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In order to investigate changes in the quality, flavor and major aroma components of pickled chilies in ceramic jars during fermentation, this study used headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) to analyze the volatile substances of pickled chilies in ceramic jars from a certain producer after storage for 1–5 years and determined their physicochemical indexes and total bacterial counts. The results showed that the levels of soluble salt-free solids, total acids, total esters, amino nitrogen and total colony count of chopped chili peppers kept changing with storage time, and the quality and aroma kept improving in the first three years, and then the quality declined in the fourth year. A total of 112 volatile compounds were detected, including 22 acids, 12 alcohols, 36 esters, 5 ketones, 9 aldehydes, 5 phenols, 18 hydrocarbons, and 5 other compounds. The volatile composition and content varied with storage time, and 36 volatile compounds with odor activity values (OAVs) greater than 1 were found, which played an important role in the overall aroma of chopped chili peppers. Among them, linalool and methyl salicylate, ethyl salicylate, 4-ethyl-2-methoxyphenol were common to all samples. The samples stored for 1–3 years were superior in terms of organoleptic quality, physicochemical properties and volatile components, while those stored for more than 3 years had a strong aroma but poorer texture.

This study investigated the effects of adding different amounts of flaxseed gum (0%, 0.2%, 0.4%, 0.6% and 0.8%) on the microstructure and physicochemical properties of starch in frozen dough using scanning electron microscopy (SEM), solid-state nuclear magnetic resonance (SSNMR) spectroscopy, X-ray diffraction (XRD), and rapid visco analysis (RVA). The aim was to reveal the potential mechanism underlying the effect of flaxseed gum on the quality improvement of frozen dough from the perspective of changes in starch characteristics of frozen dough. The results showed that after repeated freeze-thaw cycles, the surface of starch particles appeared sunken, the relative crystallinity of starch decreased, the double helix structure of starch molecules was destroyed, and consequently water molecules were more likely to enter starch particles, thus resulting in an increase in peak viscosity and final viscosity. The addition of flaxseed gum inhibited the formation of ice crystals in frozen dough and delayed the unwinding of the double helix structure of starch molecules, and the hydrogen bond interaction between flaxseed gum and starch molecules increased the relative crystallinity of starch (22.2%–23.8%). As a result, the peak viscosity of starch decreased from 2 409.5 mPa⋅s to 2 247.2–2 288.5 mPa⋅s, and the gelatinization temperature gradually increased. In conclusion, flaxseed gum delayed the damage of starch in dough caused by repeated freeze-thaw cycles, thereby protecting frozen dough.

In the stratified fully mechanized caving of ultra-thick coal seams, the inclined layout of upper and lower caving faces leads to lower fully mechanized caving face intermittently crossing residual coal pillars of the upper layer. This results in complex overburden breakage and mine pressure behavior, posing challenges to surrounding rock control and hindering safe coal production. Based on the 250203 lower fully mechanized caving face at Yanbei Coal Mine of Gansu Huating Coal Power Co., Ltd., methods of physical similarity simulation experiments, numerical modeling, and field measurements were applied to study the overburden movement and stress distribution evolution characteristics under residual coal pillars of the upper layer during inclined fully mechanized caving. The instability characteristics of residual coal pillars of the upper layer and the variation patterns of surrounding rock support pressure under the disturbance of lower fully mechanized caving were clarified. The overburden of the inclined working face was categorized into internal and external fields, and the evolution characteristics of the overburden structure when the lower fully mechanized caving face crossed the residual coal pillars were revealed. The results showed that the mining of the lower fully mechanized caving face induced instability in residual coal pillars of the upper layer, resulting in large-scale overburden collapse. The roof structure within the disturbed zone evolved progressively into a composite structure of "low-level inverted step cantilever beams and high-level large masonry beams". When the working face was 15 m from the residual coal pillar, the maximum vertical stress of the pillar reached 46.7 MPa, an increase of 9.9% compared to the pre-mining state, with significant pillar deformation observed. When the working face was directly beneath the residual coal pillar, the stress concentration zones of the pillar displayed an approximately crescent-shaped distribution. As the inclined position of the lower fully mechanized caving face relative to the residual coal pillars changed, the overburden breakage in the external field exhibited an approximately symmetrical trapezoidal shape. The dynamic instability of the structure caused the overburden collapse in the internal field to evolve through stages of "asymmetric double arches", "symmetric double arches", and "single arch". These findings provide significant guidance for the safe mining of stratified fully mechanized caving faces in ultra-thick coal seams.

Abstract In mining, the roof structure of a working face with a large mining height in a shallow‐buried coal seam is prone to cutting instability in front of the support, which causes support crushing and coal‐wall spalling. This study analyzes 2201 working faces with large mining heights in the Haiwan No. 3 Mine by investigating the mechanical response law of coal walls under the transient excitation of roof structure instability. A mechanical model of coal walls under dynamic and static load coupling is established, revealing the formation mechanism of coal wall spalling and its main influencing factors. Prevention and control technologies of advanced grooving and pressure relief are proposed, and grooving parameters are determined. The results show that: During the mining process of large mining height working face in shallow buried coal seam, the step change of static response of coal wall is induced by the transient instability of roof structure, and the high abutment pressure is formed on the coal wall. At the same time, the strain energy and gravitational potential energy of roof cutting instability are released to form a dynamic load. Under the action of dynamic and static load, the plastic failure dissipation power of the coal wall is greater than the critical power, and the coal wall spalling occurs. The main influencing factors are mining load, mining height, and internal friction angle of the coal wall. The method of advanced grooving pressure relief can change the roof structure and coal force mechanism, by cutting off the connection between the coal wall and immediate roof, reducing the height of the coal wall force, effectively controlling the position of the roof cut off break line, reducing the static load effect of the roof on the coal, and transferring the position of the dynamic load response to the depth of the coal wall. When the grooving height‐depth ratio k is 0.75, the peak value of the abutment pressure moves 5.57 m to the front of the working face, and the pressure relief effect is the best. The above research results provide an effective active protection method for controlling coal wall spalling in fully mechanised working face with large mining height.

Valley photonic crystals (VPCs) provide an intriguing approach to suppress backscattering losses and enable robust transport of light against sharp bends, which could be utilized to realize low-loss and small-footprint devices for on-chip optical communications. However, there are few studies on how to achieve power-efficient tunable devices based on VPCs, which are essential for implementing basic functions such as optical switching and routing. Here, we propose and experimentally demonstrate a thermally tunable add-drop filter (ADF) based on VPCs operating at telecommunication wavelengths. By leveraging the topological protection of the edge state and the distinct property of negligible scattering at sharp bends, a small footprint of 17.4 × 28.2 μm2 and a low insertion loss of 2.7 dB can be achieved for the proposed device. A diamond-shaped microloop resonator is designed to confine the light and enhance its interaction with the thermal field generated by the microheater, leading to a relatively low power of 23.97 mW needed for switching the output signal from one port to the other. Based on the thermally tunable ADF under the protection of band topology, robust data transmission is implemented with an ultrahigh data rate of 132 Gb/s. Our work shows great potential for developing high-performance topological photonic devices with the thermally tunable silicon-based VPCs, which offers unprecedented opportunities for realizing topologically protected and reconfigurable high-speed datalinks on a chip.

Abstract Accurate estimation of the effects of dynamic disturbances on stress concentration is crucial for the stability of rock engineering and the corresponding analytical approaches are needed. This study presents an analytical approach to calculate the relative stress distribution with a point source inside and outside a hole using the linear theory of elasticity. The Helmholtz potentials and Sommerfeld integral are employed to describe the displacement and stress components, and then formulate the equilibrium equations to solve the equivalent stress distribution around the hole. Numerical examples demonstrate the impact of model parameters on the equivalent stress, such as frequency, hole radius, source location, etc. It is found that sometimes high frequencies can make the equivalent stress greater far from the source than that close to the source. Additionally, when the ratio of the distance between the source and the hole axis to the hole radius exceeds ten, the equivalent stress distribution around the hole remains nearly constant. This approach can be used for the design and assessment of underground engineering structures' stability under dynamic disturbances.

The pseudo-inclined mining has limited capacity to reduce the inclination angle of working face, necessitating breakthroughs in longwall comprehensive mechanized mining methods for steeply inclined medium-thick coal seam with inclination angles exceeding 60°. Taking the 3212 working face in the Lüshuidong Coal Mine of Huayingshan Coal Industry Co., Ltd. as the engineering background, a double-inclined mining method was proposed based on pseudo-inclined mining. Physical similarity simulation and numerical simulation were employed to analyze the movement and accumulation patterns and stress evolution of the gangue filling after roof breaking in double-inclined mining. The results showed that the layout of the double-inclined working face needed to be coordinated with the mine’s mining design, and the maintenance of two roadways of the working face was challenging. The return air roadway side was significantly affected by the mining stress, while coordinating the transportation roadway with the working face support was also difficult. Due to the irregularly shaped goaf roof structure, the double-inclined mining had larger collapse steps, and its gangue collapse accumulation formed an inverted triangular pattern with periodic sliding characteristics. The accumulation of large gangue blocks resulted in non-uniform pressure in the upper and middle sections of the working face. In double-inclined mining, the original rock stress increased gradually with the advancing distances of the working face, with varying pressure intensities in the upper, middle, and lower inclined sections of the working face. Generally, the upper and middle sections experienced greater pressure than the lower section, but the stress concentration factor of the support pressure was higher in the lower inclined section compared to that in the upper and middle sections of the working face. The stress evolution characteristics of the roof in double-inclined mining were influenced by the working face shape and advancing distance. Additionally, the roadway inclination angle adjustment caused the range of stress release to decrease asymmetrically from the upper to the lower inclined sections.

In actual production, the cutting and crushing process is the result of multi action coupling. The bidirectional coupling technology of discrete element method (DEM) and multi bodydynamics (MBD) can achieve information exchange between coal mining equipment and coal wall. It is in line with actual production situations and has significant advantages. In order to improve the working performance of the shearer drum, based on the DEM-MBD bidirectional coupling mechanism, combined with mechanical performance experiments and simulation experiments to obtain actual operating parameters, a bidirectional coupling model of the shearer drum cutting coal wall is established using simulation software EDEM and RecurDyn. The torque and cutting force experienced by the drum during the simulation process are analyzed, and it is proved that the coupling effect and cutting effect are good. Single factor experiments and orthogonal experiments are designed to analyze the influence of drum operating parameters on working performance. SPSS software is used to obtain the degree of influence of drum speed, cutting depth, and traction speed on cutting specific energy consumption, coal loading rate, and load fluctuation coefficient. The feasibility of the model is verified through on-site experiments. A multi-objective optimization model is constructed with drum speed, cutting depth, and traction speed as decision variables, and cutting specific energy consumption, coal loading rate, and load fluctuation coefficient as objectives. The improved multi-objective gray wolf optimization (MOGWO) algorithm and technique for order preference by similarity to ideal solution (TOPSIS) method are used to solve the model. It is found that when the drum speed is 31.12 r/min, the cutting depth is 639.4 mm, and the traction speed is 5.58 m/min, the working performance of the shearer drum is optimal. At this time, the cutting specific energy consumption is 0.467 7 kW·h/m3, the coal loading rate is 43.01%, and the load fluctuation coefficient is 0.327 8.

There are many kinds of distortion of carburizing and quenching gears, but they will be reflected in the grinding amount of gear flanks. Through theoretical analysis and long-term production practice, it is found that the change of tip diameter has the greatest influence on the grinding amount, the change of the helix angle and the circumferential warpage of gear rims takes the second place, and other factors have relatively little influence. The empirical formulas for calculating the maximum and minimum grinding amount of the tooth flank are given, according to the change of the tip diameter and other parameters. These formulas can be used to determine the case-hardened depth (CHD) in drawings, the reserved grinding amount of gear flanks during gear hobbing, and the target CHD during carburizing and quenching. After carburizing and quenching, the maximum and minimum grinding amount and the actual CHD can be calculated and predicted according to these formulas, so as to provide guidance for subsequent production.

In recent years, China's new-style tea industry has shown a booming development, and the new-style tea market has become an arena for a new round of capital competition. In the context of the influx of new brands, how to shape their brand personalization has become critical. From the perspective of brand building, this paper takes the leading tea brands on the market as analysis cases, studies the application of illustration design in new tea packaging and its commercial added value, and discusses the application of illustration in new tea packaging.

the carbon peaking and carbon neutralization goals and the wave of intelligent coal mine construction, China’s coal machinery industry will transform and upgrade toward intelligence, green, safety, and efficiency. Opportunities and challenges coexist in the coal machinery market, and development of intelligent technologies and machinery for coal mining has become an urgent demand. This study is based on the development reality in China that intelligent coal mining is still in its early stage and sorts out the current status and trends of intelligent technologies and machinery for coal mining. Moreover, it analyzes the key technology system of intelligent coal machinery, covering intelligent coalmine equipment support, intelligent fully mechanized mining and caving, intelligent rapid excavation, intelligent perception and control of main/auxiliary transportation systems, and coalmine robot technologies. Moreover, development directions are proposed, including key basic materials and advanced processing techniques, flexible manufacturing and virtual simulation, equipment remanufacturing, deep fusion of 5G with industrial Internet, industrial big data and artificial intelligence, intelligent perception and interconnection by the Internet of Things, and cyber-physical and digital twin systems. Furthermore, we suggest to strengthen breakthroughs in intelligent technologies and machinery for coal mining, improve the talent support system, emphasize both plan guidance and industrial layout optimization, and establish an innovative ecosystem for coal machinery research and manufacture, thereby promoting the high-quality development of coal machinery.

Currently, the intelligent construction of coal mines is in a transitional exploration period from primary to intermediate and advanced intelligence. The independent innovation capability of existing intelligent equipment still needs to be strengthened. The core technologies are bottleneck problems. The reliability problem under complex underground storage conditions is prominent. It is of great significance to use the new generation of information technology to promote the digital transformation and upgrading of coal mine equipment, and to promote safe, efficient, and green coal mining through digitization and intelligence. This paper reviews the evolution process of intelligent development of coal mining technology and equipment in China. This paper analyzes the production characteristics of each stage of coal mining. It is considered that the coal mine intelligence has completely changed the coal production mode. The production factors are mainly intelligent equipment, intelligent systems, and compound technical talents. They have essentially improved the level of safe and efficient production of coal mines, and helped the coal mines to reduce personnel, increase safety and improve efficiency. Among them, intelligent coal mine equipment is the core element of coal mine intelligence. Considering the demand for the digital and intelligent transformation and development of future coal mine equipment, we have explored the path of integrating the new generation of information technology and coal mine equipment. It uses equipment as an intelligent carrier. It endows the equipment with humanized intelligent capabilities such as intelligent perception and analysis, intelligent judgment and reasoning, intelligent decision-making and control, intelligent diagnosis and learning through the new generation of information technology. The system architecture of the Internet of things, 5G, big data, cloud computing, and artificial intelligence technology enabling intelligent perception, intelligent transmission, intelligent analysis, intelligent computing and intelligent decision-making of coal mine equipment has been constructed. The paper analyzes the theoretical, technical, and talent challenges in the development of digital intelligence in coal mining equipment. The paper proposes suggestions for the development of digital intelligence in coal mining equipment. Consolidating the foundation is the priority. It is necessary to build a top-level design theoretical system for intelligent coal mining equipment, strengthen key core technology research, accelerate the construction of new infrastructure, and consolidate the foundation of intelligent coal mining development. The intelligent technology for coal mining equipment and special application scenarios in coal mines develop in collaboration and coupling. It is suggested to meet the on-site needs of coal mines, form the intelligent technology for coal mine specific scenarios, and explore innovative models of equipment digital intelligence integration. The intelligent technology and green mining technology integrate and develop. It is suggested to create digital, intelligent, and green equipment. It is suggested to develop new models of intelligent, efficient, and low-carbon coal mining, and promote energy conservation and consumption reduction in coal production. It is suggested to establish a sound system for cultivating intelligent talents in coal mines, cultivate interdisciplinary and composite talents, and enhance talent reserves for the construction of intelligent coal mines.

Intelligent decision-making support for multi-behavior collaborative control in coal mine excavation is one of the core functions of the coal mine excavation working face. The optimal time series planning of multi-behavior collaborative control in excavation is the key to intelligent decision-making. In order to solve the problems of single control mode, solidification and poor collaborative operation capability of multi-behavior in coal mine excavation, an intelligent decision-making model of multi-behavior collaborative control in coal mine excavation is designed. It realizes the collaborative operation of multi-behavior in the optimal time series. Firstly, an intelligent decision-making method for excavation multi-behavior collaborative control is proposed. The feasible time series planning set and multi-objective optimal time series planning strategy for excavation multi-behavior are determined. Secondly, based on the regulations and process requirements of the excavation site, a set of excavation action events is determined. By analyzing the time relationship between two action events in the event set, a constraint matrix for the time relationship of excavation multi-behaviors is obtained. Thirdly, based on the transformation method of the time relationship constraint matrix, the multi-behavior time relationship constraint matrix of excavation is transformed into a time relationship constraint matrix. The feasible time series planning set of excavation multi-behavior is obtained. Finally, the solving functions for different excavation objectives are defined and the optimal time series for different excavation objectives is obtained. The experimental results show that the excavation robot can work collaboratively without interference according to the optimal time series planning results of the excavation action determined by the model under different excavation objects. The execution time of one working cycle of the excavation operation is basically consistent with the time calculated by the decision-making model.

Seabed fluid migration is a critical process that involves the transport and movement of liquids, gases, and seawater within and outside the seabed, which has significant impacts on the genesis, development, and evolution of seabed geological disasters. Notably, typical disasters such as submarine landslides in the sea area of China demonstrate a strong relevance with seabed fluid migration phenomena. In this paper, we analyze the distribution characteristics of typical fluid migration system types and geological disaster causes taking the northern South China Sea as an example, and we summarize the observation and investigation methods of seabed fluid migration. Furthermore, we propose the primary issues and content that must be addressed in the study of disasters induced by seabed fluid migration and their prevention and control. Specifically, we suggest that research should focus on the three phases, namely disaster genesis induced by deep high-pressure fluid migration, disaster development caused by gas hydrate decomposition and fluid migration, and disaster triggering resulting from ocean water movement. Based on breakthroughs in technological bottlenecks such as multi-system integration, multi-scale cooperation, and multi-dimensional information processing in deep-sea exploration, we must conduct in-depth research on the evolution mechanisms of seabed disaster genesis, development, and triggering under the influence of seabed fluid migration. Additionally, we must develop theoretical methods for seabed disaster risk prevention and control under the coupled effects of seabed fluid migration, geological environment, and human activities.

The degradation process of shearer bearings is not a simple linear or exponential relationship. It should be analyzed in different stages. However, the current prediction method for the remaining useful life (RUL) of shearer bearings does not fully consider this factor. In order to solve this problem, a method for predicting the remaining useful life of shearer bearings based on an improved similarity model is proposed. The model uses a universal similarity model to describe the process of equipment degradation. Based on this, through root mean square clustering analysis, the bearing degradation process is divided into the stable operation stage, initial degradation stage, and accelerated degradation stage. With the help of traditional similarity model ideas, the health condition of shearer bearings is calculated by segment. And it is fitted to obtain a degradation curve sample library, Through data preprocessing and similarity analysis on offline sample library data and real-time data of online shearers, the bearing RUL of the shearer is ultimately obtained. The experimental results show that the mean absolute error values of the RUL prediction method for shearer bearings based on improved similarity model are reduced by 30.49%, 7.54%, 16.98%, 24.74%, 17.96% and 9.49% respectively, compared to the convolutional gated recurrent unit (ConvGRU), convolutional long short-term memory neural network (ConvLSTM), convolutional neural networks (CNN), self-organizing map (SOM), recurrent neural networks (RNN), and traditional similarity models. The proposed model can effectively predict bearing RUL. The on-site test results show that after continuous monitoring of the bearing of the shearer for 87 days, the health condition of the bearing is gradually reduced from 0.997 to 0.972. The result is basically consistent with the actual use of the bearing of the shearer on site. It verifies the effectiveness of this method.

The low light images can lead to many computer vision tasks not achieving the expected results. This can affect subsequent image analysis and intelligent decision-making. The existing low light image enhancement methods for underground coal mines do not consider the real noise of the image. In order to solve this problem, a method for enhancing low light images in coal mines based on Retinex model containing noise is proposed. The Retienx model containing noise is established. The noise estimation module (NEM) is used to estimate real noise. The original image and estimated noise are used as inputs to the illumination component estimation module (IEM) and reflection estimation module (REM) to generate and couple the illumination and reflection components. At the same time, gamma correction and other adjustments are made to the illumination components. And division operations are performed on the coupled image and adjusted illumination components to obtain the final enhanced image. NEM uses a three-layer CNN to perform Bayer sampling on noisy images. It reconstructs them to generate a three channel feature map which is the same size as the original image. Both IEM and REM use ResNet-34 as the image feature extraction network. The multi-scale asymmetric convolution and attention module (MACAM) is introduced to enhance the network's capability to filter details and important features. The qualitative and quantitative evaluation results indicate that this method can balance the relationship between light sources and dark environments, reduce real-world noise's impact, and perform well in image naturalness, realism, contrast, structure, and other aspects. The image enhancement effect is superior to models such as Retinex-Net, Zero-DCE, DRBN, DSLR, TBEFN, RUAS, etc. The effectiveness of NEM and MACAM is verified through ablation experiments.

Studying the canopy spectral reflection characteristics of different N-efficient maize varieties and analyzing the relationship between their growth indicators and spectral vegetation indices can help the breeding and application of N-efficient maize varieties. To achieve the optimal management of N fertilizer resources, developing N-efficient maize varieties is necessary. In this research, maize varieties, i.e., the low-N-efficient (Zhengdan 958, ZD958), the high-N efficient (Xianyu 335, XY335), the double-high varieties (Qiule 368, QL368), and the double inefficient-type varieties (Yudan 606 YD606), were used as materials. Results indicate that nitrogen fertilization significantly increased the vegetation indices NDVI, GNDVI, GOSAVI, and RVI of maize varieties with different nitrogen efficiencies. These findings were consistent with the performance of yield, dry matter mass, and leaf nitrogen content and were also found highest under both medium and high nitrogen conditions in the double-high variety QL368. The correlations of dry matter quality, leaf nitrogen content, yield, and vegetation indices (NDVI, GNDVI, RVI, and GOSAVI) at the filling stage of different N-efficient maize varieties were all highly significant and positive. In this relationship, the best effect was found at the filling stages, with correlation coefficients reaching 0.772–0.942, 0.774–0.970, 0754–0.960, and 0.800–0.960. The results showed that the yield, dry matter weight, and leaf nitrogen content of maize varieties with different nitrogen efficiencies increased first and then stabilized with the increase in the nitrogen application level in different periods, and the highest nitrogen application level of maize yield should be between 270 and 360 kg/hm2. At the filling stage, canopy vegetation index of maize varieties with different nitrogen efficiencies was positively correlated with yield, dry matter weight, and leaf nitrogen content, especially GNDVI and GOSAVI on the leaf nitrogen content. It can be used as a means to predict its growth index.

In order to reveal the protective mechanism of hydrocolloid on wheat dough gluten during freezing, Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) were used to investigate the secondary structure, microstructure, disulfide bond content and water-binding capacity of gluten obtained from frozen-thawed wheat dough added with different amounts of flaxseed gum (FG) or Artemisia sphaerocephala Krasch. gum (ASKG). Results showed that freezing-thawing treatment destroyed the ordered network structure of gluten, while FG and ASKG could cross-link with the gluten protein network structure to form a stable cross-linked structure. Upon the addition of FG or ASKG at 0.4%, the gluten network structure was most uniform and dense. The addition of FG and ASKG increased the content of disulfide bond and the relative content of α-helix in gluten, making the gluten structure more stable under repeated freeze-thaw cycles. In addition, the addition of FG and ASKG reduced the surface hydrophobicity and consequently led to an increase in the water-binding capacity of wheat gluten. The highest water-binding capacity was obtained at an addition level of 0.4% for both FG and ASKG, 1.46 and 1.43 times higher than that of the control group without added hydrocolloid undergoing repeated freeze-thaw cycles, respectively. All these results showed that FG and ASKG could interact with gluten to form a stable cross-linked structure, thereby enhancing the water binding capacity of gluten protein and exerting a cryoprotective effect on it.

The CPC's Report on the 20th National Congress reiterated that per capita GDP will reach the level of a moderately developed country(referred to as middle income) by 2035, and the rapid expansion of economic scale is bound to drive energy demand and increase energy consumption, posing challenges to energy supply. The “dual carbon” target promotes the “dual control” of energy consumption to the “dual control” of carbon emissions (referred to as the new “dual control”), which will make the carbon control measures more accurate and increase the pressure of low-carbon transition. Under the background of “middle income” and new “dual control”, it will be a challenging and unavoidable major subject of times to coordinate energy supply and low-carbon transformation, grasp the new opportunities of coal industry development, and enable the transformation and upgrading of the coal industry and high-quality development based on the basic national conditions dominated by coal. By reviewing the historical contribution of the coal industry to the development of the Chinese national economy, combined with the characteristics of China’s resource endowment and the reality of economic and social development at the present stage, it is believed that coal will still play an energy security role in the future and support the realization of the “middle income” goal. The problems of safety, ecology, and low intelligence in the process of coal development were deeply analyzed, and it was revealed that the new challenges facing the coal industry under the background of “middle income” and new “dual control” are mainly increased requirements for energy supply and increasing pressure of low-carbon transition. The change law of coal power consumption in different two-carbon periods was calculated: coal power consumption will be 2 billion tons in 2030, and will drop to 500 million tons in 2060, a decrease of 75%, which greatly impacts coal sales and brings huge potential risks to the development of the coal industry. New opportunities for the development of the coal industry in different periods of dual carbon were put forward. From now to the peak period of carbon emissions, the focus will be on promoting the coordinated development of energy conservation and low-carbon. Opportunities should be seized, such as the integration of coal and coal electricity, the integration of coal mine and circular economy industrial park, coordinated development of mining area restoration and ecology, coal feeding education strategy, coupling of mechanized coal mining and intelligent mining, resource utilization and energy utilization of coalbed methane, etc., to provide energy support for achieving the goal of “middle income”, and achieve high-quality development in the guarantee of supply, and provide a basis for green and low-carbon transition development. In the period from carbon peak to carbon neutral, the coupled development of coal and modern coal chemical industry, coal energy and new energy, will be vigorously developed to realize the leapfrog development of green and low-carbon transition based on high-quality development and help the early realization of the goal of carbon neutral.

The existing hydraulic support modeling method has the problems of single modeling mode and lack expression of internal actions of the model. It is difficult to realize deep knowledge mining of the digital twin model. The modeling of hydraulic support only studies the mechanical or hydraulic parts separately. It is difficult to master its overall dynamic characteristics. In order to solve the above problems, taking the shield hydraulic support ZY6800/08/18D as the research object, a hydraulic support digital twin joint modeling method is proposed. The three-dimensional solid models of the mechanical system and the hydraulic system of the hydraulic support are established by using the SolidWorks software. The three-dimensional solid model is imported into the MapleSim software by .sldasm file format. The kinematic pair is used for connecting the mechanical part, and the hydraulic element is used for connecting the hydraulic part. The twin models of the mechanical system and the hydraulic system of the hydraulic support are established. The twin models are combined to carry out data interaction and model optimization with the physical entity through a database. In order to make the model one to one mapping physical entity, the hydraulic support digital twin is established, including system layer, information layer and physical layer. The consistency experiment of virtual and real is carried out on the digital twin of the hydraulic support. Under the condition of inputting the same signal into the physical entity and the twin, the consistency of the angle change of the connecting rod between the physical entity and the twin is analyzed. The rationality and accuracy of the model are verified. The results show that the fitting degree of the angle of the connecting rod between the physical entity and the twin is 0.986, which is close to 1. The fitting degree is good, which indicates that the position and attitude information of the twin model driven by real data is basically consistent with the running result of the physical entity. The overall angle error of the connecting rod is from −0.198° to +0.185°, which meets the precision requirements within the precision range of the tilt sensor. The motion law of the digital twin model conforms to the actual motion state of the hydraulic support. The mutual mapping and mutual fusion between the physical entity and the digital twin are realized.

Under the background of the current development of the intelligent high-quality coal mine industry, based on the technical mechanism of 'human-cyber-physical system'(HCPS) of new generation intelligent manufacturing , the theoretical system and technical path of the new generation intelligent coal mine with human as the center are proposed. By introducing the evolution of modern coal mine mechanization, informatization and intelligent production mode and the technical development of human, cyber system and physical system, as well as the key scene technologies faced by the development process of the new generation intelligent coal mine, the HCPS technical system of the new generation intelligent coal mine is constructed. The interaction mechanism of human, coal mine cyber system and coal machine equipment physical system is expounded. The HCPS technology elements are given from the four dimensions of human, coal mine cyber system, coal machine equipment physical system and their integration. According to the human-centered development concept, the human-machine cooperation technology path under HCPS theoretical system of the new generation intelligent coal mine is proposed. The core technologies such as human-machine autonomous cooperative interaction mode under the goal of coal mine safety production, situation awareness of coal mine safety production based on man in the loop , coal mine system control sharing under human-machine trust and interaction mode, visualization application development of human-machine information interaction of coal mine task scene are emphatically expounded. It is pointed out that training and practice of the multi-disciplinary talent of mining, machinery, cyber, computer and management, and the innovation of coal mine management, safety system, production mode and personnel type of work are the two key points of the development of the new generation intelligent coal mine.

The scraper conveyor is the key transportation equipment in the coal mine. The iron foreign body entering the scraper conveyor will lead to wear and tear, chain breakage, and even cause serious accidents such as production stoppage and personal injury. The existing scraper conveyor foreign bodies identification method has the problems of poor adaptability to underground images and the incapability of distinguishing the types and quantities of foreign bodies. To solve the above problems, a multi-object detection method for iron foreign bodies in scraper conveyor based on improved mask region-convolutional neural network (Mask R-CNN) is proposed. The image enhancement algorithm based on the Laplace operator is used to preprocess the images collected under the environment of low illumination and high dust. The enhanced images are marked to make a data set. The ResNet-50 feature extractor of the Mask R-CNN model is used to obtain the image features of iron foreign bodies. The feature pyramid network is used for feature fusion to ensure both high-level semantic features (such as category, attribute, etc.) and low-level contour features (such as color, contour, texture, etc.), so as to improve the accuracy of small-scale iron foreign body identification. To solve the problem that the anchor point generated by the Mask R-CNN model does not correspond to the size of the iron foreign body to be detected, the Mask R-CNN model is improved. K-means Ⅱ clustering algorithm is used to replace the original anchor point generation scheme. The cluster center point is obtained by traversing the length and width information of the tag box in the data set, so as to achieve the multi-object detection of iron foreign bodies in the scraper conveyor. The experimental results show that the average detection time of the improved Mask R-CNN model is 0.732 s, which is shortened by 0.093 s and 0.002 s compared with Mask R-CNN and YOLOv5 respectively. The average precision is 91.7%, which is 11.4% and 2.9% higher than that of Mask R-CNN and YOLOv5 respectively.

The intelligent working face has abnormal working conditions such as lost support, uneven straightness, and support skew after the automatic following of hydraulic support. Therefore, manual regulation and control are needed. At present, the research lacks the knowledge discovery of manual regulation and control working conditions after the hydraulic support automation in the production process of the intelligent working face. This is not conducive for workers to quickly judge the number of hydraulic support requiring manual regulation and control. In order to solve the above problems, based on the identification of the number of hydraulic support that is not up to the standard after the hydraulic support automation, a manual regulation and control decision model of middle hydraulic support cluster automation in the intelligent working face is put forward. Firstly, the historical data of the working face is analyzed. It is concluded that after the automatic following of the hydraulic support is finished, three characteristic values can be used as important characteristics for judging whether the hydraulic support carries out manual regulation and control after the automatic following of the hydraulic support. The characteristic values include the distance of the automatic following of the hydraulic support, the stroke variation of the pushing oil cylinder before and after the automatic following of the hydraulic support, and the absolute difference between the number of the hydraulic support at the position of the shearer and the number of the judged hydraulic support. According to the above conclusion, the structure of the manual control decision model after the hydraulic support cluster automation is proposed. The data acquisition module is used for providing the original data. The data preprocessing module prepares the original data by outlier processing, filtering, sorting and correlation analysis. The characteristic engineering module calculates and standardizes the above three characteristic values to provide a sample set for the classification model. After the classification model divides the sample set, the ID3 decision tree is used for classification. Finally, the number of hydraulic supports needing normal working conditions and the number of hydraulic supports nedeing manual control are output. The results of the model evaluation show that, compared with the traditional K-nearest neighbor (KNN), support vector machine (SVM), logical regression (LR) classification algorithms, the training set accuracy of the ID3 decision tree based classification model for the working conditions of hydraulic supports in the middle of the intelligent working face is 92.27%. The test set accuracy is 93.75%. The model can better distinguish the manual control hydraulic support number after automation.

When the steel belt auxiliary bolt is used in the coal mine underground heading roadway, if the positioning of the steel belt anchor hole is not accurate, the drill bit is easy to cause equipment damage when hitting the steel belt or anchor net. There are large potential safety hazards. In order to solve the above problems, an intelligent identification and positioning method of steel belt anchor hole in coal mine roadway support based on improved YOLOv5s model is proposed. ① The definition of the anchor hole image is increased by the super-resolution(SR). The high-frequency information of the anchor hole edge in the image is prevented from being lost due to image blurring. ② Because the anchor hole is small and the camera has a certain distance from the anchor hole, it is easy to lose the characteristic information of the small anchor hole in the convolutional neural network. This affects the detection effect of the anchor hole. The coordinate attention mechanism (CA) module is added to the Backbone network of YOLOv5s model. The network layers of the characteristic extraction network in the YOLOv5s network are increased. The coordinate information of the target object is integrated into the convolutional network. The characteristic information of the anchor hole small target can be effectively extracted, and the success rate of anchor hole detection is improved. ③ The YOLOv5s network embedded in the CA module is trained to the anchor hole dataset reconstructed by SR, and the improved YOLOv5s model, namely SR-CA-YOLOv5s model, is obtained. ④ The SR-CA-YOLOv5s model combined with the binocular camera is used to identify and locate the anchor hole in real-time. The experimental results show that compared with the YOLOv5s model, the mean average precision of the SR-CA-YOLOv5s model is 96.8%, which is 3.1% higher than the YOLOv5s model. The SR-CA-YOLOv5s model has better detection capability and avoids missing detection to a certain extent. Although the frames per second (FPS) of the SR-CA-YOLOv5s model is reduced by 18.5 frames/s, its FPS remains at 166.7 frames/s, which does not affect the real-time detection function of the model. The actual test results show that the SR-CA-YOLOv5s model can accurately detect the anchor hole and obtain the three-dimensional coordinate of the anchor hole relative to the camera under different lighting conditions. The coordinate error is within 6 mm, and the FPS meets the real-time requirements.

In recent years, the number of hand, foot, and mouth disease (HFMD) cases in China has continued to grow, and the disease has become a serious public health issue. Xinjiang Uygur Autonomous Region is located in the arid areas of Northwest China, where the epidemiological trend of HFMD is gradually increasing and characterized by geographical heterogeneity. In this study, based on the HFMD case data in all counties and cities in Xinjiang, we employed statistical and GIS spatial analyses, and geographic probe models to characterize the spatiotemporal differentiation of HFMD epidemics in Xinjiang during the period of 2009–2018, and quantitatively analyzed the factors influencing the spatial differentiation of HFMD epidemics. The results showed that HFMD incidence rate in Xinjiang had non-stationary temporal characteristics on the interannual and monthly scales, and the monthly variation characteristics of HFMD epidemic were quite different in southern and northern Xinjiang. The spatial distribution characteristics of HFMD epidemics showed a north–south spatial differentiation pattern with the Tianshan Mountains as the boundary; cold spot and hot spot of HFMD epidemics in Xinjiang have shifted from scattered to concentrated, and the spatial differentiation pattern had gradually stabilized. Moreover, the dominant factors influencing the spatial differentiation of HFMD epidemics in Xinjiang were socioeconomic factors, such as per capita GDP and urbanization rate, while the basic factors affecting its spatial differentiation were natural environmental factors. The spatial differentiation and evolution patterns of HFMD epidemics differed between northern and southern Xinjiang. Specifically, the leading role of socioeconomic factors is more obvious in southern Xinjiang than in northern Xinjiang, while natural environmental factors (e.g., dryness and relative humidity) contribute to the prevalence of HFMD epidemics in northern Xinjiang, and the perturbing effect of these factors was more prominent than other factors. The results of this study can provide a scientific basis for the prevention of HFMD epidemics and early warning of HFMD epidemics in Xinjiang.

Autonomous and accurate positioning of roadheader is the key to realize intelligent and unmanned heading face. The combination of strapdown inertial navigation and odometer is an ideal positioning scheme for roadheader. The positioning error of strapdown inertial navigation accumulates with time and the position and posture perception capability of single odometer is limited. In order to solve the above problems, a roadheader combined positioning method based on strapdown inertial navigation and differential odometer is proposed by introducing differential odometer as auxiliary positioning on the basis of strapdown inertial navigation. The realization of the method consists of three parts: position and posture perception based on strapdown inertial navigation, dead reckoning based on differential odometer, and data fusion based on Kalman filtering. The reference displacement and attitude angle of roadheader in navigation coordinate system are obtained by strapdown inertial navigation. The differential odometer is composed of two odometers installed on the left and right tracks of the roadheader, and the dead reckoning of the roadheader is calculated by the differential odometer. Kalman filter is designed according to the error equation of strapdown inertial navigation and differential odometer. The difference between the position and posture data of roadheader obtained by strapdown inertial navigation and differential odometer is used as the input of Kalman filter. The output value of Kalman filter is used to correct and compensate the strapdown inertial navigation data. The validity judgment of odometer data is integrated into the combined positioning method, so that the influence of track slipping on the positioning precision is avoided. The positioning experiment of roadheader is carried out in the simulated roadway. The results show that the heading angle error measured by the combined positioning method can be controlled within 0.6° and the position error can be controlled within 0.19 m. The method has high positioning precision.

Abstract Background The calcification of the tooth pulp is a pathological condition that occurs in response to various factors. A uncommon haematological condition known as paroxysmal nocturnal haemoglobinuria (PNH) is characterized by bouts of haemolysis, and it requires long-term use of glucocorticoids (GCs). Case presentation A female patient who was diagnosed with PNH and had a history of long-term use of GCs came to our department for root canal therapy (RCT) for teeth 25, 26, and 27. The radiographs showed generalized pulp canal obliteration (PCO) in most of the patients. None of these teeth (25, 26, or 27) were sensitive to percussion, and they did not respond to thermal or electrical sensitivity tests. A diagnose of pulp necrosis was made for these teeth. RCT was carried out with the help of an oral microscope, and then a prosthodontic procedure was created for the teeth. Conclusions Based on the patient’s long history use of GCs and a series of related studies, we conclude that the long-term usage of GCs contributes significantly to the onset of PCO.

The current real-time and integrity of massive data acquisition in fully mechanized working faces are poor. The abnormal data cleaning takes a long time. The data mining delays are large. This leads to low utilization rate of fully mechanized working data and incapability to assist management in issuing decision-making instructions in real-time. In order to solve the above problems, a massive data mining and analysis platform for fully mechanized working faces is designed. The platform consists of a data source layer, a data acquisition and storage layer, a data mining layer, and a front-end application layer. The data source layer is provided with raw data by various hardware devices on the working surface. The data acquisition and storage layer uses the OPC UA gateway to collect real-time monitoring information from underground sensors, and then stores the data in the InfluxDB storage engine through the MQTT protocol and RESTful interface. The data mining layer uses the Hive data engine and Yarn resource manager to filter out abnormal data caused by workplace interference during the data acquisition process. It solves the problem of local data acquisition order disorder caused by network latency. The Spark distributed mining engine is used to explore the potential value of massive working condition data in the working face device group, improving the running speed of the data mining model. The front-end application layer utilizes visual components to associate with the back-end database. It interacts with the back-end data in real-time through AJAX technology to achieve visual display of model mining results and various monitoring data. The test results show that the platform can fully ensure the real-time and integrity of data acquisition. The cleaning efficiency is 5 times better than a standalone MySQL query engine and the mining efficiency is 4 times better than a standalone Python mining engine.

For grid-connected agricultural distribution networks with a high proportion of wind power， it is pivotal to study how to optimally dispatch various types of equipment to cope with uncertain risks and smooth out random fluctuations based on meeting the demand of agricultural electricity load and irrigation water load. By constructing an expectation index of power deficit for evaluating wind power uncertainty， the paper proposes an optimal risk-averse dispatching method for power-water coupled agricultural distribution networks based on pumped storage to improve the efficiency of electric energy distribution and use in rural areas and realize the safe and stable operation of agricultural distribution networks. Simulations of the optimization model of the water-power coupled agricultural distribution networks with complex equation constraints and inequality constraints are carried out using the evolutionary predator and prey strategy （EPPS）， and the results show that the proposed method can effectively avoid uncertain risks， improve the utilization efficiency of wind power， and smooth the net load curve of the system.

Psoriasis is an autoimmune disease, which has a significant impact on the quality of patient’s life. And, there is still no cure for psoriasis. The human dental pulp stem cell (hDPSC) possesses the properties of immunoregulation. In this study, we aimed to determine the effect of hDPSC on the imiquimod (IMQ)-induced psoriasis in mice. The psoriasis model was established by topical application of IMQ cream in mice for 7 days. We found that subcutaneous injection of hDPSC could reduce the symptoms of skin lesions in IMQ-induced psoriasis and suppress the expression of keratin 16, S100A8, S100A9, which are associated with abnormal epidermal proliferation. Subepithelial inflammatory cytokines, CD4+ T lymphocytes and CD11c+ dendritic cells infiltrations were significantly inhibited in by hDPSC. The TNF-α, IFN-γ expressions in serum were decreased, and splenomegaly induced by IMQ was improved after hDPSC treatment. In summary, our study demonstrated that hDPSC could reduce the symptoms of skin lesions and suppress local and systemic immune responses of IMQ-induced psoriasis in mice, which might provide a new sight for the treatment of psoriasis.

Based on the geological structure of Yima coalfield and working face 21221of Qianqiu Mine as the engineering background, this paper designs a similar simulation experiment of fault slip instability under the influence of mining, and explores the characteristics of the influence of coal slicing mining disturbance on fault slip instability law.The results show that because the upper layer of the coal seam has caused a large degree of loosening and separation of the roof, when the lower layer of the coal is mined, the original loose roof rock further collapses, so the lower layer of the coal is mined Disturbance triggers a larger collapse range of the overlying rock mass, which makes the displacement changes around the fault become more intense, and the phenomenon of upper and lower disk displacement is more obvious.The strain energy peak accumulated in the coal rock mass is larger; at the same time, the fault slip is relatively lagging behind the violent release of strain energy in the rock mass of the coal, so the"sudden drop"phenomenon of the peak strain energy can be used as a precursor to judge fault slip and instability; the violent release of energy in the lower layer mining is earlier than the upper layer mining, and it is easier to induce the occurrence of rock-burst disasters.

Yima coal field has an extra-thick hard rock strata, which is the main cause of many dynamic disasters in the mining area. Based on the mining face 21221 of Qianqiu mine in Yima coal field, Henan Province, China, this paper studies the movement characteristics of extra-thick hard roof during the advancement of working face by similar simulation and numerical analysis methods, carries out laboratory test on bursting liability of roof strata, analyzes the dynamic evolution law of collapse vertical displacement of extra-thick hard roof, and obtains the relationship between mining-induced stress and movement of the roof. The results show that: the failure of extra-thick hard roof could be divided into three stages: quiet period, stable failure period and collapse period. The vertical displacement of roof collapse changes a little during the quiet period, and is basically concentrated in the goaf during the stable failure period, showing a trapezoidal decreasing trend upward. However, it will develop rapidly upward during the collapse period. The movement law of extra-thick hard roof presents an unstable dynamic change phenomenon, that is separation, instantaneous subsidence, separation closure, intermittent stability, instantaneous collapse and compaction. With the advancement of the working face, the deformation and movement of extra-thick hard strata lead to the continuous and unstable subsidence pressure exerting on the stope. The structural instability of the extra-thick hard roof leads to severe mutation of ground pressure on the stope and sudden decrease of mining-induced stress, and that is an important inducement for the occurrence of rock burst.

The support process of hydraulic support is a dynamic change process in time and space regions. At present, the evaluation of support quality of hydraulic support mostly focuses on the static characteristics of support. There is little research on the dynamic change of support column pressure. In order to solve the above problems, a dynamic evaluation model of support quality of hydraulic support in the space-time region based on improved LeNet-5 network is established by using the deep learning method. Firstly, the column pressure data of hydraulic support in working face is preprocessed (missing value filling, abnormal value processing, screening, sorting, etc.) to obtain more complete pressure data hydraulic support. Secondly, the preprocessed column pressure data of the hydraulic support is arranged according to time and space. The important characteristics are extracted such as initial setting force, circulating end-resistance, time-weighted resistance, and spatial distribution condition of resistance, which reflect the support condition of the hydraulic support in intelligent mining working face. The pressure time-sequence and the pressure space-sequence are combined into the total space-time pressure matrix in 2D space-time region. Thirdly, according to the support requirements of the working face, the support quality of the space-time region into seven types, namely, support quality preliminary deterioration, support quality continuous deterioration, support quality deep deterioration, support quality general maintenance, support quality preliminary optimization, support quality continuous optimization and support quality good maintenance. On the total space-time pressure matrix, the sliding window is used to intercept the sub-matrix with the given size at a certain interval. The sub-matrix is one-to-one corresponding to the seven support quality types in space-time regions to form samples and labels. Finally, the samples and the labels are input into the improved LeNet-5 network for training. The dynamic evaluation model of support quality of hydraulic support in space-time region is constructed, which evalues the support condition of hydraulic support in the region in real-time. The experimental results show that the model based on improved LeNet-5 network can be used to identify the dynamic effect of support quality in the working face, and provide the basis for the field operators to adjust the support state of the hydraulic support pertinently. The classification accuracy is 85.25%, which is 12% higher than that of the model based on LeNet-5 network. At the same time, the improved LeNet-5 network can converge to the optimal solution quickly in the training process, which accelerates the training speed of the network. The result verifies the advantages of the improved LeNet-5 network in evaluation of the support quality of hydraulic support in space-time region of intelligent working face.

The damage degree of the surrounding rock in the coal mine roadway is closely related to the support form, thus to carry out research on the ultimate bearing capacity of U-shaped steel support in coal bursting mine is of important guiding significance for the prevention and control of coal burst.Taking a typical coal bursting mine in Yima Coalfield as the engineering background, a theoretical calculation mechanical model of U-shaped steel deformation under uniform loading is established and the analytical solution of the overall deformation is given, then deformation of U-shaped steel and roadway under the action of uniform and non-uniform load is compared, and the affordable ultimate load from overlying strata and applicable ultimate depth of U-shaped steel support is studied when the load increases.The change of approach amount and speed of the roadway roof and floor through on-site monitoring is obtained, and improving the bearing capacity of U-shaped steel support to control the deformation of roadway and coal burst is analyzed.The results show that: with the increase of load, there will be a sharp acceleration point in the deformation and strain energy of roadway, which can be used to determine the applicable ultimate depth of U-shaped steel support.The applicable ultimate depth of U-shaped steel under uniform loading is 1 600 m, and the value is 1 600 m under the non-uniform loading.When the ultimate depth is reached, U-shaped steel will be completely in failure state, and the deformation of roadway will increase rapidly.When designing U-shaped steel support for the roadway of coal bursting mine, it is very important to improve the contact condition of surrounding rock support and apply uniform load to the U-shaped steel support boundary to ensure good contact between surrounding rock and U-shaped steel, and improve the bearing capacity of U-shaped steel.U-shaped steel support with greater bearing capacity can absorb more energy released from surrounding rocks, and better control the approach amount between the roof and floor of the roadway, preventing the deformation of the roadway from increasing rapidly, thus the stability of the roadway is ensured and the coal burst in the roadway is effectively controlled.

During the mining process of coal mines, coal pillars are often formed in the goaf on three sides, and the stress behavior of coal seams has a significant impact on the safety of coal mines. The upper part of panel 3104 of Xinwang Coal Mine is the room-pillar goaf of the 2# coal seam, and the lower part is the goaf of the 7# coal seam. The same coal seam is adjacent to the goaf of panel 3102. Therefore, there are hidden dangers for safe mining in panel 3104 affected by the goaf on three sides. Through FLAC3D numerical simulation, on-site Pasat-M detection, microseismic monitoring, and support resistance measurement, the stress distribution law of surrounding rock in the working face is analyzed. The following conclusions are obtained. There are several high wave velocity stress concentration areas in the coal body of 3104 working face. During the mining of the working face, the mining influence 90m in front of the working face began to spread to the area of the left coal pillar, and the vertical stress at the left coal pillar began to increase. Stress concentration appears in 0–20m strata at the top of 3# coal seams. At the top of a 3# coal seam, the vertical stress concentration coefficient can reach 2 in the range of 4–12m. The influence range of 3104 working face mining advance can reach 200m. Under the influence of mining, a large number of microseismic events occurred at 160m above and 150m below 3# coal seam. This law has reference value for the mining strata behaviors of coal seams under similar conditions.

The outbreak of COVID-19 has led to a sharp increase in the demand for disposable medical protective clothing in the short term. In order to shorten the marketing cycle, a large number of domestic disposable medical protective clothing products have been sterilized by electron beam irradiation, which is more efficient than ethylene oxide sterilization. However, the performance of such clothing must adhere to strict requirements and the process parameters of this sterilization method still lack systematic data support. In order to ensure the reliability of electron beam sterilization of disposable medical protective clothing, research on a corresponding process was carried out. Typical disposable medical protective clothing available on the market made of polypropylene (PP) and coated with polyethylene (PE) was selected as the material studied. An appropriate method was selected to establish the corresponding sterilization dose with reference to the standard methods—“Disposable medical protective clothing irradiation sterilization emergency specification (temporary)” and “ISO 11137-2:2013 Sterilization of health care products—Radiation—Part 2: Establishing the sterilization dose.” The change in material properties after irradiation sterilization with different absorbed doses was studied. Based on the obtained parameters, an algorithm for the average absorbed dose on irradiation by an irradiation electron linac was proposed. Results showed that absorbed doses of 20.3 kGy and 31.5 kGy allowed the products to achieve sterility assurance levels of 10-3 and 10-6, respectively. The material performance of the products after irradiation at 25.0 kGy, 30.0 kGy and 35.0 kGy were able to meet national standard requirements such as elongation at break, breaking strength, impermeability, and filtration efficiency. Thus, a satisfactory electron beam irradiation sterilization process for medical disposable protective clothing has been established.

Subwavelength structures such as subwavelength gratings (SWGs) and subwavelength metamaterials are capable of tailoring the optical properties of materials and controlling the flow of light at the nanoscale. The effective indices of the subwavelength structured strip and slab waveguides can be changed in a wide range by choosing an appropriate duty cycle or a filling factor of silicon, which provides an effective method to manipulate the optical field and achieve effective index matching for functional devices. Recent advances in nanofabrication techniques have made it possible to implement subwavelength structures in silicon strip and slab waveguides. Here we review various approaches used to design subwavelength structures and achieve exotic optical responses and discuss how these structures can be used to realize high-performance silicon photonic devices. Both one-dimensional SWG devices and two-dimensional subwavelength metamaterial devices are covered in this review, including subwavelength structure–based polarization handling devices, mode manipulation devices, and building blocks for integrated optical interconnects. Perspectives on subwavelength structured silicon photonic devices are also discussed.