Local residents in Xiahe County, Gansu Province, clear up debris left by floodwaters on August 20. People’s Daily Online reported that rains triggered mudrock flows that hit seven villages in three towns on August 19...Local residents in Xiahe County, Gansu Province, clear up debris left by floodwaters on August 20. People’s Daily Online reported that rains triggered mudrock flows that hit seven villages in three towns on August 19. About 3,000 rooms in the county’s houses are reported to have collapsed and more than 11,400 were flooded. Three people were killed and one was missing. A 30-meter section of highway linking Xiahe and other regions was blocked by falling rocks and mud. The report said the county government had mobilized staff for disaster-relief work.展开更多
Ⅰ. The Development Plan for Auto-electronic technology in China 1. The Status Quo of China’s Auto-electronic Technology Application There is a clear disparity between China and developed countries in the field of au...Ⅰ. The Development Plan for Auto-electronic technology in China 1. The Status Quo of China’s Auto-electronic Technology Application There is a clear disparity between China and developed countries in the field of auto-electronic technology. The comprehensive level of China today’s auto-electronic展开更多
Conical picks are the key cutting components used on roadheaders,and they are replaced frequently because of the bad working conditions.Picks did not meet the fatigue life when they were damaged by abrasion,so the pic...Conical picks are the key cutting components used on roadheaders,and they are replaced frequently because of the bad working conditions.Picks did not meet the fatigue life when they were damaged by abrasion,so the pick fatigue life and strength are excessive.In the paper,in order to reduce the abrasion and save the materials,structure optimization was carried out.For static analysis and fatigue life prediction,the simulation program was proposed based on mathematical models to obtain the cutting resistance.Furthermore,the finite element models for static analysis and fatigue life analysis were proposed.The results indicated that fatigue life damage and strength failure of the cutting pick would never happen.Subsequently,the initial optimization model and the finite element model of picks were developed.According to the optimized results,a new type of pick was developed based on the working and installing conditions of the traditional pick.Finally,the previous analysis methods used for traditional methods were carried out again for the new type picks.The results show that new type of pick can satisfy the strength and fatigue life requirements.展开更多
Real-time identification of rock strength and cuttability based on monitoring while cutting during excavation is essential for key procedures such as the precise adjustment of excavation parameters and the in-situ mod...Real-time identification of rock strength and cuttability based on monitoring while cutting during excavation is essential for key procedures such as the precise adjustment of excavation parameters and the in-situ modification of hard rocks.This study proposes an in-telligent approach for predicting rock strength and cuttability.A database comprising 132 data sets is established,containing cutting para-meters(such as cutting depth and pick angle),cutting responses(such as specific energy and instantaneous cutting rate),and rock mech-anical parameters collected from conical pick-cutting experiments.These parameters serve as input features for predicting the uniaxial compressive strength and tensile strength of rocks using regression fitting and machine learning methodologies.In addition,rock cuttabil-ity is classified using a combination of the analytic hierarchy process and fuzzy comprehensive evaluation method,and subsequently iden-tified through machine learning approaches.Various models are compared to determine the optimal predictive and classification models.The results indicate that the optimal model for uniaxial compressive strength and tensile strength prediction is the genetic algorithm-optimized backpropagation neural network model,and the optimal model for rock cuttability classification is the radial basis neural network model.展开更多
Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planti...Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planting system(HDPS)offers a viable method to enhance productivity by increasing plant populations per unit area,optimizing resource utilization,and facilitating machine picking.Cotton is an indeterminate plant that produce excessive vegeta-tive growth in favorable soil fertility and moisture conditions,which posing challenges for efficient machine picking.To address this issue,the application of plant growth retardants(PGRs)is essential for controlling canopy architecture.PGRs reduce internode elongation,promote regulated branching,and increase plant compactness,making cotton plants better suited for machine picking.PGRs application also optimizes photosynthates distribution between veg-etative and reproductive growth,resulting in higher yields and improved fibre quality.The integration of HDPS and PGRs applications results in an optimal plant architecture for improving machine picking efficiency.However,the success of this integration is determined by some factors,including cotton variety,environmental conditions,and geographical variations.These approaches not only address yield stagnation and labour shortages but also help to establish more effective and sustainable cotton farming practices,resulting in higher cotton productivity.展开更多
Water-weakening presents a promising strategy for the in-situ improvement of rock cuttability.This study unveils the influences of water saturation on the mechanical response and fragmentation characteristics of rock ...Water-weakening presents a promising strategy for the in-situ improvement of rock cuttability.This study unveils the influences of water saturation on the mechanical response and fragmentation characteristics of rock samples.A series of rock-cutting tests using conical pick indentation was conducted on three types of sandstone samples under both dry and water-saturated conditions.The relationships between cutting force and indentation depth,as well as typical cuttability indices are determined and compared for dry and water-saturated samples.The experimental results reveal that the presence of water facilitates shearing failure in rock samples,as well as alleviates the fluctuations in the cutting force-indentation depth curve Furthermore,the peak cutting force(F_(p)),cutting work(W_(p)),and specific energy(SE)undergo apparent decrease after water saturation,whereas the trend in the indentation depth at rock failure(D_(f))varies across different rock types.Additionally,the water-induced percentage reductions in F_(p)and SE correlate positively with the quartz and swelling clay content within the rocks,suggesting that the cuttability improvement due to water saturation is attributed to the combined effects of stress corrosion and frictional reduction.These findings carry significant implications for improving rock cuttability in mechanized excavation of hard rock formations.展开更多
With the continuous expansion of deep underground engineering and the growing demand for safety monitoring,microseismic monitoring has become a core method for early warning of rock mass fracture and engineering stabi...With the continuous expansion of deep underground engineering and the growing demand for safety monitoring,microseismic monitoring has become a core method for early warning of rock mass fracture and engineering stability assessment.To address problems in existing methods,such as low data processing efficiency and poor phase recognition accuracy under low signal-to-noise ratio(SNR)conditions in complex geological environments,this study proposes an intelligent phase picking model based on ResUNet.The model integrates the residual learning mechanism of ResNet with the multi-scale feature extraction capability of UNet,effectively mitigating the vanishing gradient problem in deep networks.It also achieves cross-layer fusion of shallow detail features and deep semantic features through skip connections in the encoder-decoder structure.Compared with traditional short-time average/long-time average(STA/LTA)algorithms and advanced neural network models such as PhaseNet and EQTransformer,ResUNet shows superior performance in picking P-and S-wave phases.The model was trained on 400000 labeled microseismic signals from the Stanford earthquake dataset(STEAD)and was successfully applied to the Shizhuyuan polymetallic mine in Hunan Province,China.The results demonstrate that ResUNet achieves high picking accuracy and robustness in complex geological conditions,offering reliable technical support for early warning of disasters such as rockburst in deep underground engineering.展开更多
The availability of a tremendous amount of seismic data demands seismological researchers to analyze seismic phases efficiently.Recently,deep learning algorithms exhibit a powerful capability of detecting and picking ...The availability of a tremendous amount of seismic data demands seismological researchers to analyze seismic phases efficiently.Recently,deep learning algorithms exhibit a powerful capability of detecting and picking on P-and S-wave phases.However,it remains a challenge to effeciently process enormous teleseismic phases,which are crucial to probe Earth’s interior structures and their dynamics.In this study,we propose a scheme to detect and pick teleseismic phases,such as seismic phase that reflects off the core-mantle boundary(i.e.,PcP)and that reflects off the inner-core boundary(i.e.,PKiKP),from a seismic dataset in Japan.The scheme consists of three steps:1)latent phase traces are truncated from the whole seismogram with theoretical arrival times;2)latent phases are recognized and evaluated by convolutional neural network(CNN)models;3)arrivals of good or fair phase are picked with another CNN models.The testing detection result on 7386 seismograms shows that the scheme recognizes 92.15%and 94.13%of PcP and PKiKP phases.The testing picking result has a mean absolute error of 0.0742 s and 0.0636 s for the PcP and PKiKP phases,respectively.These seismograms were processed in just 5 min for phase detection and picking,demonstrating the efficiency of the proposed scheme in automatic teleseismic phase analysis.展开更多
PICK1蛋白(protein interacting with C alpha kinase 1)是一种同时具有PDZ和BAR区域的支架蛋白,在哺乳动物体内与多种蛋白质相互作用,并被证明在多种生理过程中发挥重要的调节作用,同时参与了多种疾病病理过程。因此,PICK1蛋白可能成...PICK1蛋白(protein interacting with C alpha kinase 1)是一种同时具有PDZ和BAR区域的支架蛋白,在哺乳动物体内与多种蛋白质相互作用,并被证明在多种生理过程中发挥重要的调节作用,同时参与了多种疾病病理过程。因此,PICK1蛋白可能成为极具前景的疾病治疗靶点。该文通过对近年来国内外发表的相关文献进行整理与分析,综述了PICK1蛋白的生理功能与其作为药物靶点的研究新进展,旨在为PICK1蛋白的深入研究提供理论支持。展开更多
文摘Local residents in Xiahe County, Gansu Province, clear up debris left by floodwaters on August 20. People’s Daily Online reported that rains triggered mudrock flows that hit seven villages in three towns on August 19. About 3,000 rooms in the county’s houses are reported to have collapsed and more than 11,400 were flooded. Three people were killed and one was missing. A 30-meter section of highway linking Xiahe and other regions was blocked by falling rocks and mud. The report said the county government had mobilized staff for disaster-relief work.
文摘Ⅰ. The Development Plan for Auto-electronic technology in China 1. The Status Quo of China’s Auto-electronic Technology Application There is a clear disparity between China and developed countries in the field of auto-electronic technology. The comprehensive level of China today’s auto-electronic
基金National Natural Science Foundation of China(Grant No.51674155)China Postdoctoral Science Foundation Funded Project(Project No.2016M592214)Natural Science Foundation of Shandong Province(Grant No.ZR2014EEM021).
文摘Conical picks are the key cutting components used on roadheaders,and they are replaced frequently because of the bad working conditions.Picks did not meet the fatigue life when they were damaged by abrasion,so the pick fatigue life and strength are excessive.In the paper,in order to reduce the abrasion and save the materials,structure optimization was carried out.For static analysis and fatigue life prediction,the simulation program was proposed based on mathematical models to obtain the cutting resistance.Furthermore,the finite element models for static analysis and fatigue life analysis were proposed.The results indicated that fatigue life damage and strength failure of the cutting pick would never happen.Subsequently,the initial optimization model and the finite element model of picks were developed.According to the optimized results,a new type of pick was developed based on the working and installing conditions of the traditional pick.Finally,the previous analysis methods used for traditional methods were carried out again for the new type picks.The results show that new type of pick can satisfy the strength and fatigue life requirements.
基金supported by the National Natural Science Foundation of China(Nos.52174099 and 52474168)the Science and Technology Innovation Program of Hunan Province,China(No.2023RC3050)+1 种基金the Natural Science Foundation of Hunan,China(No.2024JJ4064)the Open Fund of the State Key Laboratory of Safety Technology of Metal Mines(No.kfkt2023-01).
文摘Real-time identification of rock strength and cuttability based on monitoring while cutting during excavation is essential for key procedures such as the precise adjustment of excavation parameters and the in-situ modification of hard rocks.This study proposes an in-telligent approach for predicting rock strength and cuttability.A database comprising 132 data sets is established,containing cutting para-meters(such as cutting depth and pick angle),cutting responses(such as specific energy and instantaneous cutting rate),and rock mech-anical parameters collected from conical pick-cutting experiments.These parameters serve as input features for predicting the uniaxial compressive strength and tensile strength of rocks using regression fitting and machine learning methodologies.In addition,rock cuttabil-ity is classified using a combination of the analytic hierarchy process and fuzzy comprehensive evaluation method,and subsequently iden-tified through machine learning approaches.Various models are compared to determine the optimal predictive and classification models.The results indicate that the optimal model for uniaxial compressive strength and tensile strength prediction is the genetic algorithm-optimized backpropagation neural network model,and the optimal model for rock cuttability classification is the radial basis neural network model.
文摘Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planting system(HDPS)offers a viable method to enhance productivity by increasing plant populations per unit area,optimizing resource utilization,and facilitating machine picking.Cotton is an indeterminate plant that produce excessive vegeta-tive growth in favorable soil fertility and moisture conditions,which posing challenges for efficient machine picking.To address this issue,the application of plant growth retardants(PGRs)is essential for controlling canopy architecture.PGRs reduce internode elongation,promote regulated branching,and increase plant compactness,making cotton plants better suited for machine picking.PGRs application also optimizes photosynthates distribution between veg-etative and reproductive growth,resulting in higher yields and improved fibre quality.The integration of HDPS and PGRs applications results in an optimal plant architecture for improving machine picking efficiency.However,the success of this integration is determined by some factors,including cotton variety,environmental conditions,and geographical variations.These approaches not only address yield stagnation and labour shortages but also help to establish more effective and sustainable cotton farming practices,resulting in higher cotton productivity.
基金supported by financial grants from the National Natural Science Foundation of China(Grant Nos.52334003 and 52104111)the National Key R&D Program of China(Grant No.2022YFC2905600)。
文摘Water-weakening presents a promising strategy for the in-situ improvement of rock cuttability.This study unveils the influences of water saturation on the mechanical response and fragmentation characteristics of rock samples.A series of rock-cutting tests using conical pick indentation was conducted on three types of sandstone samples under both dry and water-saturated conditions.The relationships between cutting force and indentation depth,as well as typical cuttability indices are determined and compared for dry and water-saturated samples.The experimental results reveal that the presence of water facilitates shearing failure in rock samples,as well as alleviates the fluctuations in the cutting force-indentation depth curve Furthermore,the peak cutting force(F_(p)),cutting work(W_(p)),and specific energy(SE)undergo apparent decrease after water saturation,whereas the trend in the indentation depth at rock failure(D_(f))varies across different rock types.Additionally,the water-induced percentage reductions in F_(p)and SE correlate positively with the quartz and swelling clay content within the rocks,suggesting that the cuttability improvement due to water saturation is attributed to the combined effects of stress corrosion and frictional reduction.These findings carry significant implications for improving rock cuttability in mechanized excavation of hard rock formations.
基金Project(2022YFC2905100)supported by the National Key Research and Development Program of ChinaProject(52174098)supported by the National Natural Science Foundation of China。
文摘With the continuous expansion of deep underground engineering and the growing demand for safety monitoring,microseismic monitoring has become a core method for early warning of rock mass fracture and engineering stability assessment.To address problems in existing methods,such as low data processing efficiency and poor phase recognition accuracy under low signal-to-noise ratio(SNR)conditions in complex geological environments,this study proposes an intelligent phase picking model based on ResUNet.The model integrates the residual learning mechanism of ResNet with the multi-scale feature extraction capability of UNet,effectively mitigating the vanishing gradient problem in deep networks.It also achieves cross-layer fusion of shallow detail features and deep semantic features through skip connections in the encoder-decoder structure.Compared with traditional short-time average/long-time average(STA/LTA)algorithms and advanced neural network models such as PhaseNet and EQTransformer,ResUNet shows superior performance in picking P-and S-wave phases.The model was trained on 400000 labeled microseismic signals from the Stanford earthquake dataset(STEAD)and was successfully applied to the Shizhuyuan polymetallic mine in Hunan Province,China.The results demonstrate that ResUNet achieves high picking accuracy and robustness in complex geological conditions,offering reliable technical support for early warning of disasters such as rockburst in deep underground engineering.
文摘The availability of a tremendous amount of seismic data demands seismological researchers to analyze seismic phases efficiently.Recently,deep learning algorithms exhibit a powerful capability of detecting and picking on P-and S-wave phases.However,it remains a challenge to effeciently process enormous teleseismic phases,which are crucial to probe Earth’s interior structures and their dynamics.In this study,we propose a scheme to detect and pick teleseismic phases,such as seismic phase that reflects off the core-mantle boundary(i.e.,PcP)and that reflects off the inner-core boundary(i.e.,PKiKP),from a seismic dataset in Japan.The scheme consists of three steps:1)latent phase traces are truncated from the whole seismogram with theoretical arrival times;2)latent phases are recognized and evaluated by convolutional neural network(CNN)models;3)arrivals of good or fair phase are picked with another CNN models.The testing detection result on 7386 seismograms shows that the scheme recognizes 92.15%and 94.13%of PcP and PKiKP phases.The testing picking result has a mean absolute error of 0.0742 s and 0.0636 s for the PcP and PKiKP phases,respectively.These seismograms were processed in just 5 min for phase detection and picking,demonstrating the efficiency of the proposed scheme in automatic teleseismic phase analysis.
文摘PICK1蛋白(protein interacting with C alpha kinase 1)是一种同时具有PDZ和BAR区域的支架蛋白,在哺乳动物体内与多种蛋白质相互作用,并被证明在多种生理过程中发挥重要的调节作用,同时参与了多种疾病病理过程。因此,PICK1蛋白可能成为极具前景的疾病治疗靶点。该文通过对近年来国内外发表的相关文献进行整理与分析,综述了PICK1蛋白的生理功能与其作为药物靶点的研究新进展,旨在为PICK1蛋白的深入研究提供理论支持。
