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
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
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
针对菠萝采摘作业中劳动力需求大以及采摘效率低等问题,对单目视觉目标识别和定位菠萝目标的抱扭式菠萝采摘机进行了研究。该菠萝采摘机由底盘、采摘机构、运动机构、机器视觉机构和运输机构组成;机器视觉系统选用Raspberry pi 3B作为...针对菠萝采摘作业中劳动力需求大以及采摘效率低等问题,对单目视觉目标识别和定位菠萝目标的抱扭式菠萝采摘机进行了研究。该菠萝采摘机由底盘、采摘机构、运动机构、机器视觉机构和运输机构组成;机器视觉系统选用Raspberry pi 3B作为图像处理器,并装载YOLO算法作为菠萝识别的分类器。采摘机构模拟人工采摘时抱紧扭转的动作行为,通过气缸推动V型机械爪进退实现对菠萝的夹持,通过控制舵机转动完成菠萝的扭断,采摘后将果实通过运输机构运送到包装点。研制了菠萝采摘机样机,利用样机进行了实验室模拟采摘实验。实验结果证明,该菠萝采摘机的视觉系统能够快速判断菠萝的位置,采摘机构夹持定位准确,可顺利完成采摘动作。该样机的研制为菠萝采摘提供了可行的技术参考。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
蛋白质是生命功能的执行者.生命体中某些关键蛋白的功能异常往往是导致疾病发生的根本原因.这些疾病相关蛋白极有可能成为药物靶点,为新药研发和疾病治疗提供重要线索.PICK1蛋白(protein interacting with Cαkinase1)结合能力广泛、功...蛋白质是生命功能的执行者.生命体中某些关键蛋白的功能异常往往是导致疾病发生的根本原因.这些疾病相关蛋白极有可能成为药物靶点,为新药研发和疾病治疗提供重要线索.PICK1蛋白(protein interacting with Cαkinase1)结合能力广泛、功能多样以及在多种重要疾病(如:癌症、精神分裂症、疼痛、帕金森综合症等)的发生发展过程中发挥潜在的作用,使其成为一个可能的药靶蛋白.PICK1与绝大多数配体蛋白的相互作用是通过其PDZ结构域与配体C末端区域的结合介导的,使PICK1的PDZ结构域成为一个潜在的药物靶点.因此,可以利用生物小分子物质特异性地结合PICK1的PDZ结构域,干扰或阻断PICK1与配体蛋白的天然相互作用,最终达到治疗相关疾病的目的.展开更多
In view of landscape design problems in the transition from vegetable producing garden to sightseeing and picking garden,definitions of both gardens were introduced and discriminated.It was proposed that landscapes in...In view of landscape design problems in the transition from vegetable producing garden to sightseeing and picking garden,definitions of both gardens were introduced and discriminated.It was proposed that landscapes in the vegetable sightseeing and picking garden included installations,open-field vegetable producing landscapes and overall environment landscapes.Landscape design concepts and principles of vegetable sightseeing and picking garden were analyzed,and it was stressed that its landscape design should take quality production of vegetables and fruits as the principal line,environment landscapes of the garden as the support,and experiencing production process as the feature,by following the principles of "integrity of garden design,characteristic vegetable varieties,proper crop rotation,ecological production process".Landscape contents of this garden were analyzed from 3 perspectives:landscape design within installations,major road,and overall appearance of the garden.Cangshang Vegetable Sightseeing and Picking Garden in Beiwu Township,Shunyi District,Beijing City was taken for an example to analyze its landscape construction inside and outside greenhouses as well as the optimization of the overall environment landscapes on the basis of introducing its landscape design concepts.展开更多
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 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.
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.
文摘针对菠萝采摘作业中劳动力需求大以及采摘效率低等问题,对单目视觉目标识别和定位菠萝目标的抱扭式菠萝采摘机进行了研究。该菠萝采摘机由底盘、采摘机构、运动机构、机器视觉机构和运输机构组成;机器视觉系统选用Raspberry pi 3B作为图像处理器,并装载YOLO算法作为菠萝识别的分类器。采摘机构模拟人工采摘时抱紧扭转的动作行为,通过气缸推动V型机械爪进退实现对菠萝的夹持,通过控制舵机转动完成菠萝的扭断,采摘后将果实通过运输机构运送到包装点。研制了菠萝采摘机样机,利用样机进行了实验室模拟采摘实验。实验结果证明,该菠萝采摘机的视觉系统能够快速判断菠萝的位置,采摘机构夹持定位准确,可顺利完成采摘动作。该样机的研制为菠萝采摘提供了可行的技术参考。
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。
文摘蛋白质是生命功能的执行者.生命体中某些关键蛋白的功能异常往往是导致疾病发生的根本原因.这些疾病相关蛋白极有可能成为药物靶点,为新药研发和疾病治疗提供重要线索.PICK1蛋白(protein interacting with Cαkinase1)结合能力广泛、功能多样以及在多种重要疾病(如:癌症、精神分裂症、疼痛、帕金森综合症等)的发生发展过程中发挥潜在的作用,使其成为一个可能的药靶蛋白.PICK1与绝大多数配体蛋白的相互作用是通过其PDZ结构域与配体C末端区域的结合介导的,使PICK1的PDZ结构域成为一个潜在的药物靶点.因此,可以利用生物小分子物质特异性地结合PICK1的PDZ结构域,干扰或阻断PICK1与配体蛋白的天然相互作用,最终达到治疗相关疾病的目的.
文摘In view of landscape design problems in the transition from vegetable producing garden to sightseeing and picking garden,definitions of both gardens were introduced and discriminated.It was proposed that landscapes in the vegetable sightseeing and picking garden included installations,open-field vegetable producing landscapes and overall environment landscapes.Landscape design concepts and principles of vegetable sightseeing and picking garden were analyzed,and it was stressed that its landscape design should take quality production of vegetables and fruits as the principal line,environment landscapes of the garden as the support,and experiencing production process as the feature,by following the principles of "integrity of garden design,characteristic vegetable varieties,proper crop rotation,ecological production process".Landscape contents of this garden were analyzed from 3 perspectives:landscape design within installations,major road,and overall appearance of the garden.Cangshang Vegetable Sightseeing and Picking Garden in Beiwu Township,Shunyi District,Beijing City was taken for an example to analyze its landscape construction inside and outside greenhouses as well as the optimization of the overall environment landscapes on the basis of introducing its landscape design concepts.
