABR measured using unipolar stimulus, and alternating polarity is currently the only way to eliminate stimulus artifacts. However, considering the physiological differences in the effects of stimuli with different polarities on the auditory system, alternating polarity stimuli may lead to latency jitter in the induced ABR. Therefore, a new method was proposed to eliminate stimulus artifacts-the method of division and sum polarity, which first used positive and negative stimuli separately and then superimposed the two responses induced. The subjects with normal hearing were recruited, and their data that the click ABRs under four polarity ways (positive polarity, negative polarity, alternating polarity, division and sum polarity), as well as the tone-burst ABRs at five frequencies under two polarity ways (alternating polarity and division and sum polarity) were compared, with a focus on their waveform differentiation and latency differences. The results show that the difference in click ABRs under different polarity ways is insignificant, indicating that it is not sensitive to stimulus polarity. The waveform of low-frequency tone-burst ABRs is better under division and sum polarity than under alternating polarity, indicating that the low-frequency tone-burst ABR is more sensitive to stimulus polarity, and the sensitivity decreases with the increase of stimulus frequency. Based on the analysis of the above results, it is recommended to use unipolar stimulus for the click ABR, and the division and sum polarity method for the tone-burst ABR. The feasibility of the division and sum polarity method is validated in this study, which provides a new approach for eliminating stimulus artifacts when measuring evoked potentials.

In response to problems of rapid excavation of deep shafts, such as lining cracking and high support costs, based on the engineering background of -906~-1 158 m section of an overseas copper and gold mine, the support parameters of shaft were studied by theoretical calculations, numerical simulations and field tests. In order to restrict deformation of the shaft and reduce the cost of support reasonably and effectively, a shaft model was established based on the engineering practice, the stability of surrounding rock with the different parameters was analyzed by FLAC^3D numerical software combined with fluid-structure interaction. The results demonstrate that for the class Ⅲ surrounding rock, “anchor net spraying+steel fiber concrete” support is adopted, and its parameters are as follows: bolt diameter 22 mm, length 2.3 m, shotcrete thickness 50 mm, row spacing 1 m×1 m, steel fiber concrete thickness 550 mm. For the locally existing class Ⅳ~Ⅴ surrounding rock, “anchor net spray+foam board+steel fiber concrete” support was proposed, and the thickness of buffer layer foam board is 100 mm, and the thickness of steel fiber concrete is 600 mm. Field test results show that the average convergence rate of surrounding rock is 0.18~0.31 mm/d after 412 h excavation, which meets the requirements of air inlet shaft construction, and the construction efficiency is improved by about 23.5% compared with the domestic deep shaft. This work can provide a guidance for the support design of shaft in soft-fractured strata with water-rich.

In order to study the effect of different scanning strategies on laser deposition of nickel-based alloy matrix, the process of deposition of IN718 alloy powder on IN718 alloy matrix under four different scanning strategies was numerically simulated by ABAQUS software, and tested under the same conditions. The heat source verification results show that the heat source model is accurate and effective, and the numerical simulation process accords with the actual deposition process. The analysis results of temperature field, stress field and deformation field show that the thermal influence of different side scanning is less than that of same side scanning and reciprocating scanning is lower than that of unidirectional scanning, and the peak temperature is lower, thus the residual stress is lower, and the deformation degree of the matrix can be effectively reduced. The experimental results show that the numerical simulation process is accurate and effective. It is concluded that the residual stress value and deformation can be reduced effectively by using the reciprocating scanning method on different sides.

The general acidification technology of horizontal wells provides a simple and important method of stimulation for carbonate gas reservoirs. The common acidization methods were divided into two major categories: matrix acidification and fracturing acidification. To determine the best stimulation method of horizontal wells between acid fracturing and matrix acidizing, it is significant to achieve the efficient exploitation of reservoirs and optimization of process program. A dimensionless gas production index formula was obtained for horizontal well acidizing in carbonate gas reservoir. Also, a carbonate-acid-stimulation coefficient model was established to optimize the general method of acidification for the horizontal wells in carbonate gas reservoirs. Then, the influence of geological and engineering factors on the acidization increase coefficient was analyzed. Finally, field verification and application were conducted. The conclusions are as follows. When the carbonate-acid-stimulation coefficient R_JH>1, the productivity of matrix acidizing wells exceeds that of fractured acidizing wells. When R_JH<1, the productivity of fractured acidizing wells is higher compared to matrix acidizing wells. A higher permeability anisotropy coefficient increases, the number of fracturing fractures and the amount of acid fluid, but reduces the carbonate-acid-stimulation coefficient. The increase in porosity and horizontal length lead, to a higher carbonate-acid-stimulation coefficient. When the porosity of the gas reservoir falls below 7.3%, the productivity of the fractured acidizing well is improved. When the length of the horizontal section is 500 m and the number of fracturing fractures exceeds 4, the carbonate-acid-stimulation coefficient R_JH<1, and the productivity of the fractured acidizing well is improved. When the total volume of acid solution injected is 600 m³, the productivity of fractured acidizing well reaches a higher level than when the acid-solution volumetric dissolving power X>0.08.As verified in the carbonate reservoirs located in the northeastern Sichuan block, the production increase effect produced by using the carbonate-acid-stimulation coefficient method is significant after process optimization. Therefore, a theoretical guidance is provided in this paper for optimizing the acidification method of horizontal wells in carbonate gas reservoirs.

CO₂ flooding technology, recognized as a mature tertiary recovery method, is widely applied in complex small fault-block oilfields with strong heterogeneity. However, severe gas channeling is commonly observed during CO₂ flooding. As a result, the improvement in oil displacement efficiency remains low, typically below 10%. To address this, effective methods were explored to enhance oil displacement efficiency. Foam profile control and plugging were utilized as key techniques to achieve this enhancement.In the experiment, the JS oilfield was used as an example. The foam performance of the gas-soluble foaming plugging agent G-CF4 and the water-soluble foaming plugging agent W-CF1 was compared. The plugging agent with better foam performance was selected. Its plugging ability and oil displacement efficiency were tested.The results show that under target reservoir conditions, the optimal foaming plugging agent is 0.25% G-CF4.Moreover, the greater the permeability difference within the core combination, the stronger the plugging effect of G-CF4 in high-permeability cores.For a core combination with a permeability difference of 88 mD, the resistance coefficient of high-permeability cores is 2.5 times higher than that of a core combination with a permeability difference of 50 mD. In the core combination with 88 mD permeability difference, G-CF4 can maintain the resistance coefficient of high permeability cores above 9.2.The injection of 0.25% G-CF4 solution for 0.3 PV, followed by CO₂ flooding, improves oil displacement efficiency by 15% compared to CO₂ flooding alone.This study provides laboratory evidence supporting the optimization of foaming plugging technology in the JS oilfield.

The performance of supercritical multi-thermal fluid injection in heavy oil reservoirs is markedly superior to that of steam flooding. However, the underground seepage law of injecting supercritical multi-thermal fluid is not yet clear. Therefore, the “high-temperature and high-pressure steady-state method” was proposed to test the oil-water and oil-gas relative permeability curves at different temperatures. The viscosity of produced heavy oil and the contact angle between oil sand and water at different temperatures were tested, and finally, combined with the oil-water, oil-gas relative permeability and Stone-Ⅱ prediction model, the isoperms of oil phase relative permeability in different hot areas during three-phase seepage were obtained. The results show that after the action of supercritical water on heavy oil, the measured viscosity of produced heavy oil decreased by 31.03% at 50 ℃ compared to steam, and the contact angle between oil sand and water decreased from 139.5° to 100.9°, indicating that the wetting properties of the oil sand develop towards a water-wet direction. Compared with the relative permeability of oil phase, the relative permeability of water phase is very small, and the characteristic value of oil-water relative permeability curve changes gradually and then suddenly at supercritical temperature. The relative permeability of oil-gas increases gradually with the increase of temperature. In the isoperms of oil phase relative permeability, the area of the oil flow zone expanded as temperatures rose, and under supercritical conditions, the flow zone area grew to 54.59%, highlighting a significant enhancement in oil-phase flow capacity. The research results of this paper can provide theoretical basis for the seepage mechanism and numerical simulation of supercritical multi-thermal fluid injected in heavy oil reservoirs.

As a power drilling tool that can meet the needs of deep ground and high temperature drilling, the short life of thrust bearing is a key problem affecting drilling efficiency. Based on the CCD design method and finite element numerical simulation method, three sets of variables were selected as the design parameters, the orthogonal test between different structural parameters and abrasive wear life was carried out, and the optimal structural parameters of the bearing were determined by the range analysis method. The temperature-displacement coupling finite element model of bearing wear was established, and the influence of axial load, temperature and wear times on the wear depth of the bearing was studied considering the frictional heat generation of the bearing. The results show that the overall performance of the bearing with optimal structural parameters is improved, the stress concentration of the bearing is alleviated, the wear of the bearing is reduced, and the optimized structural life is increased by 4.6% compared with the initial life. This research method provides a reference value for the optimal design of this type of bearing. At the same time, the influence of different working conditions on bearing wear was analyzed. With the increase of axial load, the wear depth of the bearing increases. With the increase of the number of wear, the wear depth also shows an increasing trend. With the increase of bottom hole temperature, the wear depth decreases first and then increases gradually.

Filling phase change capsules in a container to form a packed bed heat storage unit is a typical applica-tion of phase change capsules. Phase change capsules are usually stacked in a specific layout in the packed bed flow channel. Studying the heat storage and release characteristics of a single phase change capsule in a packed bed flow channel can help optimize the design of a medium-temperature phase change heat storage system. Therefore, a two-dimensional packed bed numerical model of phase change cap-sules was established. The heat transfer and flow characteristics of the external heat transfer fluid flowing through the phase change capsules in the direction of gravity, counter gravity and vertical gravity were compared and studied. The effects of flow rate, temperature and capsule diameter on the melting process of phase change capsules were studied. The results show that the heat transfer rate of the windward side of the phase change capsule in the packed bed channel is faster. Due to the thermal resistance of the cavity air and the natural convection, the complete solidification time is the shortest when the heat transfer fluid flows countercurrently. Compared with the downstream flow, the complete solidification time of the up-stream flow is shortened by 8.9%. When the diameter of the phase change capsule is 12 mm, the melting speed of the phase change capsule with PTFE as the wall material in the center is 1.45% slower than that of the 304 stainless steel phase change capsule, and the average heat storage rate is 1.5% lower. The melting rate of the phase change capsule with modified PTFE as the wall material cavity in the center is 6.9% faster than that of the 304 stainless steel phase change capsule, and the average heat storage rate is 5.8% higher. Increasing the HTF inlet velocity and temperature can increase the average heat storage rate of the phase change capsule and shorten the melting time of the phase change capsule. The heat storage and release characteristics of the capsule have important guiding significance for the design optimization and practical application of the capsule monomer and the medium temperature phase change heat storage system.

Small sensors powered by wind-solar hybrid power supply do not require regular replacement of the power supply, and can be deployed in various remote areas. However, in practice, the photovoltaic panels are prone to accumulation of dirt, resulting in reduced power generation, and the rotating parts of small wind turbines are also very prone to failure. A wind-solar hybrid power device with a simple structure and self-cleaning capability was designed, and its solar and wind energy collection performance was experimentally evaluated based on meteorological statistical data. Moreover, the vibration cleaning efficacy of the photovoltaic panels under various dust coverage levels was compared. The research results show that the device can generate a maximum output power of 77.28 mW under simulated clear weather conditions (light intensity of 948.1 W/m²). The starting wind speed of wind energy collection based on piezoelectric and vortex-induced vibration effects is around 1.5 m/s, and a wind speed of 4.3 m/s in the experiments can generate an output power of 4.63 mW. After vortex-induced vibration cleaning of the photovoltaic module with different dust coverage densities, the output power of solar energy collection can be restored to over 84% of the clean state. This study presents a new technical solution for maintenance-free small-scale micro power generation equipment in remote areas.

It is essential to perform equipment reliability classification in order to devote limited resources to NPP equipment management reasonably, improve equipment reliability and availability while reducing maintenance workload and cost, and enable NPP’s safe, reliable, and economical operation. In light of engineering characteristics of the demonstration fast reactor and the challenges during the construction, an attainable new method was developed to standardize the process of equipment reliability classification and complete the classification of all systems. The differences and characteristics among the methods were compared, the implementation process of the new method was proposed and demonstrated with two real systems. The application shows that the proposed method is efficient, effective and rational, hence can offer to assist other NPPs of similar reactors to implement reliability classification during NPP construction.

Aiming at the problems of poor disturbance immunity and long dynamic response time of the grid-connected inverter based on VSG(virtual synchronous generator) control, an improved fuzzy adaptive control strategy for VSG was proposed. First, a small-signal model of VSG was established to analyze the effects of virtual inertia and damping coefficient on the dynamic response of the system. Determine the value range of the two parameters and use the sparrow search algorithm to optimize the initial values of inertia and damping in the adaptive strategy. Next, the angular frequency change curve of the system after perturbation was analyzed to refine the design fuzzy rules. Finally, a stand-alone VSG model was built in MATLAB/Simulink to compare the different control strategies. The results show that the fuzzy adaptive strategy proposed in this paper not only improves the response speed of the system, but also has a strong anti-disturbance ability when the command power and load power change suddenly. The effectiveness of this paper's strategy is proved.

A novel clustering approach combining Kmeans++ and PAM was introduced to segment the daily load curve chronologically for the dynamic reconfiguration of distribution networks incorporating time-varying wind solar power and loads. Multi-objective dynamic reconfiguration model of distribution network based on the optimal objectives of comprehensive cost, voltage offset and load balance. To enhance the computational efficiency of the model, an INOA(improved Nutcracker optimization algorithm) was proposed, which used Tent mapping+quasi-reflection learning to provide high-quality initial population. Dynamic fitness-distance balance selection method and tangential flight strategy were introduced to enhance the global search capability. The Cauchy-Gaussian variation perturbation was incorporated to augment the algorithm’s capability to escape from local optima. Using the IEEE 33-node system as a basis, the outcomes indicate that the suggested approach effectively achieves optimal load distribution and efficiently addresses the restructured model.

In order to study the prediction and health management of PEMFCs(proton exchange membrane fuel cells) for vehicles, a method combining GWO(grey wolf optimizer) and RBF(radial basis function) neural network with relative power loss rate as a health indicator was proposed to predict the remaining useful life of vehicular PEMFCs. Firstly, by analyzing the polarization curve of the fuel cell at the initial moment, a calculation method based on the relative power loss rate as a health indicator was constructed, and its feasibility was verified using the grey correlation analysis method. Then, the RBF neural network optimized by GWO algorithm was applied to predict the remaining useful life of vehicular PEMFCs. Finally, the proposed method was validated using two datasets. The results show that compared with other methods, the GWO-RBF method proposed in this paper has the smallest average absolute percentage error and root mean square error, the largest coefficient of determination, and a relative error of less than 1%. It is concluded that the proposed method can be used to predict the remaining useful life of vehicular PEMFCs with fewer datasets and better accuracy.

The efficient utilization of electromagnetic spectrum resources has become a significant concern in the domain of wireless communications, with EMSM(electromagnetic spectrum map) playing a crucial role in visually representing spectrum usage within a specific task area and providing valuable support for the optimization of wireless networks. To address the challenges associated with generating fine-grained EMSMs under conditions of complex scenes and limited spatial point monitoring data, an improved DRN(deep residual network) model, ES-AFB(enhanced with a spatial attention feature block), was proposed. This model drew inspiration from image super-resolution techniques and leveraged the strong spatial characteristics of EMSMs to design a deep residual network capable of extracting the correlation and spectral features of EMSMs. The enhanced spatial attention feature block was utilized to mine the intrinsic implicit spatial features of coarse-grained EMSMs. Subsequently, the data size was reconfigured through the network’s multilayer up-sampling module, enabling the achievement of a more effective fine-grained image restoration. This approach allows for the generation of high-quality fine-grained EMSMs using limited coarse-grained monitoring data. The effectiveness of the algorithm is validated through simulation experiments, with the root-mean-square error of the EMSMs generated from actual data being found to be no more than 3%.

A simplified lower-limb exoskeleton model was established for the prototype, and the D-H parameter method was used to perform dynamic analysis. Joint angles were measured experimentally and used as inputs for the controller. To address the robot's trajectory tracking problem, traditional PID control was employed, showing good tracking performance but slow response and parameter tuning speed. Although PSO(particle swarm optimization) accelerated the parameter tuning, issues with low convergence accuracy and local optimum traps persisted. Therefore, a PID control based on a chaotic-mapping improved PSO algorithm was designed. The results show that the randomness was enhanced, the parameter tuning speed was increased, and the tracking error was reduced. Simscape was used for visual simulation of joint angles, and the control performance was further validated through various experiments.

Considering the oscillation phenomenon of the original DWA(dynamic window approach) in path planning, an improved DWA path planning algorithm was designed, which is integrated with the artificial potential field method. Firstly, the safety constraint of the DWA algorithm is improved, and the linear obstacle distance evaluation function in the original DWA was improved to the nonlinear obstacle potential field function in the artificial potential field method. Secondly, the improved DWA was combined with the smooth A^* path of the gradient descent method to solve the problem of poor global planning of the traditional algorithm. Finally, the feasibility of the algorithm was verified by simulation experiments and physical experiments. In the simulation experiments, compared with the original algorithm, the improved algorithm in this paper reduces the path of the designed obstacle scene by 9.84%, reduces the running time by 31.71%, and improves the smoothness by 6.49%. Meanwhile, compared with the results of related literatures, the results of this paper have been improved to different degrees in different scenarios. In the physical experiments of automated guided vehicle, the path length is reduced by 10.76% and the elapsed time is reduced by 13.09%. Therefore, the improved DWA generates better path smoothness, shorter path length and shorter elapsed time.

In order to do a good job of emergency management in the airport sector, strengthen the construction of the emergency response system, and improve the emergency response capability, a decision support method based on hybrid reasoning was proposed for the disposal of airport emergencies. Firstly, the ontology model of airport emergencies was constructed by abstracting the emergencies and the disposal process in the airport based on the actual scenarios and official documents. Secondly, the hybrid reasoning combining rule-based reasoning and case-based reasoning was introduced for case retrieval, and case representation was performed for the constructed ontology model to construct a case database. Lastly, the retrieval results are corrected using a feature weighting algorithm for attribute trade-offs, and the attribute parameters were adjusted using a neural network-based weight parameter optimization strategy. The advantages of the Bert+LSTM combination in this task scenario were verified by comparing it with commonly used deep learning models, and the final example proves that when an emergency occurs, the model can focus on the emergency itself, refer to historical cases and disposal standards, and obtain a structured data that comprehensively describes the information and disposal measures of the emergency, which provides support for emergency disposal decision making.

Aiming at the problem that ViBe (visual background extractor) algorithm is prone to ghosting during moving target detection, an improved algorithm, ViBe-BR (ViBe with background restoration) was proposed by adding a background restoration stage to the original algorithm. First, the foreground region within the background image was pre-extracted by combining three-frame differencing. Then, the interior of the region was filled using the background pixels around the foreground region to obtain the restored image; Finally, the restored image was corrected and ViBe detection was performed based on the reduced background to achieve the effect of suppressing ghosting. The experimental results show that the ViBe-BR algorithm achieves good detection results in four different scenes, and compared with the ViBe algorithm, the average precision, recall, and F₁ value of foreground detection have been improved by 0.222, 0.03, and 0.123 in that order, which effectively eliminates the influence of ghosting, and it can be applied to practical geo-localization tasks in order to obtain the geographic location information of the moving targets.

To solve the problem of high memory and computational resource demands in obstacle detection models within autonomous driving perception domain controllers, a lightweight obstacle detection method based on improved YOLOv8 was proposed. This method reconstructs the YOLOv8 backbone network using FasterNet, which utilizes less memory access and computational resources. To mitigate the accuracy decline and the insufficient detection capabilities for small objects caused by model lightweighting, three main improvements were made to YOLOv8: SPD-Conv (space-to-depth convolution) was used to replace traditional stride convolution in the neck network to enhance small object feature extraction. IPIoU(inner powerful IoU), combining the concepts of IIoU(inner IoU) and PIoU(powerful IoU), is introduced as the bounding box regression loss to accelerate loss convergence and improve small object detection performance. SimAM (simple attention module) was incorporated to further enhance model detection accuracy. Experimental results demonstrate that, compared to the original model, the improved model achieves a reduction of 29.1% in parameters, 20.5% in computational load, and 28.8% in model size, while increasing mAP@0.5 by 1.2%. Once deployed in autonomous driving vehicle controllers, the model effectively detects obstacles on the road ahead.

Aspect-level sentiment analysis detects the sentiment polarity of given aspect terms from a fine-grained perspective, providing decision support for e-commerce, consumers, and other groups by mining textual aspect sentiment. Different syntactic dependencies were treated equally in existing methods resulting in the influence of relation types in convolutional networks and the global information from semantic perspective being overlooked. To address these issues, considering the flexibility and complexity of graph structures, the excellent performance of auxiliary tasks in capturing aspect sentiment based on global semantic information and completing fine-grained aspect information, the model WRCN-CL (weight relational convolutional networks and complementary task) which incorporates two tasks: WRCN(weighted relational convolutional networks) and CL (complementary learning) was proposed. Specifically, Bi-LSTM (bidirectional long short-term memory network) was used to extract textual features, which were entered into WRCN and CL tasks separately. Aspect-related semantic information was collected from a global perspective to enhance knowledge, while the aspect representations from CL combined with GCN (graph convolutional networks) to deeply explore syntactic information based on positional and type-aware relational information in WRCN. The fused global and local features were then input into a pooling layer to obtain comprehensive information representation for improved classification performance. Experimental results demonstrate significant improvements with the accuracy of 83.49%、78.19%、75.89% on three public datasets compared to baseline models, proving the effectiveness of the proposed model in aspect-level sentiment analysis classification task.

In order to understand the properties of char which is the main byproduct of coal pyrolysis in the reactor with internals, especially the char prepared at high temperatures, the char samples were prepared by pyrolysis of Yulin bituminous coal under vacuum condition between 1 000 ℃ and 1 500 ℃ in the fixed-bed reactor with internals. The variations of pore structure, composition, calorific value, and carbon structure of chars with pyrolysis temperature were studied. The isothermal combustion characteristic and kinetics were investigated by MFBRA(micro fluidized bed reaction analyzer). The results show that the increase in pyrolysis temperature leads to a deeper degree of pyrolysis. As the pyrolysis temperature increases, the C content and C/H molar ratio of chars gradually increase, H and O contents of chars gradually decreases, and the calorific value increases slightly. The specific surface area and pore volume of chars firstly decrease with the increase of pyrolysis temperature, and then remain essentially constant after the pyrolysis temperature exceeds 1 300 ℃, which is consistent with the trend of graphitization degree. The three-dimensional diffusion model G(x)=[1-(1-x)^1/2]² can well describe the combustion process of chars. The activation energy and pre-exponential factor increase with the increase of pyrolysis temperature, and vary in the range of 30.49~90.17 kJ/mol and 0.69~352.25 s^-1, respectively. The significant decrease in combustion reactivity of char prepared above 1400 ℃ is mainly due to the sharp increase of C/H.

Pressure drop plays an important effect on the performance of fiber filter-stick, and is determined by the materials and geometry structure. In order to develop a method to design and guide the production of the fiber filter, the multiple regression method uses the density of fiber tow, length, and circumference of the sticker as the argument to train the pressure drop model based on the production data. The fiber filter is modeled as multizone represented as the fiber tow and the forming paper. The flow dynamics in these zones are simulated based on the porous media model. The osmotic coefficients represent the pressure drop in the zone packed with the fiber tow. The simulation results show that the pressure drop is positively correlated with the length of the fiber rob, and the type of filter tow has a greater influence on the pressure drop than the circumference. For the design of the fiber filter, the regression model is first used to obtain the consumption of the fiber tow based on the design pressure drop. Then the simulation based on the porous media model is carried out to validate the prediction. If the error between the two methods is within 10%, this predicted fiber stick can be produced. By analyzing the production data and the prediction from models, it is concluded that the method proposed in this work is sufficient to direct the design and production.

Wood consists of a number of early and latewood alternately, in order to predict the strength of the mechanical properties of the specimen according to the proportion of early and latewood, a composite material model with early and latewood as the basic modeling unit was established. Starting from the fine level of wood, the wood is regarded as a composite material ideally bonded by two kinds of materials with different mechanical properties of early and late wood. Taking Yunnan pine as the research object, the respective mechanical parameters of early and late wood were obtained respectively, and according to the theory of composite plywood, the composite material tensile strength model of early and late wood with smooth grain was set up, and the test is utilized to verify the results. The results show that it is feasible to consider the wood as early and latewood composite material and use the laminate theory to predict its mechanical properties. The relative errors between the theoretical values of tensile elastic modulus and tensile strength and the experimental values are within 10%, which is highly reliable, and according to this model, the tensile strength and elastic modulus can be calculated through the measurement of the volume fraction of the latewood in the material, which will provide a good basis for the subsequent early and latewood tests and the further study of the early and latewood related models. This model can provide a reference basis and theoretical foundation for the in-depth study of the subsequent early and late material test and the related model of early and late material.

Due to the negative impact of abandoned powdered clay on land waste and pollution, it is beneficial to improve the powdered clay and use it for backfill in engineering construction.The effects of lignin fiber content and cement content on the unconfined compressive strength of silty clay excavated from a tunnel along the Yangtze River in Hangzhou,Zhejiang Province were studied. The formation mechanism of the compressive strength was analyzed. Finally,the pore microstructure of the sample was quantitatively analyzed by SEM experiment. The results show that the compressive strength reached the maximum value when the lignin fiber content is 4%,and the unconfined compressive strength was greater than that when the lignin fiber content is 2%, 6% and 8%. No matter how much lignin fiber content is,the unconfined compressive strength q'_u increases gradually with the increase of cement content. With the addition of lignin fiber,the average diameter of pores gradually concentrated in the range of particle size less than 1 μm,and the proportion of pores 1~2 μm and 2~4 μm increased with the increase of cement content. With the increase of lignin fiber and cement content,pore abundance mainly concentrated in the range of 0.2~0.5.With the addition of lignin fiber,the particle abundance mainly concentrated in the range of 0.1~0.6,and the particle distribution showed a “mountain” pattern with the addition of cement.

To investigate the mechanism of interaction between root-soil composites and rock interfaces in the ecological protection of rocky slopes, physical models of interfaces were established, considering different plant species, types of soil for slope protection, and degrees of rock weathering. Under natural stress conditions, direct shear tests on the root-soil-rock interfaces were conducted. The variation patterns of shear strength and shear displacement at the interfaces were revealed, and the influence mechanisms of plant root morphology, types of protective soil, and rock weathering degree on the shear strength of the interfaces were analyzed. The results indicate that plant roots, penetrating through the slope protection soil and into the bedrock fissures, significantly enhance the shear strength of the soil in the shear zone and the anti-slip capacity of the soil-rock interface, thereby improving the overall cohesion between the slope protection soil and the rock. The shear strength of the interface is positively correlated with the vertical extension of plant roots and the degree of rock weathering. Compared to scenarios without plants, the planting of vetiver grass increased the peak shear strength by 53.9%, and under highly weathered bedrock conditions, the peak strength increased by 22.4%. Compared to ordinary soil, substrate soil containing binders and aggregating agents significantly improved the shear performance of the soil, increasing the peak shear strength of the interface by 20.1%.

The data-driven approach of machine learning enables the intelligent construction of TBM(tunnel boring machines), which is crucial for optimizing the tunneling process, improving the safety of tunneling and reducing labor costs. In order to solve the problems of excessive noise, redundant parameters and difficult effective feature extraction in TBM operation data, a data-driven machine learning method was used to mine the complex machine-soil interaction contained in the data and realize the classification and prediction of TBM surrounding rock mass. First, for the large amount of operational data generated during TBM tunneling, the KDE (kernel density estimation) method was used to extract features from typical tunneling parameter curves, and the maximum probability of the key operating parameters during stable tunneling stage of TBM is obtained. Then, based on the actual TBM operation data, an integrated learning algorithm for surrounding rock classification stacking was proposed. The algorithm is further optimized through k-fold cross-validation, and the complex relationships in the data are mined by using the two-layer learning framework of base classifier and meta-classifier. Finally, a data set of 5 868 TBM segments was used to verify the effectiveness of the proposed algorithm. The results show that the average F₁ of the four-classification problem is 0.705, and the average F₁ of the two-classification problem is 0.797, which are better than the four selected base classifiers.

Exploring the impact of various hole shapes on the acoustic emission properties of rocks and the fracture mechanisms within rock structures containing holes is of utmost importance, as it enables the detection of fracture progression in rock engineering and the prediction of instability and failure in defective rocks. The mechanical properties, acoustic emission evolution patterns, precursor characteristics of failure, and failure mechanisms of red sandstone samples with different hole shapes were investigated through uniaxial compression tests and acoustic emission systems. The results indicate that the compressive strength, elastic modulus, and strain energy of the intact specimen are approximately 1.4 times, 1.3 times, and 1.7 times greater, respectively, than those of the specimen containing holes. There is a mutation point in the evolution of the multifractal spectrum of A_E at 86%~95% of the peak stress, where the width undergoes a transition from an average low value to a sudden increase before and after the mutation point. This change is accompanied by an increase in fluctuation range, from small to large. The A_E waveform characteristics are distinguished by the presence of dual main frequency bands. As the sample approaches failure, a significant number of low-frequency and high-amplitude signals, along with high-amplitude and high-amplitude signals, are generated. These peculiar trends in acoustic emission can be used as precursors to the critical instability of red sandstone. Based on the correlation between the main frequency of the acoustic emission signal of the rupture event and the fracture mechanism of the rock, the failure mechanism of the intact and square cavity red sandstone is mainly characterized by tension-shear mixed failure, and the shear failure of circular cavity red sandstone is revealed, which effectively avoids the subjectivity of the RA-AF failure mode classification method, and the research results can provide certain guiding significance for engineering design and optimization.

The study focused on wrap-around reinforced soil retaining walls and proposed a calculation method for panel displacement. The horizontal displacement was divided into two components for calculation: the horizontal displacement caused by the strain of reinforcement and the overall horizontal displacement generated by the horizontal earth pressure acting on the back of the reinforced zone. When calculating the horizontal displacement caused by the reinforcement strain, the reinforced zone was divided into subzones through the potential failure surface of reinforced soil retaining wall and the natural repose angle of soil. The horizontal distribution of the reinforcement load was assumed, yielding a simplified calculation model for the horizontal displacement caused by reinforcement strain. For the calculation of the overall horizontal displacement of the reinforced zone, the zone was treated as a ‘cantilever beam’, taking into account the variation in elastic modulus of the reinforced zone with height. The theoretical results obtained through the proposed method were compared with experimental and numerical simulation results. The distribution trend of the displacements was basically consistent, indicating that the proposed method can effectively calculate the panel displacement of wrap-faced reinforced soil retaining walls.

Addressing challenges such as large memory footprint, high computational complexity, and insufficient real-time detection speed in road crack detection models for complex scenarios, a highly efficient and precise algorithm named FCG-YOLO was proposed. Lightweight modules and attention mechanisms were integrated, and traditional feature fusion pyramids were enhanced.The algorithm incorporates PConv into the residual calculation module of YOLOv8n to introduce the improved C2f_Faster structure, thereby reducing model parameters and computational complexity. To enhance detection accuracy, GAM(global attention mechanism) was introduced into the backbone, and the Feature Fusion Pyramid SPPF was improved to SPPFCSPC module, enhancing the model’s ability to represent and fuse features of road cracks.The impact of each module on algorithm performance was verified through ablation experiments, identifying a lightweight and accurate model configuration. Furthermore, the robustness and generalization of the algorithm were explored in practical application scenarios.FCG-YOLO demonstrates outstanding detection efficiency, achieving a detection accuracy of 90.3% mAP50 and 74.4% mAP50-95 on the validation set, with a detection speed of 345 frames per second. These results highlight its high detection efficiency and significant practical value.

Frost damage is a key problem in the construction of alpine tunnels. A numerical model was established to consider heat conduction and convection for designing effective thermal insulation measures. Orthogonal test conditions were designed to analyze the significance of different thermophysical parameters on lining temperature and their influence on lining damage. The results show that the convection coefficient, the thermal conductivity of the insulation layer, and the thermal conductivity of the surrounding rock have significant influences. The thermal conductivity of the insulation layer has the most significant impact. As its thermal conductivity increases, the lowest lining temperature decreases, and the damage level increases. An increase in the convection coefficient decreases the minimum lining temperature and increases damage. Higher thermal conductivity of surrounding rock raises the minimum lining temperature, spreading damage from the vault to the sides. Reducing the thermal conductivity and convection coefficient of the insulation layer, while increasing the thermal conductivity of the surrounding rock, improves overall damage distribution.

To solve the engineering problem of unclear standards and strong subjective experience when shield tunneling drivers set excavation parameters, which makes it difficult to control the shield tunneling attitude, an intelligent prediction model for shield tunneling attitude that considers the comprehensive effect of geological conditions, tunnel structure, and excavation parameters was proposed. Firstly, AWPSO (adaptive inertia weight particle swarm optimization) algorithm was established. Then, a shield attitude prediction model was constructed by combining GRU (gated recurrent unit) neural network, where the AWPSO algorithm was used to determine the optimal combination of hyperparameters in the GRU neural network. Finally, a case study was conducted to verify the on-site monitoring data between Zhangjiang Station and Resort Station on the Shanghai Suburban Railway Airport Connection Line. The results indicate that the proposed shield tunneling attitude prediction model based on AWPSO-GRU has high reliability and engineering practicality, which can provide reference and basis for setting construction parameters during shield tunneling.

Vehicle performance and energy efficiency can be significantly enhanced by PGS-FHEP (planetary gear set based flywheel hybrid electric powertrain). The main components were designed and matched, and DP (dynamic programming) control strategy was introduced based on ECMS (equivalent consumption minimization strategy) to obtain the optimal SOC (state of charge ) trajectory. The initial optimal equivalent factor obtained by GA (genetic algorithm) was adjusted in real time to ensure that the actual SOC trajectory is consistent with the optimal trajectory. Thus, a real-time A-ECMS (adaptive equivalent consumption minimization strategy) was built, and the three control strategies were simulated and compared under CLTC-C (China light-duty commercial vehicle test cycle) condition. The results show that under the control of A-ECMS, compared with the traditional ECMS, the comprehensive energy consumption of FHEV (flywheel hybrid electric vehicle) equipped with the PGS-FHEP is reduced by 2.51%, and the control effect is closer to the DP control strategy. The energy recovery rate of the PGS-FHEP is 57.72%, of which 23.64% is recovered in the form of mechanical energy. In addition, the participation of the flywheel significantly reduces the peak power of the battery during energy recovery process.

Under the overarching vision of Healthy China, the imperative to investigate the design of health-oriented streets has gained paramount importance, aligning with the humanistic and sustainable evolution of urban landscapes. Addressing the limitations of existing health street evaluation methodologies marked by intricate indices, misalignment with the current state of China’s streetscapes, and a dearth of quantitative scrutiny, exploratory factor analysis was employed to distill latent variables. Through a structured approach encompassing health questionnaire analysis, structural equation modeling, and the quantification of health determinants, the research localizes health parameters and constructs a robust, quantifiable evaluation framework for street health. The analysis uncovers that four latent variables demonstrating significant positive correlations with street health outcomes, listed in descending order of influence magnitude: street quality improvement, accessible transportation provision, vibrant block development, and healthy environment promotion. The structural equation model-based quantitative analysis of street health elements furnishes scientific and empirical underpinnings for the development of superior health-conscious urban blocks. This methodological advancement not only refines the precision of street design geared towards health but also elevates the living standards of residents, thereby contributing to the realization of Healthy China’s aspirations.

Formation RF (radio frequency) systems compatibility is an important comprehensive ability that affects the survival and combat effectiveness of aircraft formation. It needs to be effectively assessed and verified through flight tests, and simulation can further optimize test design and improve test efficiency. Thus, a flight test method for formation RF systems compatibility based on simulation prediction was proposed. Firstly, the development trend of RF compatibility and is its capability requirements were summarized and analyzed. Secondly, a simulation model of formation RF systems compatibility based on interference conflict was proposed, and a flight test method of formation RF systems compatibility based on the interference conflict distance from simulation analysis was established. Finally, simulation analysis and flight profile design were conducted for a typical formation RF compatibility test. This study has provided an effective method for the test profile design and flight verification of formation RF systems compatibility.

A method for diagnosing AC series arc faults based on the Inception module and BiLSTM (bidirectional long short-term memory) was proposed to address the challenge of identifying small current changes caused by arc faults in aviation cables. First, features of the raw current data were extracted by calculating the discrete sum of squares of the autocorrelation coefficient, Shannon entropy, and wavelet energy entropy. These features are then combined to form a new feature matrix, enhancing the original data's feature representation. Subsequently, the Inception-BiLSTM network learns from the feature matrix and ultimately completes the arc fault diagnosis. To validate the diagnostic performance of the model in practical environments, a series of experiments were conducted, including vibration tests, stress tests, and wet cable tests, based on an aviation cable arc fault simulation platform, with the experimental data being integrated as detection samples. The experimental results show that the proposed method achieves a high accuracy rate of 99.69% in identifying arc faults.

In order to address a series of safety management issues involved in low-altitude economic development, the technical routes and principles of low-altitude economy as well as the operational experience of implementation plans are summarized, and four universal construction plans for low-altitude security and protection are analyzed, namely the radar and integrated perception technology fusion plan, the broadcast automatic dependent surveillance technology plan, the remote identification technology plan, and the multi-source fusion plan based on TDOA radio technology. On this basis, an evaluation index system for unmanned aerial vehicle detection technology was constructed, and a multi-attribute evaluation method based on DEMATEL and TOPSIS was established. The results show that the multi-source fusion plan based on time difference of arrival (TDOA) is an effective and universal solution for building a low-altitude security system in cities. It is concluded that the construction of a low-altitude security system is a systematic project, which requires the joint efforts of governments, enterprises and the whole society. Integration is needed at the levels of technology, data and operation to meet the future development needs of the low-altitude economy.

To investigate the mechanism and influencing factors of soil heating with coupled in situ thermal technology, a two-dimensional experimental setup was used to simulate the heat treatment process, and the effects of coupled steam injection on thermal conductive heating as well as the effects of steam injection rate and heating mode on the application of the thermal conductive heating and steam injection technology were investigated. The results show that coupling steam injection on the basis of thermal conductive heating treatment can accelerate heat transfer, reduce heat loss, shorten the heating time by 35.67%, and reduce energy consumption by 24.53%. The main mechanism of steam injection enhanced heating is as follows. The additional heat injection increases the temperature difference, which in turn enhances convective heat transfer in the liquid phase driven by buoyancy. The upward migration of steam under buoyancy or pressure to enhance convective heat transfer in the gas phase. In thermal conductive heating and steam injection treatment, changing the steam injection rate or heating mode had a small effect on the treatment energy consumption, increasing the steam flow rate from 0.18 to 0.54 kg/h can shorten the heating time by 22.05%, but increase the water consumption by 132.43%. Compared with the thermal conductive heating and steam injection heated at the same time, thermal conductive heating heated for 30 min and then coupled with steam injection can reduce the water consumption by 28.57%, but will extend the heating time by 3.84%. In engineering applications, suitable restoration solutions should be selected based on duration, cost, etc.

To accurately and comprehensively explore the entire process of physical fatigue development in rescue team members during weighted walking, a multidimensional fatigue assessment method based on eye movement characteristics, electromyographic signals, and subjective evaluation is proposed. Eight volunteers were recruited for the weight-bearing walking fatigue induction experiment. The glasses eye tracking was used to extract the eye movement data about ST (saccade time), average SS (saccade speed) and maximum SA (saccade amplitude). The correlation between the characteristics of eye movement and the degree of fatigue estimated by subjective evaluation was -0.857±0.059, -0.938±0.092, not correlated, respectively. The correlation with iEMG to judge fatigue degree was -0.782±0.090, -0.942±0.030, -0.928±0.026, respectively. Multiple linear regression analysis was performed on subjective score, iEMG value and eye movement parameters. The regression model yielded a coefficient of determination R²=0.989, with the following standardized coefficients: iEMG signals=0.27, ST=-0.16, SS=-0.513, and SA=-0.124. This study makes new explorations and attempts in the monitoring and evaluation methods of fatigue during weighted walking.

Utilizing the PyroSim numerical simulation method, a comprehensive study was conducted to investigate the mechanism of the smoke pull-through phenomenon in a top-central exhaust system under conditions of counter-flowing jets, with a focus on the effects of various exhaust powers. Changes in smoke layer thickness, temperature distribution, and airflow velocity within tunnels were investigated under conditions of enhanced exhaust efficiency. Critical exhaust efficiency thresholds associated with smoke pull-through phenomena were identified across varying heat release rates of fire sources. Furthermore, the critical Froude number for smoke pull-through in centralized exhaust systems was established under counter-flowing jet conditions, along with the critical exhaust rate coefficient required to prevent such occurrences. The findings revealed that as the exhaust power increased, the exhaust port R3, located farthest from the fire source, was the first to experience smoke pull-through, followed by R2, while R1 remained unaffected. An increase in the heat release rate of the fire source led to a corresponding rise in the critical exhaust power threshold for smoke pull-through. A moderate increase in exhaust power could improve exhaust performance; however, exceeding a specific critical value would trigger smoke pull-through, thereby reducing exhaust efficiency. At heat release rates of 20 MW, 30 MW, and 50 MW, the critical exhaust powers were identified as 80 m³/s, 100 m³/s, and 150 m³/s, respectively, with optimal exhaust powers of 50 m³/s, 70 m³/s, and 110 m³/s. Furthermore, the critical Froude number for smoke pull-through was determined to be 35, and the critical exhaust rate coefficient was 0.8.These findings provide a theoretical basis for optimizing the design of exhaust systems, enhancing efficiency, and promoting energy conservation.

As an important firefighting equipment, fire cylinders need to undergo regular safety evaluations during their service period. In order to efficiently and accurately evaluate the safety status of fire steel cylinders, a safety evaluation model suitable for fire steel cylinders was established based on the analytic hierarchy process and fuzzy comprehensive evaluation method. The feasibility of the model was verified through case evaluation. Secondly, the BP neural network based on MPGA (multi population genetic algorithm) is used to optimize the safety evaluation model of fire steel cylinders. This method improves the process of updating weights and thresholds of the BP neural network through multi population genetic algorithm, improving the accuracy of BP neural network prediction results and the efficiency of fire steel cylinder safety evaluation. Finally, the construction of safety evaluation models for fire steel cylinders based on BP, GA-BP, and MPGA-BP was completed using Python. By comparing and analyzing the prediction results of three models, it was found that the MPGA-BP neural network has the smallest prediction error, proving that the proposed MPGA-BP safety evaluation model has high accuracy and can more efficiently and accurately evaluate the safety of fire steel cylinders.