A long enough period of observation of the Sun’s gravitational dragging effects by using a modified Cavendish’s balance output of experimental evidence shows new patterns. Those patterns can be explained assuming th...A long enough period of observation of the Sun’s gravitational dragging effects by using a modified Cavendish’s balance output of experimental evidence shows new patterns. Those patterns can be explained assuming that the Sun has a torus with rotation, precession, and nutation. This purpose of this paper is to introduce the frequencies of all those movements. The torus’s rotational period can be used to explain the Sun’s magnetic pole reversal. Utilizing a modified Cavendish’s balance showed an output of dragging forces stronger than the attraction between the gravitational masses. This tool afforded this research a new experimental possibility to a more precise determination of the Universal Gravitational Constant Big G. Moreover, the dragging forces directly affect any volume of mass, which includes the atmosphere. This paper shows a correlation between the Sun’s dragging peaks and density of the air squared. The aforementioned correlation and the inverse cubic relation with the distance to the Sun are common for the dragging and tide forces providing the possibility that tidal forces are also a gravitational dragging consequence. The last 2017 total Solar eclipse created a new temporal reaction on the modified Cavendish’s balance. That temporal pattern looks as the spatial pattern created by an opaque disk. This similarity allows the researcher to calculate that the dragging forces are transmitted by photons with spatial periodicity of value λ = 6.1 km.展开更多
Experimental determinations of Newton’s gravitational constant, Big G, have increased, in number and precision, during the last 30 years. There is, however, a persistent discrepancy between various authors. After exa...Experimental determinations of Newton’s gravitational constant, Big G, have increased, in number and precision, during the last 30 years. There is, however, a persistent discrepancy between various authors. After examining some literature proposing that the differences in Big G might be a function of the length of the day along the years, this paper proposes an alternative hypothesis in which the periodicity of said variation is a function of the relative periodicity of the Sun-Earth distance. The hypothesis introduced here becomes a direct application of the Kerr Metric that describes a massive rotating star. The Kerr solution for the equations of the General Theory of Relativity of Albert Einstein fits well with this relative periodicity and adequately predicts the arrangement of the ex-perimental G values reported by sixteen different laboratories. Also, the author explains how the Sun disturbs gravity on the surface of the Earth.展开更多
为分析安徽省土地利用时空演化特征,以1980年、1995年、2000年、2005年、2010年、2015年、2020年土地利用现状图为基础,利用Sankey图、土地利用动态度等方法分析了安徽省近40年土地利用演变特征;结合Getis-Ord General G聚类方法和多距...为分析安徽省土地利用时空演化特征,以1980年、1995年、2000年、2005年、2010年、2015年、2020年土地利用现状图为基础,利用Sankey图、土地利用动态度等方法分析了安徽省近40年土地利用演变特征;结合Getis-Ord General G聚类方法和多距离空间聚类(Ripleys K函数)方法对安徽省土地利用变化累积量进行了时空模式分析;并基于地理探测器模型分析了多种驱动因子对土地利用变化的单一与交互解释程度。结果表明:①1980—2020年安徽省建设用地、草地、水域面积分别增加35.04%、2.44%和0.75%,耕地、林地分别减少4.63%和0.98%;安徽省综合土地利用动态度逐渐增加,建设用地和耕地变化较快,单一动态度最高分别达到3.15%和-0.39%;林地、草地、水域变化较为稳定。②1980—2020年安徽省土地利用的剧烈变化具有显著的聚集性,通过K-means聚类可将不同程度的变化较好地分类;土地利用剧烈变化区域聚集特征受观测尺度变化的影响小于平缓变化区域。③地理探测结果表明:与人类活动强度密切联系的社会因子(夜间灯光数据、GDP、人口、到城市和主要道路距离等)和地形因子(高程、坡度)以及各因子间交互作用是土地利用变化的重要推动力。展开更多
This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation approaches.Channel estimation typically involves the insertion of pilot symbols w...This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation approaches.Channel estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable layout.The model,called Stacked Generalization for Channel Estimation(SGCE),aims to enhance channel estimation performance by eliminating pilot insertion and improving throughput.The SGCE model incorporates six machine learning methods:random forest(RF),gradient boosting machine(GB),light gradient boosting machine(LGBM),support vector regression(SVR),extremely randomized tree(ERT),and extreme gradient boosting(XGB).By generating meta-data from five models(RF,GB,LGBM,SVR,and ERT),we ensure accurate channel coefficient predictions using the XGB model.To validate themodeling performance,we employ the leave-one-out cross-validation(LOOCV)approach,where each observation serves as the validation set while the remaining observations act as the training set.SGCE performances’results demonstrate higher mean andmedian accuracy compared to the separatedmodel.SGCE achieves an average accuracy of 98.4%,precision of 98.1%,and the highest F1-score of 98.5%,accurately predicting channel coefficients.Furthermore,our proposedmethod outperforms prior traditional and intelligent techniques in terms of throughput and bit error rate.SGCE’s superior performance highlights its efficacy in optimizing channel estimation.It can effectively predict channel coefficients and contribute to enhancing the overall efficiency of radio mobile systems.Through extensive experimentation and evaluation,we demonstrate that SGCE improved performance in channel estimation,surpassing previous techniques.Accordingly,SGCE’s capabilities have significant implications for optimizing channel estimation in modern communication systems.展开更多
It has been an exciting journey since the mobile communications and artificial intelligence(AI)were conceived in 1983 and 1956.While both fields evolved independently and profoundly changed communications and computin...It has been an exciting journey since the mobile communications and artificial intelligence(AI)were conceived in 1983 and 1956.While both fields evolved independently and profoundly changed communications and computing industries,the rapid convergence of 5th generation mobile communication technology(5G)and AI is beginning to significantly transform the core communication infrastructure,network management,and vertical applications.The paper first outlined the individual roadmaps of mobile communications and AI in the early stage,with a concentration to review the era from 3rd generation mobile communication technology(3G)to 5G when AI and mobile communications started to converge.With regard to telecommunications AI,the progress of AI in the ecosystem of mobile communications was further introduced in detail,including network infrastructure,network operation and management,business operation and management,intelligent applications towards business supporting system(BSS)&operation supporting system(OSS)convergence,verticals and private networks,etc.Then the classifications of AI in telecommunication ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization organizations.Towards the next decade,the prospective roadmap of telecommunications AI was forecasted.In line with 3rd generation partnership project(3GPP)and International Telecommunication Union Radiocommunication Sector(ITU-R)timeline of 5G&6th generation mobile communication technology(6G),the network intelligence following 3GPP and open radio access network(O-RAN)routes,experience and intent-based network management and operation,network AI signaling system,intelligent middle-office based BSS,intelligent customer experience management and policy control driven by BSS&OSS convergence,evolution from service level agreement(SLA)to experience level agreement(ELA),and intelligent private network for verticals were further explored.The paper is concluded with the vision that AI will reshape the future beyond 5G(B5G)/6G landscape,and we need pivot our research and development(R&D),standardizations,and ecosystem to fully take the unprecedented opportunities.展开更多
文摘A long enough period of observation of the Sun’s gravitational dragging effects by using a modified Cavendish’s balance output of experimental evidence shows new patterns. Those patterns can be explained assuming that the Sun has a torus with rotation, precession, and nutation. This purpose of this paper is to introduce the frequencies of all those movements. The torus’s rotational period can be used to explain the Sun’s magnetic pole reversal. Utilizing a modified Cavendish’s balance showed an output of dragging forces stronger than the attraction between the gravitational masses. This tool afforded this research a new experimental possibility to a more precise determination of the Universal Gravitational Constant Big G. Moreover, the dragging forces directly affect any volume of mass, which includes the atmosphere. This paper shows a correlation between the Sun’s dragging peaks and density of the air squared. The aforementioned correlation and the inverse cubic relation with the distance to the Sun are common for the dragging and tide forces providing the possibility that tidal forces are also a gravitational dragging consequence. The last 2017 total Solar eclipse created a new temporal reaction on the modified Cavendish’s balance. That temporal pattern looks as the spatial pattern created by an opaque disk. This similarity allows the researcher to calculate that the dragging forces are transmitted by photons with spatial periodicity of value λ = 6.1 km.
文摘Experimental determinations of Newton’s gravitational constant, Big G, have increased, in number and precision, during the last 30 years. There is, however, a persistent discrepancy between various authors. After examining some literature proposing that the differences in Big G might be a function of the length of the day along the years, this paper proposes an alternative hypothesis in which the periodicity of said variation is a function of the relative periodicity of the Sun-Earth distance. The hypothesis introduced here becomes a direct application of the Kerr Metric that describes a massive rotating star. The Kerr solution for the equations of the General Theory of Relativity of Albert Einstein fits well with this relative periodicity and adequately predicts the arrangement of the ex-perimental G values reported by sixteen different laboratories. Also, the author explains how the Sun disturbs gravity on the surface of the Earth.
文摘为分析安徽省土地利用时空演化特征,以1980年、1995年、2000年、2005年、2010年、2015年、2020年土地利用现状图为基础,利用Sankey图、土地利用动态度等方法分析了安徽省近40年土地利用演变特征;结合Getis-Ord General G聚类方法和多距离空间聚类(Ripleys K函数)方法对安徽省土地利用变化累积量进行了时空模式分析;并基于地理探测器模型分析了多种驱动因子对土地利用变化的单一与交互解释程度。结果表明:①1980—2020年安徽省建设用地、草地、水域面积分别增加35.04%、2.44%和0.75%,耕地、林地分别减少4.63%和0.98%;安徽省综合土地利用动态度逐渐增加,建设用地和耕地变化较快,单一动态度最高分别达到3.15%和-0.39%;林地、草地、水域变化较为稳定。②1980—2020年安徽省土地利用的剧烈变化具有显著的聚集性,通过K-means聚类可将不同程度的变化较好地分类;土地利用剧烈变化区域聚集特征受观测尺度变化的影响小于平缓变化区域。③地理探测结果表明:与人类活动强度密切联系的社会因子(夜间灯光数据、GDP、人口、到城市和主要道路距离等)和地形因子(高程、坡度)以及各因子间交互作用是土地利用变化的重要推动力。
基金This research project was funded by the Deanship of Scientific Research,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,grant No(43-PRFA-P-58).
文摘This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation approaches.Channel estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable layout.The model,called Stacked Generalization for Channel Estimation(SGCE),aims to enhance channel estimation performance by eliminating pilot insertion and improving throughput.The SGCE model incorporates six machine learning methods:random forest(RF),gradient boosting machine(GB),light gradient boosting machine(LGBM),support vector regression(SVR),extremely randomized tree(ERT),and extreme gradient boosting(XGB).By generating meta-data from five models(RF,GB,LGBM,SVR,and ERT),we ensure accurate channel coefficient predictions using the XGB model.To validate themodeling performance,we employ the leave-one-out cross-validation(LOOCV)approach,where each observation serves as the validation set while the remaining observations act as the training set.SGCE performances’results demonstrate higher mean andmedian accuracy compared to the separatedmodel.SGCE achieves an average accuracy of 98.4%,precision of 98.1%,and the highest F1-score of 98.5%,accurately predicting channel coefficients.Furthermore,our proposedmethod outperforms prior traditional and intelligent techniques in terms of throughput and bit error rate.SGCE’s superior performance highlights its efficacy in optimizing channel estimation.It can effectively predict channel coefficients and contribute to enhancing the overall efficiency of radio mobile systems.Through extensive experimentation and evaluation,we demonstrate that SGCE improved performance in channel estimation,surpassing previous techniques.Accordingly,SGCE’s capabilities have significant implications for optimizing channel estimation in modern communication systems.
文摘It has been an exciting journey since the mobile communications and artificial intelligence(AI)were conceived in 1983 and 1956.While both fields evolved independently and profoundly changed communications and computing industries,the rapid convergence of 5th generation mobile communication technology(5G)and AI is beginning to significantly transform the core communication infrastructure,network management,and vertical applications.The paper first outlined the individual roadmaps of mobile communications and AI in the early stage,with a concentration to review the era from 3rd generation mobile communication technology(3G)to 5G when AI and mobile communications started to converge.With regard to telecommunications AI,the progress of AI in the ecosystem of mobile communications was further introduced in detail,including network infrastructure,network operation and management,business operation and management,intelligent applications towards business supporting system(BSS)&operation supporting system(OSS)convergence,verticals and private networks,etc.Then the classifications of AI in telecommunication ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization organizations.Towards the next decade,the prospective roadmap of telecommunications AI was forecasted.In line with 3rd generation partnership project(3GPP)and International Telecommunication Union Radiocommunication Sector(ITU-R)timeline of 5G&6th generation mobile communication technology(6G),the network intelligence following 3GPP and open radio access network(O-RAN)routes,experience and intent-based network management and operation,network AI signaling system,intelligent middle-office based BSS,intelligent customer experience management and policy control driven by BSS&OSS convergence,evolution from service level agreement(SLA)to experience level agreement(ELA),and intelligent private network for verticals were further explored.The paper is concluded with the vision that AI will reshape the future beyond 5G(B5G)/6G landscape,and we need pivot our research and development(R&D),standardizations,and ecosystem to fully take the unprecedented opportunities.
