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Lanzhou, an industrial city in northwestern China, is prone to air pollution due to its unique valley basin topography. The incidence of otolaryngologic diseases is closely related to the air quality. Based on air-quality data and outpatient data from an otolaryngology clinic within a hospital in Lanzhou during 2014‒2022, we analyzed the statistical relationships between the concentrations of six common air pollutants and the number of outpatient visits for common otolaryngologic inflammatory diseases using a generalized additive model. We used the results to discuss the potential role of urban airborne pollen in contributing to allergic rhinitis, and we also examined the variability of the relationship between air pollutants and otolaryngologic diseases under anthropogenic interventions using periods before and during the COVID-19 outbreak. Rising concentrations of CO, NO 2 , PM 2.5 , PM 10 , and SO 2 in Lanzhou led to an increase in the number of outpatient visits for otolaryngologic inflammatory diseases, and the impact patterns were different for different genders and different diseases. In terms of gender, CO, NO 2 , and SO 2 had a significantly greater impact on the number of visits for otolaryngologic inflammatory disease for males than for females, while PM 2.5 and PM 10 had a more significant impact on the female population. The number of outpatient visits for acute otitis media and allergic rhinitis also increased with increasing concentrations of the above five pollutants, while airborne pollen was an important trigger for high incidences of allergic rhinitis in July and August. In addition, during the period of lockdown and control due to the COVID-19 pandemic, there was a decrease in the relative risk of the five aforementioned pollutants with respect to the occurrence of inflammatory otolaryngologic disorders. The effect of these pollutants on such disorders was reduced compared with that observed during the pre-pandemic period, indicating that effective air pollution control is an important measure that can be implemented to reduce the occurrence of otolaryngologic inflammatory diseases and protect residents. This study reveals the occurrence pattern of otolaryngologic diseases and their relationships with air pollutants in Lanzhou, which is important for the prevention of otolaryngologic diseases and the formulation of air pollution control strategies.

Due to the emergence of mass personalized production, intelligent welding systems must achieve high levels of productivity and flexibility. Therefore, a self-learning welding-task sequencing method that is driven by data and knowledge was developed during this study. First, a minimized dataset of welding sequences, which is required to predict the welding deformation, was designed according to the number and directions of the welds included in the welding tasks. The dataset consisted of a finite number of welding sequences and their corresponding welding deformation data. Then, an algorithm to predict the welding deformation was developed. To improve the interpretability of the results, domain knowledge was integrated into the construction and training processes of a self-learning model. Finally, a case study regarding bracket welding was investigated. With FEA as the benchmark, the maximum relative error of the welding deformation predicted by the algorithm designed to predict the welding deformation was 8%. The maximum deformation of the optimal welding-task sequence output by the self-learning welding-task sequencing method driven by data and knowledge was 32.31% less than that produced by the rule-based reasoning method. The study results demonstrate that the proposed welding-task sequencing method is effective for welding sequence planning of laser welding bracket structures.

To reduce the difficulty of acquiring and transmitting data in mining hoist fault diagnosis systems and to mitigate the low efficiency and unreasonable reasoning process problems, a fault diagnosis method for mine hoisting equipment based on the Internet of Things (IoT) is proposed in this study. The IoT requires three basic architectural layers: a perception layer, network layer, and application layer. In the perception layer, we designed a collaborative acquisition system based on the ZigBee short distance wireless communication technology for key components of the mine hoisting equipment. Real-time data acquisition was achieved, and a network layer was created by using long-distance wireless General Packet Radio Service (GPRS) transmission. The transmission and reception platforms for remote data transmission were able to transmit data in real time. A fault diagnosis reasoning method is proposed based on the improved Dezert-Smarandache Theory (DSmT) evidence theory, and fault diagnosis reasoning is performed. Based on interactive technology, a humanized and visualized fault diagnosis platform is created in the application layer. The method is then verified. A fault diagnosis test of the mine hoisting mechanism shows that the proposed diagnosis method obtains complete diagnostic data, and the diagnosis results have high accuracy and reliability.

The performance of the brake system is directly related to the safety and reliability of the mine hoist operation. Mining the useful fault information in the operation of a mine hoist brake system, analyzing the abnormal parts and causes of the equipment, and making accurate early prediction and diagnosis of hidden faults are of great significance to ensure the safe and stable operation of a mine hoist. This study presents a fault diagnosis method for hoist disc brake system based on machine learning. First, the monitoring system collects the information of the hoist brake system, extracts the fault features, and pretreats it by SPSS (Statistical Product and Service Solutions). This work provides data support for fault classification. Then, due to the complex structure of the hoist brake system, the relationship between the fault factors often has a significant impact on the fault. Considering the correlation between the fault samples and the attributes of each sample data, the C4.5 decision tree algorithm is improved by adding Kendall concordance coefficient, and the improved algorithm is used to train the sample data to get the decision tree classification model. Finally, the fault sample of the hoist brake system is trained to get the algorithm model, and then the fault diagnosis rules are generated. The state of the brake system is judged by classifying the data. Experiments show that the improved C4.5 decision tree algorithm takes the relativity of conditional attributes into account, has a higher diagnostic accuracy when processing more data, and has concise and clear fault classification rules, which can meet the needs of hoist fault diagnosis.

A mathematical hydraulic support self-tracking model for three-machine cooperative mining is proposed to address low efficiency and difficulties in strategy evaluation of a fully mechanized coal face. The proposed model uses the coordinates and traction speed of the shearer to calculate the frequency of the circular hydraulic support and realize the coordinated operation of the three-machine mining technology. A unity3d hardware-in-the-loop simulation experimental hearer and hydraulic support platform was used to validate the model of autonomous follow-up. The results indicate that collaborative control of coal mining allowed for an efficiency 3.76% higher than under automatic operation mode and 46.03% higher than under manual control; thus, The mathematical model provided an improved production efficiency of the fully mechanized mining face. The mathematical model also provides a more intelligent and reliable security support, and improves the intelligent level of hydraulic support follow-up control.

With regards to the low degree of digitization, lack of real geological terrain, and low degree of automation in the cutting process of the traditional virtual fully mechanized mining face, we studied the key technologies of virtual operation and cutting path planning of the shearer on the three-dimensional (3D) roof and floor based on the virtual reality engine (Unity3D). Firstly, the virtual 3D coal seam was constructed through the 3D geological coordinate data of the mine. On this basis, the shape function of the scraper conveyor with the adaptive configuration on the floor was constructed to obtain the combined operation of the virtual shearer and the scraper conveyor. The movement of the shearer’s walking and height-adjustment was then, analyzed. A strategy for automatic height-adjustment based on the adjustment of the direction of the drum movement is hence, proposed to control the cutting path of the shearer. Finally, different experimental schemes were simulated in the developed prototype system after which each of the schemes was evaluated using the fuzzy comprehensive evaluation method. The results show that the proposed strategy for trajectory control can improve the accuracy and stability of the shearer’s motion trajectory. In Unity3D, the pre-selected schemes and digital and visual planning of coal production are previewed ahead of time, the whole production process can be mapped synchronously in the production process. It is also obtained that the virtual preview and evaluation of the production process can provide some guidance for actual production.

Adult-onset Still's disease (AOSD) is a rare systemic inflammatory disorder marked by recurrent fever, rash, arthritis, and multi-organ involvement. Its occurrence with pregnancy complicates diagnosis and management. To present the diagnosis and treatment process of two pregnant patients with newly diagnosed AOSD. Two cases of AOSD were initially diagnosed during pregnancy. Case one involved a pregnant woman at 16 + 2 weeks of gestation with recurrent fevers, rash, and myalgia. She responded well to treatment with methylprednisolone and cyclosporine and subsequently had a normal vaginal delivery. Case two involved a pregnant woman at 30 + 6 weeks of gestation who presented with systemic joint pain and fever. After being diagnosed with AOSD, she underwent a cesarean section. Treatment included methylprednisolone, cyclosporine, and addition of methotrexate postpartum. Adult-onset Still's disease can be triggered by pregnancy, requiring a multidisciplinary approach for optimal management and fetal outcomes.

Members of the glycosyltransferase 8 (GT8) family play an important role in regulating gene expression in response to many kinds of biotic and abiotic stress. In this study, 56 members of the apple GT8 family were identified, and their gene structure, phylogenetic relationships, chromosomal localization, and promoter cis-acting elements were comprehensively analyzed. Subsequently, 20 genes were randomly selected from the evolutionary tree for qRT-PCR detection, and it was found that MhGolS2 was significantly overexpressed under stress conditions. MhGolS2 was isolated from M.halliana and transgenic Arabidopsis thaliana, tobacco and apple callus tissues were successfully obtained. The transgenic plants grew better under stress conditions with higher polysaccharide, chlorophyll and proline content, lower conductivity and MDA content, significant increase in antioxidant enzyme activities (SOD, POD, CAT) and maintenance of low Na+/K+ as compared to the wild type. Meanwhile, the expression levels of reactive oxygen species-related genes (AtSOD, AtPOD, and AtCAT), Na+ transporter genes (AtCAX5, AtSOS1, and AtHKT1), H+-ATPase genes (AtAHA2 and AtAHA8), and raffinose synthesis-related genes (AtSTS, AtRFS1, and AtMIPS) were significantly up-regulated, while the expression levels of K+ transporter genes (AtSKOR, AtHAK5) were reduced. Finally, the Y2H experiment confirmed the interaction between MhGolS2 and MhbZIP23, MhMYB1R1, MhbHLH60, and MhNAC1 proteins. The above results indicate that MhGolS2 can improve plant saline-alkali tolerance by promoting polysaccharide synthesis, scavenging reactive oxygen species, and increasing the activity of antioxidant enzymes. This provides excellent stress resistance genes for the stress response regulatory network in apple.Key messageThis report characterizes the apple GT8 gene family in detail and shows that MhGolS2 plays an important role in the positive regulation of saline-alkali tolerance.

During coal mining processes, the mechanical roller used for cutting encounters various materials, including coal walls with diverse properties. This study demonstrated an approach that can reflect actual operational conditions highly accurately. To calibrate the discrete-element coal particle bond model, a methodology that integrates experimental and simulation techniques was adopted in this work. The model was verified by comparison with experimental results to develop a relatively accurate interception model. Unlike traditional discrete element interception models, which have limited capacity to intercept a large volume of single-particle pulverized coal, this model can generate a range of coal lumps-pulverized coal, small lumps, and large lumps-during the cutting process. The model was utilized to assess drum loading, coal-breaking efficiency, and lump coal rate. When transitioning from hard to soft coal, cutting torque decreases linearly from 100 to 500 Nm. Compared to the considerably small torques observed when cutting soft rock, the fluctuations in torque within the cutting drum of the model were significantly reduced. The maximum differences in torque were 2188.241, 1950.913, and 1033.902 Nm, respectively. Soft coal with the highest number of intercepted coal particles had an 8.89% higher total particle count than soft rock with the lowest number of intercepted particles. A vibrating screen test designed in EDEM to measure the intercepted lump coal rate showed that the overall lump coal rate decreased with diminishing coal rock strength. The lump coal rate for soft rock reached 34.98%, whereas that for hard to medium-soft coals were 30%–32%. Additionally, the loading rates for hard, medium, and medium-soft coals were similar: the lump coal rate for soft coal decreased to 27.91%. During the drum cutting simulation, medium, medium-soft, and soft coals produced upper flake gang with a false top. Medium-soft and soft coals formed a flake gang at the center of the coal wall. Torque curves corresponding to coal-wall gang formation revealed a consistent pattern: each curve exhibits a series of consecutive torque peaks followed by a sharp decline. These findings provide significant theoretical foundations and a simulation tool for optimizing shearer design, selecting cutting parameters, predicting coal wall stability, and controlling coal quality.

This paper proposes a modified YOLOv4 model, named GYOLO, for coal gangue recognition with the aim of reducing model parameters, improving calculation speed, and reducing equipment requirements. To achieve this, the paper optimizes the feature extraction network structure by using linear operation instead of traditional convolution to obtain redundant feature maps, thus reducing the number of parameters by 29.7%. A feature fusion network structure is also reconstructed to strengthen the model’s use of feature information, further explore the dependence of each channel feature, and make better use of feature information. The ablation experiment is designed to verify the effect of each improvement. The image is blurred to improve the difficulty of target detection and test the robustness of the GYOLO model. The generative adversarial network is trained with a small amount of coal gangue data, and then a large amount of virtual data is obtained by using the generative adversarial neural network. The GYOLO model is trained by transfer learning, which reduces the dependence of the model on real data. The GYOLO algorithm is compared with a variety of excellent target detection algorithms to analyze the performance of the algorithm. It is verified that the accuracy of the proposed method is 97.08%, which is 2.3% higher than that of the original model, the amount of parameters is reduced by 19.6%, and the amount of data required is reduced by 57.3%. The balance between data volume, parameter quantity and model performance is further realized.