The basic theory and effect of the new farming method of ＂Fenlong＂ cultivation which has been included in the main extension technology of Ministry of Agriculture of the People＇s Republic of China is fully illustra...The basic theory and effect of the new farming method of ＂Fenlong＂ cultivation which has been included in the main extension technology of Ministry of Agriculture of the People＇s Republic of China is fully illustrated for the first time, and it is the fourth set （generation） of farming modes and methods following manpower, animal and mechanical （tractor） farming. It follows the natural law to achieve soil activation, water saving, oxygen increase, warming and desalination through the active use of natural resources like soil, rainfall and solar energy, thereby promoting a new round of natural agricultural production and quality improvement and water con- servation, which has crop yield increase by 10%-30%, quality improvement of 5%, natural precipitation retaining increase by100%. The characteristics and mechanism are the use of spiral drill for one-time completion of the land preparation by drilling vertically to 30-50 cm of soil layer through high speed peeling. After instant high temperature and many fierce impacts, mechanical frictions, it could achieve the multiplication of the number of loose soil, soil physical modification and expansion of the soil nutrients, reservoirs, oxygen, microorganisms （＂Four pools＂）. Fenlong cultivation can give birth to new farming culture and civilization, and it can achieve the physical ＂desalinized＂ transformation and utilization of saline soil. The formation of Fenlong green farming technology system makes it possible to invent the farming tools of ＂serf-propelled Fenlong machinery＂ that has got the patent, and it is the method for farmland （dry land, paddy field） Fenlong cultivation, saline-alkali soil smash-ridging cultivation and for the abundance of grass ecology on degraded grassland. The application of Fenlong ＂4＋1＂ （arable, saline-alkali soil, grasslands, Sponge City＋rivers） green development in China can achieve the ＂double safety＂ of food and living space.展开更多
Difficulties encountered in studying generators of semigroup of binary relations defined by a complete X -semilattice of unions D arise because of the fact that they are not regular as a rule, which makes their invest...Difficulties encountered in studying generators of semigroup of binary relations defined by a complete X -semilattice of unions D arise because of the fact that they are not regular as a rule, which makes their investigation problematic. In this work, for special D, it has been seen that the semigroup , which are defined by semilattice D, can be generated by the set .展开更多
In this paper, we first give the definitions of finitely continuous topological space and FC-subspace generated by some set, and obtain coincidence point theorem, whole intersection theorems and Ky Fan type matching t...In this paper, we first give the definitions of finitely continuous topological space and FC-subspace generated by some set, and obtain coincidence point theorem, whole intersection theorems and Ky Fan type matching theorems, and finally discuss the existence of saddle point as an application of coincidence point theorem.展开更多
The rapid prediction of seepage mass flow in soil is essential for understanding fluid transport in porous media.This study proposes a new method for fast prediction of soil seepage mass flow by combining mesoscopic m...The rapid prediction of seepage mass flow in soil is essential for understanding fluid transport in porous media.This study proposes a new method for fast prediction of soil seepage mass flow by combining mesoscopic modeling with deep learning.Porous media structures were generated using the Quartet Structure Generation Set(QSGS)method,and a mesoscopic-scale seepage calculation model was applied to compute flow rates.These results were then used to train a deep learning model for rapid prediction.The analysis shows that larger average pore diameters lead to higher internal flow velocities and mass flow rates,while pressure drops significantly at the throats of fine pores.The trained model predicts seepage mass flow rates with deviations within±20%,achieving a root mean square error of 0.24261 and an average deviation of-0.02197.Importantly,the method performs well even with limited training data,though image-based deep learning approaches may yield better accuracy when larger datasets are available.展开更多
基金Supported by the National Key Technology R&D Program of China(2014BAD06B05)the Major Project of Science and Technology of Guangxi(2017AA22015)~~
文摘The basic theory and effect of the new farming method of ＂Fenlong＂ cultivation which has been included in the main extension technology of Ministry of Agriculture of the People＇s Republic of China is fully illustrated for the first time, and it is the fourth set （generation） of farming modes and methods following manpower, animal and mechanical （tractor） farming. It follows the natural law to achieve soil activation, water saving, oxygen increase, warming and desalination through the active use of natural resources like soil, rainfall and solar energy, thereby promoting a new round of natural agricultural production and quality improvement and water con- servation, which has crop yield increase by 10%-30%, quality improvement of 5%, natural precipitation retaining increase by100%. The characteristics and mechanism are the use of spiral drill for one-time completion of the land preparation by drilling vertically to 30-50 cm of soil layer through high speed peeling. After instant high temperature and many fierce impacts, mechanical frictions, it could achieve the multiplication of the number of loose soil, soil physical modification and expansion of the soil nutrients, reservoirs, oxygen, microorganisms （＂Four pools＂）. Fenlong cultivation can give birth to new farming culture and civilization, and it can achieve the physical ＂desalinized＂ transformation and utilization of saline soil. The formation of Fenlong green farming technology system makes it possible to invent the farming tools of ＂serf-propelled Fenlong machinery＂ that has got the patent, and it is the method for farmland （dry land, paddy field） Fenlong cultivation, saline-alkali soil smash-ridging cultivation and for the abundance of grass ecology on degraded grassland. The application of Fenlong ＂4＋1＂ （arable, saline-alkali soil, grasslands, Sponge City＋rivers） green development in China can achieve the ＂double safety＂ of food and living space.
文摘Difficulties encountered in studying generators of semigroup of binary relations defined by a complete X -semilattice of unions D arise because of the fact that they are not regular as a rule, which makes their investigation problematic. In this work, for special D, it has been seen that the semigroup , which are defined by semilattice D, can be generated by the set .
文摘In this paper, we first give the definitions of finitely continuous topological space and FC-subspace generated by some set, and obtain coincidence point theorem, whole intersection theorems and Ky Fan type matching theorems, and finally discuss the existence of saddle point as an application of coincidence point theorem.
基金Dynamics of CO_(2) Leakage and Seepage in Wellbores Under Reservoir Stimulation,grant number YJCCUS25SFW0004.
文摘The rapid prediction of seepage mass flow in soil is essential for understanding fluid transport in porous media.This study proposes a new method for fast prediction of soil seepage mass flow by combining mesoscopic modeling with deep learning.Porous media structures were generated using the Quartet Structure Generation Set(QSGS)method,and a mesoscopic-scale seepage calculation model was applied to compute flow rates.These results were then used to train a deep learning model for rapid prediction.The analysis shows that larger average pore diameters lead to higher internal flow velocities and mass flow rates,while pressure drops significantly at the throats of fine pores.The trained model predicts seepage mass flow rates with deviations within±20%,achieving a root mean square error of 0.24261 and an average deviation of-0.02197.Importantly,the method performs well even with limited training data,though image-based deep learning approaches may yield better accuracy when larger datasets are available.
